MIRNA FINGERPRINT IN THE DIAGNOSIS OF LUNG CANCER

Abstract

The present invention provides novel methods for diagnosing diseases based on the determination of specific miRNAs that have altered expression levels in disease states compared to healthy controls.

Description

CROSS REFERENCE TO RELATED APPLICATION

[0001] This application is a Divisional Application of U.S. application Ser. No. 15/671,856, filed Aug. 8, 2017, which is a Divisional Application of U.S. application Ser. No. 13/376,281, filed Jan. 19, 2012, now U.S. Pat. No. 9,758,827, issued Sep. 12, 2017, which is a 35 U.S.C. 371 National Phase Entry Application from PCT/EP2010/057942, filed Jun. 7, 2010, which claims the benefit of U.S. Provisional Applications No. 61/184,452 filed Jun. 5, 2009, 61/213,971 filed Aug. 3, 2009, 61/287,521 filed Dec. 17, 2009 and European Patent Application No. 09015668.8 filed on Dec. 17, 2009, the disclosures of all of the above are incorporated herein in their entirety by reference.

BACKGROUND OF THE INVENTION

[0002] MicroRNAs (miRNA) are a recently discovered class of small non-coding RNAs (17-14 nucleotides). Due to their function as regulators of gene expression they play a critical role both in physiological and in pathological processes, such as cancer (Calin and Croce 2006; Esquela-Kerscher and Slack 2006; Zhang, Pan et al. 2007; Sassen, Miska et al. 2008).

[0003] There is increasing evidence that miRNAs are not only found in tissues but also in human blood both as free circulating nucleic acids (also called circulating miRNAs) and in mononuclear cells. A recent proof-of-principle study demonstrated miRNA expression pattern in pooled blood sera and pooled blood cells, both in healthy individuals and in cancer patients including patients with lung cancer (Chen, Ba et al. 2008). In addition, a remarkable stability of miRNAs in human sera was recently demonstrated (Chen, Ba et al. 2008; Gilad, Meiri et al. 2008). These findings make miRNA a potential tool for diagnostics for various types of diseases based on blood analysis.

[0004] Lung cancer is the leading cause of cancer death worldwide (Jemal, Siegel et al. 2008). Its five-year survival rate is among the lowest of all cancer types and is markedly correlated to the stage at the time of diagnosis (Scott, Howington et al. 2007). Using currently existing techniques, more than two-thirds of lung cancers are diagnosed at late stages, when the relative survival rate is low (Henschke and Yankelevitz 2008). This reality calls for the search of new biomarkers that are able to catch lung cancer while it is still small and locally defined.

[0005] Various markers have been proposed to indicate specific types of disorders and in particular cancer. However, there is still a need for more efficient and effective methods and compositions for the diagnosis of diseases and in particular cancer.

SUMMARY OF THE INVENTION

[0006] The present invention provides novel methods for diagnosing diseases based on the determination of specific miRNAs that have altered expression levels in disease states compared to healthy or other relevant controls. The present invention particularly provides novel methods for the diagnosis and/or prognosis and/or monitoring of lung cancer or related diseases in human individuals based on miRNA analysis from samples derived from blood.

[0007] Subject-matter of the invention is a method for diagnosing lung cancer, comprising the steps

[0008] (a) determining an expression profile of a predetermined set of miRNAs in a biological sample from a patient; and

[0009] (b) comparing said expression profile to a reference expression profile, wherein the comparison of said determined expression profile to said reference expression profile allows for the diagnosis of lung cancer.

[0010] A "biological sample" in terms of the invention means a sample of biological tissue or fluid. Examples of biological samples are sections of tissues, blood, blood fractions, plasma, serum, etc. A biological sample may be provided by removing a sample of cells from a subject, but can also be provided by using a previously isolated sample. For example, a tissue sample can be removed from a subject suspected of having a disease by conventional biopsy techniques. In a preferred embodiment, a blood sample is taken from the subject. In one embodiment, the blood or tissue sample is obtained from the subject prior to initiation of radiotherapy, chemotherapy or other therapeutic treatment. According to the invention, the biological sample preferably is a blood, plasma, PBMC (peripheral blood mononuclear cell) or a serum sample. Further, it is also preferred to use blood cells, e.g. erythrocytes, leukocytes or thrombocytes.

[0011] A biological sample from a patient means a sample from a subject suspected to be affected by a disease. As used herein, the term "subject" refers to any mammal, including both human and other mammals. Preferably, the methods of the present invention are applied to human subjects.

[0012] In step (a) of the method of the invention, an expression profile of a predetermined set of miRNAs is determined. The determination may be carried out by any convenient means for determining nucleic acids. For expression profiling, qualitative, semi-quantitative and preferably quantitative detection methods can be used. A variety of techniques are well known to those of skill in the art. In particular, the determination may comprise nucleic acid hybridization and/or nucleic acid amplification steps.

[0013] Nucleic acid hybridization may for example be performed using a solid phase nucleic acid biochip array, in particular a microarray, beads, or in situ hybridization. The miRNA microarray technology affords the analysis of a complex biological sample for all expressed miRNAs. Nucleotides with complementarity to the corresponding miRNAs are spotted or synthesized on coated carriers. E.g., miRNAs isolated from the sample of interest may be labeled, e.g. fluorescently labeled, so that upon hybridization of the miRNAs to the complementary sequences on the carrier the resulting signal indicates the occurrence of a distinct miRNA. Preferably, microarray methods are employed that do not require a labeling of the miRNAs prior to hybridization (FIG. 3-4) and start directly from total RNA input. On one miRNA microarray, preferably the whole predetermined set of miRNAs can be analyzed. Examples of preferred hybridization assays are shown in FIGS. 1-4. The design of exemplary miRNA capture probes for use in hybridization assays is depicted in FIGS. 5 and 6.

[0014] Further, real-time or quantitative real-time polymerase chain reaction (RT-RCR or qRT-PCR) can be used to detect also low abandoned miRNAs. Furthermore, bead-based assays, e.g. the Luminex platform, are suitable.

[0015] Alternative methods for obtaining expression profiles may also contain sequencing, next generation sequencing or mass spectroscopy.

[0016] The predetermined set of miRNAs in step (a) of the method of the invention depends on the disease to be diagnosed. The inventors found out that single miRNA biomarkers lack sufficient accuracy, specificity and sensitivity, and therefore it is preferred to analyze more complex miRNA expression patterns, so-called miRNA signatures. The predetermined set of miRNAs comprises one or more, preferably a larger number of miRNAs (miRNA signatures) that are differentially regulated in samples of a patient affected by a particular disease compared to healthy or other relevant controls.

[0017] The expression profile determined in step (a) is subsequently compared to a reference expression profile in step (b). The reference expression profile is the expression profile of the same set of miRNAs in a biological sample originating from the same source as the biological sample from a patient but obtained from a healthy subject. Preferably, both the reference expression profile and the expression profile of step (a) are determined in a blood or serum sample including whole blood, plasma, serum or fractions thereof, or in a sample of peripheral blood mononuclear cells, of erythrocytes, leukocytes and/or thrombocytes. It is understood that the reference expression profile is not necessarily obtained from a single healthy subject but may be an average expression profile of a plurality of healthy subjects. It is preferred to use a reference expression profile obtained from a person of the same gender, and a similar age as the patient. It is also understood that the reference expression profile is not necessarily determined for each test. Appropriate reference profiles stored in databases may also be used. These stored references profiles may, e.g., be derived from previous tests. The reference expression profile may also be a mathematical function or algorithm developed on the basis of a plurality of reference expression profiles.

[0018] The method of the invention is suitable for diagnosing lung cancer. The diagnosis may comprise determining type, rate and/or stage of lung cancer. The course of the disease and the success of therapy such as chemotherapy may be monitored. The method of the invention provides a prognosis on the survivor rate and enables to determine a patient's response to drugs.

[0019] The inventors succeeded in developing a generally applicable approach to arrive at miRNA signatures that are correlated with a particular disease. The general work flow is depicted in FIG. 9. In more detail, the following steps are accomplished:

[0020] 1. miRNAs are extracted from a biological sample of a patient, preferably a blood or serum sample or a sample comprising erythrocytes, leukocytes or thrombocytes, using suitable kits/purification methods

[0021] 2. The respective samples are measured using experimental techniques. These techniques include but are not restricted to:

[0022] Array based approaches

[0023] Real time quantitative polymerase chain reaction

[0024] Bead-based assays (e.g. Luminex)

[0025] Sequencing

[0026] Next Generation Sequencing

[0027] Mass Spectroscopy

[0028] 3. Mathematical approaches are applied to gather information on the value and the redundancy of single biomarkers. These methods include, but are not restricted to:

[0029] basic mathematic approaches (e.g. Fold Quotients, Signal to Noise ratios, Correlation)

[0030] statistical methods as hypothesis tests (e.g. t-test, Wilcoxon-Mann-Whitney test), the Area under the Receiver operator Characteristics Curve

[0031] Information Theory approaches, (e.g. the Mutual Information, Cross-entropy)

[0032] Probability theory (e.g. joint and conditional probabilities)

[0033] Combinations and modifications of the previously mentioned examples

[0034] 4. The information collected in 3) are used to estimate for each biomarker the diagnostic content or value. Usually, however, this diagnostic value is too small to get a highly accurate diagnosis with accuracy rates, specificities and sensitivities beyond the 90% barrier.

[0035] Please note that the diagnostic content for our miRNAs can be found in the attached figures. These figures include the miRNAs with the sequences, the fold quotient, the mutual information and the significance value as computed by a t-test.

[0036] 5. Thus statistical learning/machine learning/bioinformatics/computational approaches are applied to define subsets of biomarkers that are tailored for the detection of diseases. These techniques includes but are not restricted to

[0037] Wrapper subset selection techniques (e.g. forward step-wise, backward step-wise, combinatorial approaches, optimization approaches)

[0038] Filter subset selection methods (e.g. the methods mentioned in 3)

[0039] Principal Component Analysis

[0040] Combinations and modifications of such methods (e.g. hybrid approaches)

[0041] 6. The diagnostic content of each detected set can be estimated by mathematical and/or computational techniques to define the diagnostic information content of subsets.

[0042] 7. The subsets, detected in step 5, which may range from only a small number (at least two) to all measured biomarkers is then used to carry out a diagnosis. To this end, statistical learning/machine learning/bioinformatics/computational approaches are applied that include but are not restricted to any type of supervised or unsupervised analysis:

[0043] Classification techniques (e.g. naive Bayes, Linear Discriminant Analysis, Quadratic Discriminant Analysis Neural Nets, Tree based approaches, Support Vector Machines, Nearest Neighbour Approaches)

[0044] Regression techniques (e.g. linear Regression, Multiple Regression, logistic regression, probit regression, ordinal logistic regression ordinal Probit-Regression, Poisson Regression, negative binomial Regression, multinomial logistic Regression, truncated regression)

[0045] Clustering techniques (e.g. k-means clustering, hierarchical clustering, PCA)

[0046] Adaptations, extensions, and combinations of the previously mentioned approaches

[0047] The inventors surprisingly found out that the described approach yields in miRNA signatures that provide high diagnostic accuracy, specificity and sensitivity in the determination of lung cancer.

[0048] According to the invention, the disease to be determined is lung cancer, e.g. lung carcinoid, lung pleural mesothelioma or lung squamous cell carcinoma, in particular non-small cell lung carcinoma.

[0049] The inventors succeeded in determining miRNAs that are differentially regulated in samples from lung cancer patients as compared to healthy controls. A complete overview of all miRNAs that are found to be differentially regulated in blood samples of lung cancer patients is provided in the tables shown in FIGS. 10A and 10B. In the tables shown in FIGS. 10A and 10B, the miRNAs that are found to be differentially regulated are sorted in the order of their mutual information and in the order of their t-test significance as described in more detail below. Mutual information (MI) (Shannon, 1984) is an adequate measure to estimate the overall diagnostic information content of single biomarkers (Keller, Ludwig et al., 2006). According to the invention mutual information is considered as the reduction in uncertainty about the class labels "0" for controls and "1" for tumor samples due to the knowledge of the miRNA expression. The higher the value of the MI of a miRNA, the higher is the diagnostic content of the respective miRNA. The computation of the MI of each miRNA is explained in the experimental section below.

[0050] For example, the predetermined set of miRNAs representative for lung cancer comprises at least 1, 7 ,10 ,15 ,20, 25, 30, 35, 40, 50, 75, 100 of the miRNAs selected from the group consisting of hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a*, hsa-miR-26b, hsa-miR-604, hsa-miR-423-3p, hsa-miR-93*, hsa-miR-29a, hsa-miR-1248, hsa-miR-210, hsa-miR-19b, hsa-miR-453, hsa-miR-126*, hsa-miR-188-3p, hsa-miR-624*, hsa-miR-505*, hsa-miR-425, hsa-miR-339-3p, hsa-miR-668, hsa-miR-363*, hsa-miR-15b*, hsa-miR-29c*, hsa-miR-550*, hsa-miR-34c-3p, hsa-miR-20a, hsa-miR-374a, hsa-miR-145*, hsa-miR-302b, hsa-miR-106a, hsa-miR-30e, hsa-miR-223, hsa-miR-1269, hsa-let-7b, hsa-miR-542-3p, hsa-miR-516b*, hsa-miR-451, hsa-miR-519c-3p, hsa-miR-1244, hsa-miR-602, hsa-miR-361-3p, hsa-miR-19a*, hsa-miR-433, hsa-miR-1200, hsa-miR-522, hsa-miR-520f, hsa-miR-519c-5p, hsa-miR-192, hsa-miR-1245, hsa-miR-151-5p, hsa-miR-1288, hsa-miR-503, hsa-miR-563, hsa-miR-663b, hsa-let-7d*, hsa-miR-199a-5p, hsa-miR-720, hsa-miR-1246, hsa-miR-338-5p, hsa-miR-297, hsa-miR-1261, hsa-miR-922, hsa-miR-185, hsa-miR-611, hsa-miR-1272, hsa-miR-1299, hsa-miR-335*, hsa-miR-497, hsa-miR-1207-3p, hsa-miR-16, hsa-miR-1, hsa-miR-1291, hsa-miR-138-2*, hsa-miR-136, hsa-miR-548d-3p, hsa-miR-561, hsa-miR-548h, hsa-miR-331-3p, hsa-miR-186*, hsa-miR-145, hsa-miR-17, hsa-miR-30b, hsa-let-7f-1*, hsa-miR-1305, hsa-miR-129-5p, hsa-miR-1204, hsa-miR-106b*, hsa-miR-619, hsa-miR-34a*, hsa-miR-652, hsa-miR-1256, hsa-miR-20b*, hsa-miR-424*, hsa-miR-517a, hsa-miR-1284, hsa-miR-199b-3p, hsa-miR-599, hsa-miR-411, hsa-miR-23b, hsa-miR-1302, hsa-miR-449a, hsa-miR-548f, hsa-miR-597, hsa-miR-603, hsa-miR-1247, hsa-miR-1539, hsa-miR-1911, hsa-miR-325, hsa-miR-409-5p, hsa-miR-182, hsa-miR-658, hsa-miR-215, hsa-miR-147b, hsa-miR-30d, hsa-miR-378*, hsa-miR-221*, hsa-miR-34b, hsa-miR-593*, hsa-miR-552, hsa-miR-378, hsa-miR-143*, hsa-miR-1266, hsa-miR-554, hsa-miR-631, hsa-miR-609, hsa-miR-30c, hsa-miR-28-5p, hsa-miR-23a, hsa-miR-645, hsa-miR-647, hsa-miR-302b*, hsa-miR-607, hsa-miR-1289, hsa-miR-1324, hsa-miR-513a-3p, hsa-miR-939, hsa-miR-29b, hsa-miR-665, hsa-miR-18a, hsa-miR-1224-5p, hsa-miR-10a*, hsa-miR-181a*, hsa-miR-218-2*, hsa-miR-371-3p, hsa-miR-377, hsa-miR-140-5p, hsa-miR-301a, hsa-miR-1277, hsa-miR-130a*, hsa-miR-1912, hsa-miR-193b, hsa-miR-214*, hsa-miR-216b, hsa-miR-302f, hsa-miR-522*, hsa-miR-548j, hsa-miR-568, hsa-miR-648, hsa-miR-662, hsa-miR-222, hsa-miR-1287, hsa-miR-891b, hsa-miR-342-3p, hsa-miR-512-3p, hsa-miR-623, hsa-miR-208b, hsa-miR-16-1*, hsa-miR-551b, hsa-miR-146b-3p, hsa-miR-520b, hsa-miR-449b, hsa-miR-520g, hsa-miR-24-2*, hsa-miR-518f, hsa-miR-649, hsa-miR-32, hsa-miR-151-3p, hsa-miR-454, hsa-miR-101, hsa-miR-19b-1*, hsa-miR-509-5p, hsa-miR-144, hsa-miR-508-5p, hsa-miR-569, hsa-miR-636, hsa-miR-937, hsa-miR-346, hsa-miR-506, hsa-miR-379*, hsa-miR-1184, hsa-miR-579, hsa-miR-23b*, hsa-miR-1262, hsa-miR-153, hsa-miR-520e, hsa-miR-632, hsa-miR-106a*, hsa-miR-31*, hsa-miR-33b*, hsa-miR-654-3p, hsa-miR-99b*, hsa-miR-1278, hsa-miR-135b, hsa-let-7c*, hsa-miR-1468, hsa-miR-374b*, hsa-miR-514, hsa-miR-590-3p, hsa-miR-606, hsa-miR-369-3p, hsa-miR-488, hsa-miR-128, hsa-miR-362-5p, hsa-miR-671-5p, hsa-miR-874, hsa-miR-1911*, hsa-miR-1292, hsa-miR-194, hsa-miR-15b, hsa-miR-342-5p, hsa-miR-125b-2*, hsa-miR-1297, hsa-miR-933, hsa-miR-493*, hsa-miR-105, hsa-miR-141, hsa-miR-181c*, hsa-miR-193a-3p, hsa-miR-302c, hsa-miR-485-5p, hsa-miR-499-3p, hsa-miR-545, hsa-miR-548b-5p, hsa-miR-549, hsa-miR-576-5p, hsa-miR-577, hsa-miR-583, hsa-miR-587, hsa-miR-624, hsa-miR-646, hsa-miR-655, hsa-miR-885-5p, hsa-miR-194*, hsa-miR-299-5p, hsa-miR-337-3p, hsa-miR-493, hsa-miR-497*, hsa-miR-519a, hsa-miR-99a*, hsa-miR-1280, hsa-miR-523*, hsa-miR-198, hsa-miR-934, hsa-miR-30d*, hsa-miR-452*, hsa-miR-548b-3p, hsa-miR-586, hsa-miR-92b, hsa-miR-517b, hsa-miR-548a-3p, hsa-miR-875-5p, hsa-miR-431*, hsa-miR-384, hsa-miR-644, hsa-miR-1185, hsa-miR-29b-2*, hsa-miR-489, hsa-miR-566, hsa-miR-1538, hsa-miR-28-3p, hsa-let-7f-2*, hsa-miR-1322, hsa-miR-1827, hsa-miR-192*, hsa-miR-302e, hsa-miR-411*, hsa-miR-424, hsa-miR-582-3p, hsa-miR-629*, hsa-miR-491-3p, hsa-miR-519b-3p, hsa-miR-1197, hsa-miR-127-5p, hsa-miR-1286, hsa-miR-132*, hsa-miR-33b, hsa-miR-553, hsa-miR-620, hsa-miR-708, hsa-miR-892b, hsa-miR-520h, hsa-miR-500*, hsa-miR-551b*, hsa-miR-186, hsa-miR-558, hsa-miR-26a, hsa-miR-1263, hsa-miR-211, hsa-miR-1304, hsa-miR-220b, hsa-miR-891a, hsa-miR-1253, hsa-miR-1205, hsa-miR-137, hsa-miR-154*, hsa-miR-555, hsa-miR-887, hsa-miR-363, hsa-miR-1537, hsa-miR-219-1-3p, hsa-miR-220a, hsa-miR-222*, hsa-miR-323-3p, hsa-miR-376b, hsa-miR-490-5p, hsa-miR-523, hsa-miR-302a*, hsa-miR-27b*, hsa-miR-591, hsa-miR-888, hsa-miR-376a*, hsa-miR-618, hsa-miR-1182, hsa-miR-532-3p, hsa-miR-181b, hsa-miR-521, hsa-miR-545*, hsa-miR-9*, hsa-miR-920, hsa-miR-571, hsa-miR-635, hsa-miR-200b, hsa-miR-455-5p, hsa-miR-876-3p, hsa-miR-373*, hsa-miR-146a*, hsa-miR-122*, hsa-miR-450b-3p, hsa-miR-24, hsa-miR-484, hsa-miR-103-as, hsa-miR-380, hsa-miR-513a-5p, hsa-miR-509-3-5p, hsa-miR-873, hsa-miR-556-5p, hsa-miR-369-5p, hsa-miR-653, hsa-miR-767-3p, hsa-miR-516a-3p, hsa-miR-520c-3p, hsa-miR-708*, hsa-miR-924, hsa-miR-520d-5p, hsa-miR-512-5p, hsa-miR-374a*, hsa-miR-921, hsa-miR-1206, hsa-miR-1259, hsa-miR-525-5p, hsa-miR-200a*, hsa-miR-1293, hsa-miR-372, hsa-miR-548a-5p, hsa-miR-548k, hsa-miR-1300, hsa-miR-1264, hsa-miR-551a, hsa-miR-196b, hsa-miR-32*, hsa-miR-33a, hsa-miR-548d-5p, hsa-miR-616, hsa-miR-876-5p, hsa-miR-508-3p, hsa-miR-26a-2*, hsa-miR-187, hsa-miR-199a-3p, hsa-miR-96*, hsa-miR-18b, hsa-miR-432*, hsa-miR-509-3p, hsa-miR-1183, hsa-miR-626, hsa-miR-513b, hsa-miR-617, hsa-miR-9, hsa-miR-519e, hsa-miR-204, hsa-miR-29c, hsa-miR-1268, hsa-miR-122, hsa-miR-7-2*, hsa-miR-15a*, hsa-miR-181d, hsa-miR-219-5p, hsa-miR-302d, hsa-miR-34a, hsa-miR-410, hsa-miR-33a*, hsa-miR-502-3p, hsa-miR-379, hsa-miR-498, hsa-miR-518d-5p, hsa-miR-556-3p, hsa-miR-502-5p, hsa-miR-31, hsa-miR-100, hsa-miR-296-3p, hsa-miR-615-5p, hsa-miR-21*, hsa-miR-657, hsa-miR-651, hsa-miR-765, hsa-miR-548m, hsa-miR-219-2-3p, hsa-miR-501-3p, hsa-miR-302a, hsa-miR-202*, hsa-miR-206, hsa-miR-520d-3p, hsa-miR-548i, hsa-miR-511, hsa-miR-30a, hsa-miR-1224-3p, hsa-miR-525-3p, hsa-miR-1225-5p, hsa-miR-223*, hsa-miR-615-3p, hsa-miR-570, hsa-miR-320a, hsa-miR-770-5p, hsa-miR-582-5p, hsa-miR-590-5p, hsa-miR-659, hsa-miR-1251, hsa-miR-664, hsa-miR-488*, hsa-miR-548g, hsa-miR-802, hsa-miR-542-5p, hsa-miR-190, hsa-miR-218-1*, hsa-miR-367*, hsa-miR-450a, hsa-miR-367, hsa-miR-124, hsa-miR-767-5p, hsa-miR-200c, hsa-miR-572, hsa-miR-526a, hsa-miR-936, hsa-miR-548n, hsa-miR-21, hsa-miR-182*, hsa-miR-34c-5p, hsa-miR-429, hsa-miR-628-5p, hsa-miR-29a*, hsa-miR-370, hsa-let-7a*, hsa-miR-101*, hsa-miR-559, hsa-miR-217, hsa-miR-519b-5p, hsa-miR-30e*, hsa-miR-147, hsa-miR-487b, hsa-miR-888*, hsa-miR-205, hsa-miR-1257, hsa-miR-7, hsa-miR-296-5p, hsa-miR-1255a, hsa-miR-380*, hsa-miR-1275, hsa-miR-330-5p, hsa-miR-1243, hsa-miR-136*, hsa-miR-141*, hsa-miR-517c, hsa-miR-621, hsa-miR-1915*, hsa-miR-541, hsa-miR-543, hsa-miR-942, hsa-miR-26a-1*, hsa-miR-567, hsa-miR-184, hsa-miR-376a, hsa-miR-124*, hsa-miR-1254, hsa-miR-1207-5p, hsa-miR-580, hsa-let-7b*, hsa-miR-539, hsa-miR-520a-3p, hsa-miR-585, hsa-miR-675b, hsa-miR-943, hsa-miR-573, hsa-miR-93, hsa-miR-27a*, hsa-miR-613, hsa-miR-220c, hsa-miR-524-3p, hsa-miR-500, hsa-miR-1201, hsa-miR-20a*, hsa-miR-1914*, hsa-miR-425*, hsa-miR-515-3p, hsa-miR-377*, hsa-miR-504, hsa-miR-548c-3p, hsa-miR-1276, hsa-miR-138, hsa-miR-431, hsa-miR-494, hsa-miR-448, hsa-miR-633, hsa-miR-487a, hsa-miR-149, hsa-miR-300, hsa-miR-1826, hsa-miR-127-3p, hsa-miR-486-5p, hsa-miR-148a, hsa-miR-1294, hsa-miR-5481, hsa-miR-142-5p, hsa-miR-889, hsa-miR-365, hsa-miR-99b, hsa-miR-200b*, hsa-miR-200a, hsa-miR-518e, hsa-miR-612, hsa-miR-183*, hsa-miR-148b, hsa-miR-103, hsa-miR-5480, hsa-miR-1203, hsa-miR-135a*, hsa-miR-383, hsa-miR-1913, hsa-miR-373, hsa-miR-371-5p, hsa-miR-298, hsa-miR-758, hsa-miR-412, hsa-miR-518c, hsa-miR-589*, hsa-miR-643, hsa-miR-592, hsa-miR-892a, hsa-miR-944, hsa-miR-576-3p, hsa-miR-581, hsa-miR-625*, hsa-miR-1260, hsa-miR-1281, hsa-miR-337-5p, hsa-miR-133b, hsa-miR-92a-2*, hsa-miR-100*, hsa-miR-589, hsa-miR-218, hsa-miR-224, hsa-miR-16-2*, hsa-miR-301b, hsa-miR-190b, hsa-miR-375, hsa-miR-548p, hsa-miR-185*, hsa-miR-519d, hsa-miR-605, hsa-miR-877, hsa-miR-125a-3p, hsa-miR-744*, hsa-miR-520c-5p, hsa-miR-148a*, hsa-miR-212, hsa-miR-505, hsa-miR-496, hsa-miR-1323, hsa-miR-548e, hsa-miR-628-3p, hsa-miR-1914, hsa-miR-584, hsa-miR-135b*, hsa-miR-1295, hsa-miR-95, hsa-miR-133a, hsa-miR-485-3p, hsa-miR-541*, hsa-miR-374b, hsa-miR-329, hsa-miR-483-5p, hsa-miR-885-3p, hsa-let-7i*, hsa-miR-935, hsa-miR-130b, hsa-miR-1274a, hsa-miR-1226, hsa-miR-518e*, hsa-miR-1225-3p, hsa-miR-923, hsa-miR-196a*, hsa-miR-1270, hsa-miR-1271, hsa-miR-610, hsa-miR-574-3p, hsa-miR-1282, hsa-miR-10b*, hsa-miR-216a, hsa-miR-144*, hsa-miR-23a*, hsa-miR-499-5p, hsa-miR-183, hsa-miR-490-3p, hsa-miR-330-3p, hsa-let-7g*, hsa-miR-483-3p, hsa-miR-214, hsa-miR-34b*, hsa-miR-302d*, hsa-miR-382, hsa-miR-454*, hsa-miR-1202, hsa-miR-202, hsa-miR-544, hsa-miR-593, hsa-miR-760, hsa-miR-940, hsa-let-7e*, hsa-miR-1237, hsa-miR-18b*, hsa-miR-630, hsa-miR-519e*, hsa-miR-452, hsa-miR-26b*, hsa-miR-516b, hsa-miR-299-3p, hsa-miR-381, hsa-miR-340, hsa-miR-132, hsa-miR-142-3p, hsa-miR-125b-1*, hsa-miR-30c-2*, hsa-miR-627, hsa-miR-1908, hsa-miR-1267, hsa-miR-507, hsa-miR-188-5p, hsa-miR-486-3p, hsa-miR-596, hsa-miR-193a-5p, hsa-miR-671-3p, hsa-miR-24-1*, hsa-miR-19b-2*, hsa-miR-1308, hsa-miR-208a, hsa-miR-135a, hsa-miR-331-5p, hsa-miR-181c, hsa-miR-640, hsa-miR-1909, hsa-miR-629, hsa-miR-10a, hsa-miR-491-5p, hsa-miR-492, hsa-miR-516a-5p, hsa-miR-510, hsa-miR-1915, hsa-miR-518c*, hsa-miR-1273, hsa-miR-25*, hsa-miR-744, hsa-miR-550, hsa-miR-890, hsa-miR-1303, hsa-miR-650, hsa-miR-1227, hsa-miR-595, hsa-miR-1255b, hsa-miR-1252, hsa-miR-455-3p, hsa-miR-345, hsa-miR-96, hsa-miR-1321, hsa-miR-513c, hsa-miR-548c-5p, hsa-miR-663, hsa-miR-320c, hsa-miR-320b, hsa-miR-654-5p, hsa-miR-326, hsa-miR-1825, hsa-miR-328, hsa-miR-146b-5p, hsa-miR-886-3p, hsa-miR-1909*, hsa-miR-1469, hsa-miR-338-3p, hsa-miR-886-5p, hsa-miR-601, hsa-miR-1298, hsa-miR-1910, hsa-miR-1226*, hsa-miR-421, hsa-miR-1471, hsa-miR-150*, hsa-miR-1229, hsa-miR-17*, hsa-miR-320d, hsa-miR-10b, hsa-miR-766, hsa-miR-600, hsa-miR-641, hsa-miR-340*, hsa-miR-616*, hsa-miR-520a-5p, hsa-miR-1179, hsa-miR-1178, hsa-miR-30b*, hsa-miR-155*, hsa-miR-138-1*, hsa-miR-501-5p, hsa-miR-191, hsa-miR-107, hsa-miR-639, hsa-miR-518d-3p, hsa-miR-106b, hsa-miR-129-3p, hsa-miR-1306, hsa-miR-187*, hsa-miR-125b, hsa-miR-642, hsa-miR-30a*, hsa-miR-139-5p, hsa-miR-1307, hsa-miR-769-3p, hsa-miR-532-5p, hsa-miR-7-1*, hsa-miR-196a, hsa-miR-1296, hsa-miR-191*, hsa-miR-221, hsa-miR-92a-1*, hsa-miR-1285, hsa-miR-518f*, hsa-miR-1233, hsa-miR-1290, hsa-miR-598, hsa-miR-769-5p, hsa-miR-614, hsa-miR-578, hsa-miR-1301, hsa-miR-515-5p, hsa-miR-564, hsa-miR-634, hsa-miR-518b, hsa-miR-941, hsa-miR-376c, hsa-miR-195*, hsa-miR-518a-5p, hsa-miR-557, hsa-miR-1228*, hsa-miR-22*, hsa-miR-1234, hsa-miR-149*, hsa-miR-30c-1*, hsa-miR-200c*, hsa-miR-1181, hsa-miR-323-5p, hsa-miR-1231, hsa-miR-203, hsa-miR-302c*, hsa-miR-99a, hsa-miR-146a, hsa-miR-656, hsa-miR-526b*, hsa-miR-148b*, hsa-miR-181a, hsa-miR-622, hsa-miR-125a-5p, hsa-miR-152, hsa-miR-197, hsa-miR-27b, hsa-miR-1236, hsa-miR-495, hsa-miR-143, hsa-miR-362-3p, hsa-miR-675, hsa-miR-1274b, hsa-miR-139-3p, hsa-miR-130b*, hsa-miR-1228, hsa-miR-1180, hsa-miR-575, hsa-miR-134, hsa-miR-875-3p, hsa-miR-92b*, hsa-miR-660, hsa-miR-526b, hsa-miR-422a, hsa-miR-1250, hsa-miR-938, hsa-miR-608, hsa-miR-1279, hsa-miR-1249, hsa-miR-661, hsa-miR-1208, hsa-miR-130a, hsa-miR-450b-5p, hsa-miR-432, hsa-miR-409-3p, hsa-miR-527, hsa-miR-877*, hsa-miR-1238, hsa-miR-517*, hsa-miR-193b*, hsa-miR-524-5p, hsa-miR-1258, hsa-miR-154, hsa-miR-637, hsa-miR-588, hsa-miR-155, hsa-miR-664*, hsa-miR-1470, hsa-miR-105*, hsa-miR-324-5p, hsa-miR-129*, hsa-miR-625, hsa-miR-519a*, hsa-miR-181a-2*, hsa-miR-199b-5p, hsa-miR-27a, hsa-miR-518a-3p, hsa-miR-1265, hsa-miR-92a, hsa-miR-29b-1*, hsa-miR-150, hsa-miR-335, hsa-miR-638.

[0051] The miRNAs that provide the highest mutual information in samples from lung cancer patients compared to healthy controls are hsa-miR-361-5p, hsa-miR-23b, hsa-miR-126, hsa-miR-527, hsa-miR-29a, hsa-let-7i, hsa-miR-19a, hsa-miR-28-5p, hsa-miR-185*, hsa-miR-23a, hsa-miR-1914*, hsa-miR-29c, hsa-miR-505*, hsa-let-7d, hsa-miR-378, hsa-miR-29b, hsa-miR-604, hsa-miR-29b, hsa-let-7b, hsa-miR-299-3p, hsa-miR-423-3p, hsa-miR-18a*, hsa-miR-1909, hsa-let-7c, hsa-miR-15a, hsa-miR-425, hsa-miR-93*, hsa-miR-665, hsa-miR-30e, hsa-miR-339-3p, hsa-miR-1307, hsa-miR-625*, hsa-miR-193a-5p, hsa-miR-130b, hsa-miR-17*, hsa-miR-574-5p, hsa-miR-324-3p (group (a)).

[0052] Further, the measured miRNA profiles of FIGS. 10A and 10B were classified according to their significance in t-tests as described in more detail in the experimental section. The miRNAs that performed best according to the t-test results are hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a*, hsa-miR-26b, hsa-miR-604, hsa-miR-423-3p, hsa-miR-93* (group (b)). A comparison of a subset of 15 of these miRNAs is depicted in FIG. 12.

[0053] The miRNAs given above that have been grouped in the order of their performance in the t-tests or in the order of their MI-values provide the highest diagnostic power. Thus, preferably the predetermined set of miRNAs for the diagnosis of lung cancer comprises one or more nucleic acids selected from the above groups (a) and (b) of miRNAs. The predetermined set of miRNAs should preferably comprise at least 7, preferably at least 10, 15, 20 or 24 of the indicated nucleic acids. Most preferably, all of the above indicated miRNAs are included in the predetermined set of miRNAs. It is particularly preferred to include the 24, 20, 15, 10 or at least 7 of the first mentioned miRNAs in the order of their performance in the t-tests or of their MI-values. A comparison of the results obtained by determining 4, 8, 10, 16, 20, 24, 28 or 40 miRNAs provided in FIG. 13A-G shows that the accuracy of the diagnosis is improved, the more miRNAs are measured.

[0054] In a particularly preferred embodiment of the method of the invention, the predetermined set of miRNAs includes the miRNAs hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a* and hsa-miR-26b.

[0055] In a further particularly preferred embodiment of the method of the invention, the miRNAs are selected from the miRNAs shown in FIG. 11A. The predetermined set of miRNAs should preferably comprise at least 7, preferably at least 10, 15, 20 or 24 of the indicated nucleic acids. It is particularly preferred to include the 24, 20, 15, 10 or at least 7 of the first mentioned miRNAs according to their order in the table in FIG. 11A.

[0056] In another embodiment, the predetermined set of miRNAs for the diagnosis of lung cancer comprises at least one preferred signature L1-251 as shown in FIG. 11B. It should be noted that preferred diagnostic sets may also comprise one or more miRNAs of the miRNAs disclosed in FIG. 11B and any combination of the miRNAs together with one or more further diagnostically relevant miRNA from FIGS. 10A, 10B or 11A. Preferred predetermined sets of miRNA molecules based on FIG. 11B comprise at least 3, 4, 5, 6, 7, 8, 9 or 10 miRNAs and up to 10, 15, or 20 or more miRNAs.

[0057] For the diagnosis of different types of diseases, such as for a different type of cancer, a different predetermined set of miRNAs should be determined in step (a) of the method of the invention. The relevant miRNA signatures can be obtained according to the workflow depicted in FIG. 9 and as explained above.

[0058] Another embodiment of the present invention is a kit for diagnosing a disease, comprising means for determining an expression profile of a predetermined set of miRNAs in a biological sample, in particular in a blood, plasma, and/or serum sample including whole blood, plasma, serum or fractions thereof, or in a sample comprising peripheral blood mononuclear cells, erythrocytes, leukocytes and/or thrombocytes. Preferably, one or more reference expression profiles are also provided which show the expression profile of the same set of miRNAs in the same type of biological sample, in particular in a blood and/or serum sample, obtained from one or more healthy subjects. A comparison to said reference expression profile(s) allows for the diagnosis of the disease.

[0059] The kit is preferably a test kit for detecting a predetermined set of miRNAs in sample by nucleic acid hybridization and optionally amplification such as PCR or RT-PCR. The kit preferably comprises probes and/or primers for detecting a predetermined set of miRNAs. Further, the kit may comprise enzymes and reagents including reagents for cDNA synthesis from miRNAs prior to realtime PCR.

[0060] A kit for diagnosing lung cancer preferably comprises means for determining the expression profile of one or more miRNAs selected from the group (a) consisting of hsa-miR-361-5p, hsa-miR-23b, hsa-miR-126, hsa-miR-527, hsa-miR-29a, hsa-let-7i, hsa-miR-19a, hsa-miR-28-5p, hsa-miR-185*, hsa-miR-23a, hsa-miR-1914*, hsa-miR-29c, hsa-miR-505*, hsa-let-7d, hsa-miR-378, hsa-miR-29b, hsa-miR-604, hsa-miR-29b, hsa-let-7b, hsa-miR-299-3p, hsa-miR-423-3p, hsa-miR-18a*, hsa-miR-1909, hsa-let-7c, hsa-miR-15a, hsa-miR-425, hsa-miR-93*, hsa-miR-665, hsa-miR-30e, hsa-miR-339-3p, hsa-miR-1307, hsa-miR-625*, hsa-miR-193a-5p, hsa-miR-130b, hsa-miR-17*, hsa-miR-574-5p and hsa-miR-324-3p.

[0061] According to another embodiment of the invention, the kit for diagnosing lung cancer preferably comprises means for determining the expression profile of one or more miRNAs selected from the group (b) consisting of hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a*, hsa-miR-26b, hsa-miR-604, hsa-miR-423-3p and hsa-miR-93*.

[0062] In a preferred embodiment, the kit comprises means for determining at least 7, preferably at least 10, 15, 20 or 24 of the indicated groups of miRNAs. It is particularly preferred to include means for determining the 24, 20, 15, 10 or at least 7 of the first mentioned miRNAs in the order of their MI-values or their performance in the t-tests as shown in the tables in FIGS. 10 and 11. Most preferably, means for determining all of the above indicated miRNAs are included in the kit for diagnosing lung cancer. The kit is particularly suitable for diagnosing lung cancer in a blood, plasma and/or serum sample or in a sample comprising peripheral erythrocytes, leukocytes and/or thrombocytes.

[0063] In a particularly preferred embodiment, the kit comprises means for determining the miRNAs hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a* and hsa-miR-26b.

[0064] The means for determining a predetermined set of miRNAs may for example comprise a microarray comprising miRNA-specific oligonucleotide probes. In a preferred embodiment, the microarray comprises miRNA-specific oligonucleotide probes for one or more miRNAs selected from the group consisting of (a) hsa-miR-361-5p, hsa-miR-23b, hsa-miR-126, hsa-miR-527, hsa-miR-29a, hsa-let-7i, hsa-miR-19a, hsa-miR-28-5p, hsa-miR-185*, hsa-miR-23a, hsa-miR-1914*, hsa-miR-29c, hsa-miR-505*, hsa-let-7d, hsa-miR-378, hsa-miR-29b, hsa-miR-604, hsa-miR-29b, hsa-let-7b, hsa-miR-299-3p, hsa-miR-423-3p, hsa-miR-18a*, hsa-miR-1909, hsa-let-7c, hsa-miR-15a, hsa-miR-425, hsa-miR-93*, hsa-miR-665, hsa-miR-30e, hsa-miR-339-3p, hsa-miR-1307, hsa-miR-625*, hsa-miR-193a-5p, hsa-miR-130b, hsa-miR-17*, hsa-miR-574-5p and hsa-miR-324-3p or (b) hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a*, hsa-miR-26b, hsa-miR-604, hsa-miR-423-3p and hsa-miR-93*. In a preferred embodiment, the microarray comprises oligonucleotide probes for determining at least 7, preferably at least 10, 15, 20 or 24 of the indicated groups (a) and (b) of miRNAs. It is particularly preferred to include oligonucleotide probes for determining the 24, 20, 15, 10 or at least 7 of the first mentioned miRNAs in the order of their MI-values or their performance in the t-tests as shown in the tables in FIGS. 10 and 11. Most preferably, oligonucleotide probes for determining all of the above indicated miRNAs of groups (a) or (b) are included in the microarray for diagnosing lung cancer.

[0065] In a particularly preferred embodiment, the microarray comprises oligonucleotide probes for determining the miRNAs hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a* and hsa-miR-26b.

[0066] The microarray can comprise oligonucleotide probes obtained from known or predicted miRNA sequences. The array may contain different oligonucleotide probes for each miRNA, for example one containing the active mature sequence and another being specific for the precursor of the miRNA. The array may also contain controls such as one or more sequences differing from the human orthologs by only a few bases, which can serve as controls for hybridization stringency conditions. It is also possible to include viral miRNAs or putative miRNAs as predicted from bioinformatic tools. Further, it is possible to include appropriate controls for non-specific hybridization on the microarray.

[0067] The invention also relates to sets of oligo- or polynucleotides for diagnosing lung cancer comprising the sequences of at least 5, preferably at least 7, 10, 15, 20 or all of the indicated miRNAs, and/or the complement of such sequences. It is particularly preferred to include oligo- or polynucleotides for detecting of the most significant miRNAs, which are represented by their order in the table depicted in FIGS. 10A, 10B or 11A. In a further embodiment, the set includes oligo- or polynucleotides for detecting the miRNA sets based on FIG. 11B as described above. The oligo- or polynucleotides preferably have a length of 10, 15 or 20 and up to 30, 40, 50, 100 or more nucleotides. The term "oligo- or polynucleotides" includes single- or double-stranded molecules, RNA molecules, DNA molecules or nucleic acid analogs such as PNA or LNA.

[0068] Another embodiment of the present invention relates to a method for the assessment of a clinical condition related to lung cancer of a patient.

[0069] Recent developments have shown that there is a tendency towards smaller sets of biomarkers for the detection of diseases. However, for single biomarkers and small biomarker sets, there is only a basic understanding whether these biomarkers are specific for only the single diseases or whether they occur in any other disease.

[0070] Therefore, the present inventors developed a novel class of diagnostic tests improving the current test scenarios. The inventors found out that a variety of diseases are correlated with a specific expression profile of miRNAs. In case a patient is affected by a particular disease, several miRNAs are present in larger amounts compared to a healthy normal control, whereas the amount of other miRNAs is decreased. Interestingly, the amount of some miRNAs is deregulated, i.e. increased or decreased, in more than one disease. The miRNA profile for a particular disease therefore shows conformity with the miRNA profile of other diseases in regard of individual miRNAs while other miRNAs show significant differences. If the expression profile of a large variety of miRNAs in a biological sample of a patient is measured, the comparison of the expression profile with a variety of reference expression profiles which are each characteristic for different diseases makes it possible to obtain information about the clinical condition of a certain patient and to determine, which disease(s) is/are present in said patient.

[0071] A further subject matter of the invention is a method for the assessment of a clinical condition related to lung cancer of a patient comprising the steps

[0072] (a) providing a sample from the patient,

[0073] (b) determining a predetermined set of miRNAs in said sample to obtain a miRNA expression profile,

[0074] (c) comparing said miRNA expression profile with a plurality of miRNA reference expression profiles characteristic for different diseases, and

[0075] (d) assessing the clinical condition of the patient based on the comparison of step (c).

[0076] The inventors found out that the above method for the assessment of a clinical condition makes it possible to carry out an integrative diagnosis of a wide variety of diseases, particularly including lung cancer. Comparing a miRNA profile obtained from a biological sample of a patient whose clinical condition is not known with a plurality of reference profiles characteristic for different diseases enables the diagnosis of a wide variety of diseases with high specificity and sensitivity.

[0077] A "biological sample" in terms of the invention means a sample of biological tissue or fluid as described hereinabove. Examples of biological samples are sections of tissues, blood, blood fractions, plasma, serum, urine or samples from other peripheral sources. Preferred biological samples are blood, plasma and/or serum samples including blood fractions such as PBMC.

[0078] The set of miRNAs determined in step (d) preferably includes a large number of different miRNAs. It is particularly preferred to use at least 10, 20, 30, 50, preferably at least 100, 200, 500 or 1,000 miRNAs. Most preferably, all known miRNAs are included in the set of miRNAs determined in step (b) Such a complex set of miRNA-biomarkers enables a diagnosis with higher specificity and sensitivity compared to single biomarkers or sets of only a few dozens of such markers.

[0079] The determination of the set of miRNAs can be done as described herein above. Preferably, the determination is done on an experimental platform which shows a high degree of automation to minimize experimental variations, measure results time- and cost-efficiently, measures results highly reproduceably and be able for measuring more than one sample at once in order to ensure a high throughput.

[0080] Step (c) preferably includes a comparison of the miRNA profile measured for a patient with a large number of different reference profiles to provide information about the presence of as many different diseases as possible. The reference expression profiles may be laid down in a database, e.g. an internet database, a centralized or a decentralized database. The reference profiles do not necessarily have to include information about all miRNAs included in step (b), which are determined in the sample of the patient. It is, according to the invention, sufficient if the reference profile provides information on those miRNAs which are altered to a large extent compared to the condition of a healthy individual in case of the presence of a disease. Alternatively, the said relevant reference may be a mathematical function or algorithm.

[0081] Preferably, an miRNA reference profile or the relevant reference according to the invention provides information on miRNA expression characteristic for a particular disease in the same type of biological sample as used in step (b) for determining a predetermined set of miRNAs in a sample from a patient. This means that, if a patient with an unknown disease is to be classified with the analysis of a blood sample, the comparison is preferably made with miRNA reference expression profiles, which do also relate to the miRNA expression pattern in a blood sample.

[0082] The reference profiles or the relevant reference characteristic for particular diseases provide information on one or more miRNAs, which are, in case of the disease, highly deregulated, for example strongly increased or decreased, as compared to a healthy condition. It is not necessary for the reference profiles to provide information about all miRNAs included in the set of biomarkers determined in step (b). However, the more miRNAs are included in the reference profile or relevant reference, the more precise the diagnosis will be. If, for example, a reference profile for lung cancer is included, it is preferred to include the characteristic miRNAs for lung cancer.

[0083] Another embodiment of this aspect of the invention is a kit for the assessment of a clinical condition related to lung cancer of a patient comprising

[0084] (a) means for determining a predetermined set of miRNAs in a biological sample from a patient, and

[0085] (b) a plurality of miRNA reference expression profiles characteristic for different diseases or a mathematical function that allows for the diagnosis on the basis of the data derived from the miRNA expression profiles of a patient.

[0086] The set of miRNAs to be determined in a biological sample from a patient preferably includes a large number of different miRNAs. It is particularly preferred to include all known miRNAs in the set of miRNAs to be determined. In each case, said predetermined set of miRNAs should include those miRNAs for which information is provided in the reference profiles characteristic for particular diseases. It is understood that only in case the set of miRNAs determined in a biological sample from a patient comprises those miRNAs included in the reference profile/reference for a disease, a diagnosis regarding this particular disease can be provided or otherwise the diagnosis may be less informative.

[0087] The assessment of a clinical condition of a patient according to the invention is suitable for diagnosing any diseases which are correlated with a characteristic miRNA profile. Accordingly, the kit for the assessment of a clinical condition preferably includes reference profiles/references for a plurality of diseases that are correlated with a characteristic miRNA profile. It is understood that all miRNAs that are significantly deregulated in the disease states for which reference profiles are provided should be included in the set of miRNAs to be determined in a biological sample from a patient. If the kit for the assessment of a clinical condition of a patient should provide information regarding, e.g. lung cancer or multiple sclerosis, a reference profile should be available providing information about the significantly deregulated miRNAs compared to a normal or any other relevant control individual or any other relevant control individual(s). A kit for the assessment of a clinical condition shall provide information on the presence of lung cancer, a reference profile characteristic for lung cancer should be included. Said reference profile preferably includes information on those miRNAs that are most significantly deregulated in the case of lung cancer. The relevant miRNAs are as disclosed hereinabove.

[0088] The invention will now be illustrated by the following figures and the non-limiting experimental examples.

FIGURES

[0089] FIG. 1:

[0090] Scheme of a miRNA hybridization assay for use in the invention.

[0091] miRNA capture probes consist of 1 miRNA probe sequence stretch that is linked to support via 3'-end or alternatively by 5'-end (not depicted here)

[0092] the miRNA probe sequence stretches are complementary to miRNA target sequences

[0093] each miRNA capture probe can bind 1 miRNA target sequences

[0094] the miRNA target sequences are labeled prior to hybridization (e.g. by biotin labeling)

[0095] FIG. 2:

[0096] Scheme of an miRNA tandem hybridization assay for use in the invention

[0097] miRNA capture probes consist of 2 DNA-based miRNA probe sequence stretches that are linked to each other by a spacer element

[0098] the miRNA probe sequence stretches are complementary to miRNA target sequences

[0099] each miRNA capture probe can bind 2 miRNA target sequences

[0100] the spacer sequence consists of 0-8 nucleotides the miRNA target sequences are labeled prior to hybridization (e.g. by biotin labeling)

[0101] FIG. 3:

[0102] miRNA RAKE-Assay for use in the invention (PT Nelson et al., Nature Methods, 2004, 1(2), 1)

[0103] the miRNA capture probes consist of one miRNA probe sequence stretch (green) and one elongation element (orange)

[0104] probes are oriented 5'.fwdarw.3', presenting a free terminal 3'-OH

[0105] the miRNA probe sequence stretch (green) is complementary to miRNA target sequences (dark green)

[0106] the elongation sequences (orange) can be freely chosen and is typically between 1-12 nucleotides long, preferably a homomeric sequence

[0107] each miRNA capture probe can bind 1 miRNA target sequences

[0108] the miRNA target sequences are NOT labeled prior to hybridization

[0109] Labeling occurs after hybridization during elongation by polymerase extention reaction

[0110] Biochip is not reusable due to exonuclease treatment

[0111] FIG. 4:

[0112] miRNA MPEA-Assay for use in the invention (Vorwerk S. et al., Microfluidic-based enzymatic on-chip labeling of miRNAs, N. Biotechnol. 2008; 25(2-3):142-9. Epub 2008 Aug. 20)

[0113] the miRNA capture probes consist of one miRNA probe sequence stretch (green) and one elongation element (orange)

[0114] probes are oriented 3'.fwdarw.5', presenting a free terminal 5'-OH the miRNA probe sequence stretch (green) is complementary to miRNA target sequences (dark green)

[0115] the elongation sequences (orange) can be freely chosen and is typically between 1-12 nucleotides long, preferably a homomeric sequence

[0116] each miRNA capture probe can bind 1 miRNA target sequences

[0117] the miRNA target sequences are NOT labeled prior to hybridization

[0118] Labeling occurs after hybridization during elongation by polymerase extention reaction

[0119] Biochip is reusable after removal of target / elongated target

[0120] FIG. 5:

[0121] miRNA capture probe design

[0122] Depicted is the design of a capture probe for the exemplary miRNA human mature miRNA let-7a for use in the various types of hybridization assays shown in FIGS. 1-4. SP=spacer element; EL=elongation element

[0123] FIG. 6:

[0124] Spacer Element.

[0125] Capture probes for use in e.g. a tandem hybridization assay as shown in FIG. 2 may comprise a spacer element SP. The spacer element represents a nucleotide sequence with n=0-12 nucleotides chosen on the basis of showing low complementarity to potential target sequences, therefore resulting in no to low degree of crosshybridization to target mixture. Preferably, n=0, i.e. there is no spacer between the 2 miRNA probe sequence stretches.

[0126] FIG. 7:

[0127] Elongation element

[0128] A capture probe, e.g. for use in a RAKE or MPEA assay as shown in FIGS. 3 and 4 may include an elongation element. The elongation element comprises a nucleotide sequence with N=0-30 nucleotides chosen on the basis of showing low complementarity to potential target sequences, therefore resulting in no to low degree of crosshybridization to target mixture. Preferred is a homomeric sequence stretch --N.sub.n-- with n=1-30, N=A or C, or T, or G. Especially preferred is a homomeric sequence stretch --Nn- with n=1-12, N=A or C, or T, or G.

[0129] FIG. 8:

[0130] Pearson Correlation Coefficient depending on the number of elongated nucleotides in capture probes in an MPEA assay.

[0131] FIG. 9:

[0132] Diagram describing the general approach for determining miRNA signatures for use as biomarkers in disease diagnosis.

[0133] FIG. 10A:

[0134] Overview of all miRNAs that are found to be differentially regulated in blood samples of lung cancer patients, grouped according to their mutual information (MI).

[0135] FIG. 10B:

[0136] Overview of all miRNAs that are found to be differentially regulated in blood samples of lung cancer patients, grouped according to their results in t-tests.

[0137] FIG. 11A:

[0138] Overview of preferred miRNAs that are found to be significantly (p<0.1) differentially regulated in blood samples of lung cancer patients.

[0139] FIG. 11B:

[0140] Overview of preferred signatures of miRNAs for the diagnosis of lung cancer.

[0141] FIG. 12:

[0142] Expression of some relevant miRNAs. The bar-chart shows for 15 deregulated miRNAs the median value of cancer samples and normal samples. Here, blue bars correspond to cancer samples while red bars to controls.

[0143] FIGS. 13A-13G:

[0144] Bar diagrams showing a classification of the accuracy, specificity and sensitivity of the diagnosis of lung cancer based on blood samples using different sizes of subsets of miRNAs. Blue bars represent accuracy, specificity and sensitivity of the diagnosis using the indicated biomarkers and red bars represent the results of the same experiments of random classifications. The relevant value is the population median (horizontal black lines inside the bars).

[0145] FIG. 13A: 4 biomarkers:

[0146] hsa-miR-126, hsa-miR-423-5p, hsa-let-7i and hsa-let-7d;

[0147] FIG. 13B: 8 biomarkers:

[0148] hsa-miR-126, hsa-miR-423-5p, hsa-let-7i; hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, and hsa-miR-19a;

[0149] FIG. 13C: 10 biomarkers:

[0150] hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a, hsa-miR-574-5p, and hsa-miR-324-3p;

[0151] FIG. 13D: 16 biomarkers:

[0152] hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a; hsa-miR-574-5p; hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, and has-let-7f;

[0153] FIG. 13E: 20 biomarkers:

[0154] hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a; hsa-miR-574-5p; hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p and hsa-miR-339-5p;

[0155] FIG. 13F: 28 biomarkers:

[0156] hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a; hsa-miR-574-5p; hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a*, hsa-miR-26b, hsa-miR-604, hsa-miR-423-3p, hsa-miR-93*, and hsa-miR-29a;

[0157] FIG. 13G: 40 biomarkers:

[0158] hsa-miR-126, hsa-miR-423-5p, hsa-let-7i, hsa-let-7d, hsa-miR-22, hsa-miR-15a, hsa-miR-98, hsa-miR-19a; hsa-miR-574-5p; hsa-miR-324-3p, hsa-miR-20b, hsa-miR-25, hsa-miR-195, hsa-let-7e, hsa-let-7c, hsa-let-7f, hsa-let-7a, hsa-let-7g, hsa-miR-140-3p, hsa-miR-339-5p, hsa-miR-361-5p, hsa-miR-1283, hsa-miR-18a*, hsa-miR-26b, hsa-miR-604, hsa-miR-423-3p, hsa-miR-93*, hsa-miR-29a, hsa-miR-1248, hsa-miR-210, hsa-miR-19b, hsa-miR-453, hsa-miR-126*, hsa-miR-188-3p, hsa-miR-624*, hsa-miR-505*, hsa-miR-425, hsa-miR-339-3p, hsa-miR-668, and hsa-miR-363*.

[0159] FIG. 14:

[0160] Classification of cancer samples versus controls for two individual miRNAs (miR-126 and miR-196). Blue bars correspond to cancer samples, while red bars correspond to controls.

[0161] FIG. 15:

[0162] Scatterplot of fold quotients of rt-qPCR (x-axis) and microarray experiments (y-axis).

[0163] FIG. 16:

[0164] The mutual information of all miRNAs that have higher information content than the best permutation test (upper red line). The middle red line denotes the 95% quantile of the 1000 permutation tests and the bottom red line the mean of the permutation experiments, corresponding to the background MI.

[0165] FIG. 17:

[0166] Box plots of the classification accuracy, specificity and sensitivity of the set of 24 best miRNAs (obtained with radial basis function support vector machine). These miRNAs allow for the discrimination between blood cells of lung cancer patients and blood cells of controls with an accuracy of 95.4% [94.9%-95.9%], a specificity of 98.1% [97.3%-98.8%], and a sensitivity of 92.5% [91.8%-92.5%]. The permutation tests showed significantly decreased accuracy, specificity and sensitivity with 94.2% [47.2%-51.3%], 56.9% [54.5%-59.3%] and 40.6% [37.9%-43.4%], respectively, providing evidence that the obtained results are not due to an overfit of the statistical model on the miRNA fingerprints.

EXAMPLE 1

[0167] Lung Cancer

[0168] 1. Material and Methods

[0169] 1.1 Samples

[0170] Blood samples were obtained with patients' informed consent. The patient samples stem from 17 patients with non-small cell lung carcinoma and normal controls. Normal samples were obtained from 19 different volunteers. More detailed information of patients and controls is given in Table 1.

TABLE-US-00001 TABLE 1 Detailed information on lung cancer patients and healthy control subjects blood donors male female lung cancer patients number 9 8 average age 67.4 60.6 squamous cell lung cancer 3 4 adenocarcinoma 6 1 adenosquamous carcinoma 0 1 broncholaveolar carcinoma 0 1 typical carcinoid 0 1 healthy subjects number 7 12 average age 43.3 36.7 blood donors male female lung cancer patients number 9 8 average age 67.4 60.6 squamous cell lung cancer 3 4 adenocarcinoma 6 1 adenosquamous carcinoma 0 1 broncholaveolar carcinoma 0 1 typical carcinoid 0 1 healthy subjects number 7 12 average age 43.3 36.7 blood donors male female lung cancer patients number 9 8 average age 67.4 60.6 squamous cell lung cancer 3 4 adenocarcinoma 6 1 adenosquamous carcinoma 0 1 broncholaveolar carcinoma 0 1 typical carcinoid 0 1 healthy subjects number 7 12 average age 43.3 36.7

[0171] 1.2 miRNA Microarray Screening

[0172] Blood of lung cancer patients and volunteers without known disease was extracted in PAXgene Blood RNA tubes (BD, Franklin Lakes, N.J. USA). For each blood donor, 5 ml of peripheral blood were obtained. Total RNA was extracted from blood cells using the miRNeasy Mini Kit (Qiagen GmbH, Hilden, Germany) and the RNA has been stored at -70.degree. C. Samples were analyzed with the Geniom Realtime Analyzer (GRTA, febit gmbh, Heidelberg, Germany) using the Geniom Biochip miRNA homo sapiens. Each array contains 7 replicates of 866 miRNAs and miRNA star sequences as annotated in the Sanger mirBase 12.0 (Griffiths-Jones, Moxon et al. 2005; Griffiths-Jones, Saini et al. 2008). Sample labeling with Biotin has been carried out either by using the miRVANA.TM. miRNA Labeling Kit (Applied Biosystems Inc, Foster City, Calif. USA) or by multifluidic-based enzymatic on-chip labeling of miRNAs (MPEA (Vorwerk, Ganter et al. 2008), incorporated herein by reference).

[0173] Following hybridization for 16 hours at 42.degree. C. the biochip was washed automatically and a program for signal enhancement was processed with the GRTA. The resulting detection pictures were evaluated using the Geniom Wizard Software. For each array, the median signal intensity was extracted from the raw data file such that for each miRNA seven intensity values have been calculated corresponding to each replicate copy of mirBase on the array. Following background correction, the seven replicate intensity values of each miRNA were summarized by their median value. To normalize the data across different arrays, quantile normalization (Bolstad, Irizarry et al. 2003) was applied and all further analyses were carried out using the normalized and background subtracted intensity values.

[0174] 1.3 Statistical Analysis

[0175] After having verified the normal distribution of the measured data, parametric t-tests (unpaired, two-tailed) were carried out for each miRNA separately, to detect miRNAs that show a different behavior in different groups of blood donors. The resulting p-values were adjusted for multiple testing by Benjamini-Hochberg (Hochberg 1988; Benjamini and Hochberg 1995) adjustment. Moreover, the Mutual Information (MI) (Shannon 1984) was computed as a measure to access the diagnostic value of single miRNA biomarkers. To this end, all biomarkers were transformed to z-scores and binned in three bins before the MI values of each biomarker, and the information whether the marker has been measured from a normal or lung cancer sample, was computed. In addition to the single biomarker analysis classification of samples using miRNA patterns was carried out using Support Vector Machines (SVM, (Vapnik 2000)) as implemented in the R (Team 2008) e1071 package. In detail, different kernel (linear, polynomial, sigmoid, radial basis function) Support Vector Machines were evaluated, where the cost parameter was sampled from 0.01 to 10 in decimal powers. The measured miRNA profiles were classified using 100 repetitions of standard 10-fold cross-validation. As a subset selection technique a filter approach based on t-test was applied . In detail, the s miRNAs with lowest p-values were computed on the training set in each fold of the cross validation, where s was sampled from 1 to 866. The respective subset was used to train the SVM and to carry out the prediction of the test samples. As result, the mean accuracy, specificity, and sensitivity were calculated together with the 95% Confidence Intervals (95% CI) for each subset size. To check for overtraining permutation tests were applied. Here the class labels were sampled randomly and classifications were carried out using the permuted class labels. All statistical analyzes were performed using R (Team 2008).

[0176] 2. Results

[0177] 2.1 miRNA Experiments

[0178] The expression of 866 miRNAs and miRNA star sequences was analyzed in blood cells of 17 patients with NSCLC. As a control blood cells of 19 volunteers without known disease were used (see also Materials and Methods).

[0179] Following RNA isolation and labeling by miRVANA.TM. miRNA Labeling Kit, the miRNA expression profiles were measured by the Geniom Bioship miRNA homo sapiens in the GRTA (febit gmbh, Heidelberg). Following intensity value computation and quantile normalization of the miRNA profiles (Bolstad, Irizarry et al. 2003), a mean correlation value of 0.97 for technical replicates was determined by using purchased total RNA from Ambion (four heart and four liver replicates). For the biological replicates the different tumor samples were compared between each other and the different normal samples between each other. The biological replicates showed a mean correlation of 0.87 and a variance of 0.009.

[0180] 2.2 Ruling Out the Influence of Age and Gender

[0181] To cross-check that age and gender do not have an influence on our analysis, t-tests were computed for the normal samples. In the case of males versus females there was no statistically significant deregulated miRNA. The most significant miRNA, hsa-miR-423, showed an adjusted significance level of 0.78.

[0182] To test for the influence of donor age the profiles obtained from samples obtained from the oldest versus youngest patients were compared by splitting the group in half based on age. Here, the most significant miRNA, miR-890, obtained an adjusted p-value of 0.87. As for gender, there were no deregulated miRNAs, thus providing evidence that age and gender do not have a substantial influence on the miRNA profiles.

[0183] 2.3 Single Deregulated miRNAs

[0184] Hypothesis testing was applied to identify miRNAs deregulated in the blood cells of lung cancer patients as compared to the blood cells of the controls. Following verification of an approximately normal distribution, two-tailed unpaired t-tests were performed for each miRNA. The respective p-values were adjusted for multiple testing by the Benjamini-Hochberg approach (Hochberg 1988; Benjamini and Hochberg 1995). In total 27 miRNAs significantly deregulated in blood cells of lung cancer patients as compared to the controls were detected. A complete list of deregulated miRNAs is given in the tables in FIGS. 10 and 11. The miRNAs that were most significantly deregulated included hsa-miR-126 with a p-value of 0.00003, hsa-let-7d with a p-value of 0.003, hsa-let-7i with a p-value of 0.003, and hsa-miR-423 with a p-value of 0.001 (FIG. 1 and FIG. 2). Other members of the let-7 family that were also found to be deregulated included hsa-let-7c, hsa-let-7e, hsa-let-7f, hsa-let-7g and hsa-let-7a. Besides miR-423, all above mentioned miRNAs were down-regulated in blood cells of lung cancer patients compared to blood cells of healthy subjects indicating an overall decreased miRNA repertoire.

[0185] To validate the findings, the miRNA profiling was repeated using an enzymatic on-chip labeling technique termed MPEA (Microfluidic-based enzymatic on-chip labeling of miRNAs). For this control experiment, 4 out of the 17 lung cancer patients and 10 of the controls were used. Hereby, 100 differentially regulated miRNAs were detected. The miRNAs that were most significantly deregulated include hsa-miR-1253 with a p-value of 0.001, hsa-miR-126 with a p-value of 0.006, hsa-let-7d with a p-value of 0.006, and hsa-let-7f with a p-value of 0.006. Of the previously identified 27 miRNAs 12 were detected to be significant in the second experiment, while the remaining miRNAs showed increased p-values. The correlation of fold changes was 0.62. Also other members of the let-7 family were confirmed as deregulated in blood cells of lung cancer patients. Furthermore, it was confirmed that the majority of the deregulated miRNAs were down-regulated in patients' blood samples. Here, 62% of the deregulated miRNAs showed decreased intensity values in lung cancer samples.

[0186] As a further control experiment an expression analysis by qRT-PCR was performed. As a test sample the fold changes of has-miR-106b, miR-98, miR-140-3p, let-7d, mir-126, and miR-22 were analyzed in blood cells of eight tumor patients and five controls. The fold quotients detected by the Geniom Biochip experiments agreed very well with the qRT-PCR experiments, as demonstrated by an excellent R.sup.2 value of 0.994. The fold quotients are presented as a scatterplot together with the R.sup.2 value and the regression line in FIG. 16.

[0187] 2.4 Diagnostic Value of miRNA Biomarkers

[0188] Mutual Information (MI) (Shannon 1984) is an adequate measure to estimate the overall diagnostic information content of single biomarkers (Keller, Ludwig et al. 2006). In the present study, Mutual Information is considered as the reduction in uncertainty about the class labels `0` for controls and `1` for tumor samples due to the knowledge of the miRNA expression. The higher the value of the MI of a miRNA, the higher is the diagnostic content of the respective miRNA.

[0189] The MI of each miRNA with the class labels was computed. First, a permutation test was carried out to determine the background noise of the miRNAs, e.g. the random information content of each miRNA. 1000 miRNAs (with replacements) were randomly selected and the class labels were sampled for each miRNA. These permutation tests yielded a mean MI value of 0.029, a 95% quantile of 0.096 and a value of 0.217 for the highest random MI. Second, the MI values were calculated for the comparison between the miRNAs in blood cells of tumor patients and controls. The overall comparison of the 866 miRNAs yielded significantly increased MI values with a two-tailed p-value of .ltoreq.10.sup.-10 as shown by an unpaired Wilcoxon Mann-Whitney test (Wilcoxon 1945; Mann and Wilcoxon 1947). The miRNA hsa-miR-361-5p showed the highest MI with a value of 0.446. The miRNAs with the best significance values as computed by the t-test, namely hsa-miR-126 and hsa-miR-98, were also among the miRNAs showing the highest MI values. In total 37 miRNAs with MI values higher than the highest of 1000 permuted miRNAs and 200 miRNAs with MI values higher than the 95% quantile were detected (FIG. 16). A complete list of miRNAs, the respective MI and the enrichment compared to the background MI is provided in the table in FIG. 10.

[0190] 2.5 Evaluating Complex Fingerprints

[0191] Even single miRNAs with highest MI values are not sufficient to differentiate between blood cells of tumor patients as compared to controls with high specificity. For example, the has-miR-126 separates blood cells of tumor patients from blood cells of healthy individuals with a specificity of 68%, only. In order to improve the classification accuracy the predictive power of multiple miRNAs was combined by using statistical learning techniques. In detail, Support Vector Machines with different kernels (linear, polynomial, sigmoid, radial basis function) were applied to the data and a hypothesis test was carried out based subset selection as described in Material and Methods. To gain statistical significance 100 repetitions of 10-fold cross validation were carried out. Likewise, 100 repetitions for the permutation tests were computed.

[0192] The best results were obtained with radial basis function Support Vector Machines and a subset of 24 miRNAs. These miRNAs allowed for the discrimination between blood cells of lung tumor patients and blood cells of controls with an accuracy of 95.4% [94.9%-95.9%], a specificity of 98.1% [97.3%-98.8%], and a sensitivity of 92.5% [91.8%-92.5%]. The permutation tests showed significantly decreased accuracy, specificity, and sensitivity with 49.2% [47.2%-51.3%], 56.9% [54.5%-59.3%] and 40.6% [37.9%-43.4%], respectively (FIG. 5), providing evidence that the obtained results are not due to an overfit of the statistical model on the miRNA fingerprints.

[0193] 3. Discussion

[0194] While complex miRNA expression patterns have been reported for a huge variety of human tumors, information there was only one study analyzing miRNA expression in blood cells derived from tumor patients. In the following the present miRNA expression profiling is related to both the miRNA expression in blood cells and in cancer cells of non-small cell lung cancer patients. A significant down-regulation of has-miR-126 was found that was recently detected in blood cells of healthy individuals, but not in blood cells of lung cancer patients (Chen, Ba et al. 2008). Down-regulation of has-miR-126 was also found in lung cancer tissue in this study. Functional studies on has-miR-126 revealed this miRNA as a regulator of the endothelial expression of vascular cell adhesion molecule 1 (VCAM-1), which is an intercellular adhesion molecule expressed by endothelial cells focuses on the identification of miRNAs in serum of patients with cancer and other diseases or healthy controls. Since most miRNAs are expressed in both, serum and blood cells of healthy controls, most serum miRNAs are likely derived from circulating blood cells. Since there was only a weak correlation between the miRNA expression in serum and blood cell, miRNA expression appears to be deregulated in either serum or blood cells of cancer patients. The present experimental example focused on the analysis of miRNA expression in blood cells of non-small cell lung cancer patients and healthy controls. Significant downregulation of has-miR-126 was found that was recently detected in blood cells of healthy individuals, but not in blood cells of lung cancer patients (Harris, YamakuchiChen, Ba et al. 2008). Downregulation of has-miR-126 was also found in lung cancer tissue (Yanaihara, Caplen et al. 2006). Functional studies on has-miR-126 revealed this miRNA as regulator of the endothelial expression of vascular cell adhesion molecule 1 (VCAM-1), which is an intercellular adhesion molecule expressed by endothelial cells (Harris, Yamakuchi et al. 2008). hsa-miR-126 is also reported to be an inhibitor of cell invasion in non-small cell lung cancer cell lines, and down-regulation of this miRNA 126 might be a mechanism of lung cancer cells to evade these inhibitory effects (Crawford, Brawner et al. 2008). Members of the has-let-7 family that were found down-regulated in the present invention were the first miRNAs reported as de-regulated in lung cancer (Johnson, Grosshans et al. 2005). This down-regulation of the let-7 family in lung cancer was confirmed by several independent studies (Takamizawa, Konishi et al. 2004; Stahlhut Espinosa and Slack 2006; Tong 2006; Zhang, Wang et al. 2007; Williams 2008). The present data are also in agreement with a recent study showing the down-regulation of has-let-7a, has-let-7d, has-let-7f, has-let-7g, and has-let-7i in blood cells of lung cancer patients (Chen, Ba et al. 2008). Notably, down-regulation of let-7 in lung cancer was strongly associated with poor clinical outcome (Takamizawa, Konishi et al. 2004). The let-7 family members negatively regulate oncogene RAS (Johnson, Grosshans et al. 2005). The miRNA has-miR-22 that showed a high MI value and up-regulation in the present study, was recently also reported to be up-regulated in blood cells of lung cancer patients (Chen, Ba et al. 2008). The miRNA has-miR-19a that also showed a high MI value and up-regulation in the present study was reported to be up-regulated in lung cancer tissue (Hayashita, Osada et al. 2005; Calin and Croce 2006). In contrast, has-miR-20a, which is significantly down-regulated in the present experiments, was reported as up-regulated in lung cancer tissue (Hayashita, Osada et al. 2005; Calin and Croce 2006). The up-regulation of has-miR-20a was found in small-cell lung cancer cell lines, the present study investigated only NSCLC. In summary, there is a high degree of consistency between miRNA expression found in the peripheral blood cells of lung cancer patients and miRNA expression in lung cancer tissue (Takamizawa, Konishi et al. 2004; Hayashita, Osada et al. 2005; Lu, Getz et al. 2005; Calin and Croce 2006; Stahlhut Espinosa and Slack 2006; Tong 2006; Volinia, Calin et al. 2006; Yanaihara, Caplen et al. 2006; Zhang, Wang et al. 2007; Williams 2008).

[0195] Some of the deregulated miRNAs identified in the present invention are also reported as de-regulated in other cancer entities, e.g. has-miR-346 in gastritic cancer, has-miR-145 in bladder cancer, and has-miR-19a in hepatocellular carcinoma and B-cell leukemia (Alvarez-Garcia and Miska 2005; He, Thomson et al. 2005; Feitelson and Lee 2007; Guo, Huang et al. 2008; Ichimi, Enokida et al. 2009). In addition, miRNAs with high diagnostic potential e.g. high MI value, were found that were not yet related to cancer as for example has-miR-527 or has-mir-361-5p that were both up-regulated in blood cells of lung cancer patients.

[0196] Besides the deregulation of single miRNAs, the overall expression pattern of miRNAs in peripheral blood cells of lung cancer patients were analyzed in comparison to the pattern in blood cells of healthy controls. Recently, Chen et al. (Chen, Ba et al. 2008) reported a high correlation of 0.9205 between miRNA profiles in serum and miRNA profiles in blood cells, both in healthy individuals. The correlation of the miRNA profiles between serum and blood cells in lung cancer patients were significantly lower (0.4492). These results are indicative of deregulated miRNAs in blood and/or serum of patients and are in agreement with the present data that show the deregulation of miRNAs in the blood cells of lung carcinoma patients. These deregulated miRNAs can be used to differentiate patients with lung cancer from normal controls with high specificity and sensitivity. This is the first evidence for the diagnostic potential of miRNA expression profiles in peripheral blood cells of cancer patients and healthy individuals.

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[0232] Vorwerk, S., K. Ganter, et al. (2008). "Microfluidic-based enzymatic on-chip labeling of miRNAs." N Biotechnol 25(2-3): 142-9.

[0233] Wilcoxon, F. (1945). "Individual comparisons by ranking methods." Biometric Bull 1: 80-83.

[0234] Williams, A. E. (2008). "Functional aspects of animal microRNAs." Cell Mol Life Sci 65(4): 545-62.

[0235] Yanaihara, N., N. Caplen, et al. (2006). "Unique microRNA molecular profiles in lung cancer diagnosis and prognosis." Cancer Cell 9(3): 189-98.

[0236] Zhang, B., X. Pan, et al. (2007). "microRNAs as oncogenes and tumor suppressors." Dev Biol 302(1): 1-12.

[0237] Zhang, B., Q. Wang, et al. (2007). "MicroRNAs and their regulatory roles in animals and plants." J Cell Physiol 210(2): 279-89.

Sequence CWU 1

1

866122RNAHomo sapiens 1ucguaccgugaguaauaaugcg 22223RNAHomo sapiens 2ugaggggcagagagcgagacuuu 23322RNAHomo sapiens 3ugagguaguaguuugugcuguu 22422RNAHomo sapiens 4agagguaguagguugcauaguu 22522RNAHomo sapiens 5aagcugccaguugaagaacugu 22622RNAHomo sapiens 6uagcagcacauaaugguuugug 22722RNAHomo sapiens 7ugagguaguaaguuguauuguu 22823RNAHomo sapiens 8ugugcaaaucuaugcaaaacuga 23923RNAHomo sapiens 9ugagugugugugugugagugugu 231020RNAHomo sapiens 10acugccccaggugcugcugg 201123RNAHomo sapiens 11caaagugcucauagugcagguag 231222RNAHomo sapiens 12cauugcacuugucucggucuga 221321RNAHomo sapiens 13uagcagcacagaaauauuggc 211422RNAHomo sapiens 14ugagguaggagguuguauaguu 221522RNAHomo sapiens 15ugagguaguagguuguaugguu 221622RNAHomo sapiens 16ugagguaguagauuguauaguu 221722RNAHomo sapiens 17ugagguaguagguuguauaguu 221822RNAHomo sapiens 18ugagguaguaguuuguacaguu 221921RNAHomo sapiens 19uaccacaggguagaaccacgg 212023RNAHomo sapiens 20ucccuguccuccaggagcucacg 232122RNAHomo sapiens 21uuaucagaaucuccagggguac 222222RNAHomo sapiens 22ucuacaaaggaaagcgcuuucu 222323RNAHomo sapiens 23acugcccuaagugcuccuucugg 232421RNAHomo sapiens 24uucaaguaauucaggauaggu 212519RNAHomo sapiens 25aggcugcggaauucaggac 192623RNAHomo sapiens 26agcucggucugaggccccucagu 232722RNAHomo sapiens 27acugcugagcuagcacuucccg 222822RNAHomo sapiens 28uagcaccaucugaaaucgguua 222927RNAHomo sapiens 29accuucuuguauaagcacugugcuaaa 273022RNAHomo sapiens 30cugugcgugugacagcggcuga 223123RNAHomo sapiens 31ugugcaaauccaugcaaaacuga 233223RNAHomo sapiens 32agguuguccguggugaguucgca 233321RNAHomo sapiens 33cauuauuacuuuugguacgcg 213421RNAHomo sapiens 34cucccacaugcaggguuugca 213522RNAHomo sapiens 35uaguaccaguaccuuguguuca 223622RNAHomo sapiens 36gggagccaggaaguauugaugu 223723RNAHomo sapiens 37aaugacacgaucacucccguuga 233823RNAHomo sapiens 38ugagcgccucgacgacagagccg 233923RNAHomo sapiens 39ugucacucggcucggcccacuac 234022RNAHomo sapiens 40cggguggaucacgaugcaauuu 224122RNAHomo sapiens 41cgaaucauuauuugcugcucua 224222RNAHomo sapiens 42ugaccgauuucuccugguguuc 224322RNAHomo sapiens 43ugucuuacucccucaggcacau 224422RNAHomo sapiens 44aaucacuaaccacacggccagg 224523RNAHomo sapiens 45uaaagugcuuauagugcagguag 234622RNAHomo sapiens 46uuauaauacaaccugauaagug 224722RNAHomo sapiens 47ggauuccuggaaauacuguucu 224823RNAHomo sapiens 48uaagugcuuccauguuuuaguag 234923RNAHomo sapiens 49aaaagugcuuacagugcagguag 235022RNAHomo sapiens 50uguaaacauccuugacuggaag 225122RNAHomo sapiens 51ugucaguuugucaaauacccca 225222RNAHomo sapiens 52cuggacugagccgugcuacugg 225322RNAHomo sapiens 53ugagguaguagguugugugguu 225422RNAHomo sapiens 54ugugacagauugauaacugaaa 225518RNAHomo sapiens 55ugcuuccuuucagagggu 185622RNAHomo sapiens 56aaaccguuaccauuacugaguu 225722RNAHomo sapiens 57aaagugcaucuuuuuagaggau 225826RNAHomo sapiens 58aaguaguugguuuguaugagaugguu 265923RNAHomo sapiens 59gacacgggcgacagcugcggccc 236023RNAHomo sapiens 60ucccccaggugugauucugauuu 236122RNAHomo sapiens 61aguuuugcauaguugcacuaca 226222RNAHomo sapiens 62aucaugaugggcuccucggugu 226322RNAHomo sapiens 63cuccugagccauucugagccuc 226422RNAHomo sapiens 64aaaaugguucccuuuagagugu 226522RNAHomo sapiens 65aagugcuuccuuuuagaggguu 226622RNAHomo sapiens 66cucuagagggaagcgcuuucug 226721RNAHomo sapiens 67cugaccuaugaauugacagcc 216821RNAHomo sapiens 68aagugaucuaaaggccuacau 216921RNAHomo sapiens 69ucgaggagcucacagucuagu 217021RNAHomo sapiens 70uggacugcccugaucuggaga 217123RNAHomo sapiens 71uagcagcgggaacaguucugcag 237219RNAHomo sapiens 72agguugacauacguuuccc 197322RNAHomo sapiens 73gguggcccggccgugccugagg 227422RNAHomo sapiens 74cuauacgaccugcugccuuucu 227523RNAHomo sapiens 75cccaguguucagacuaccuguuc 237617RNAHomo sapiens 76ucucgcuggggccucca 177719RNAHomo sapiens 77aauggauuuuuggagcagg 197822RNAHomo sapiens 78aacaauauccuggugcugagug 227921RNAHomo sapiens 79auguaugugugcaugugcaug 218019RNAHomo sapiens 80auggauaaggcuuuggcuu 198123RNAHomo sapiens 81gcagcagagaauaggacuacguc 238222RNAHomo sapiens 82uggagagaaaggcaguuccuga 228323RNAHomo sapiens 83gcgaggaccccucggggucugac 238426RNAHomo sapiens 84gaugaugauggcagcaaauucugaaa 268522RNAHomo sapiens 85uucuggaauucugugugaggga 228622RNAHomo sapiens 86uuuuucauuauugcuccugacc 228721RNAHomo sapiens 87cagcagcacacugugguuugu 218818RNAHomo sapiens 88ucagcuggcccucauuuc 188922RNAHomo sapiens 89uagcagcacguaaauauuggcg 229022RNAHomo sapiens 90uggaauguaaagaaguauguau 229124RNAHomo sapiens 91uggcccugacugaagaccagcagu 249222RNAHomo sapiens 92gcuauuucacgacaccaggguu 229323RNAHomo sapiens 93acuccauuuguuuugaugaugga 239422RNAHomo sapiens 94caaaaaccacaguuucuuuugc 229522RNAHomo sapiens 95caaaguuuaagauccuugaagu 229622RNAHomo sapiens 96aaaaguaaucgcgguuuuuguc 229721RNAHomo sapiens 97gccccugggccuauccuagaa 219822RNAHomo sapiens 98gcccaaaggugaauuuuuuggg 229923RNAHomo sapiens 99guccaguuuucccaggaaucccu 2310023RNAHomo sapiens 100caaagugcuuacagugcagguag 2310122RNAHomo sapiens 101uguaaacauccuacacucagcu 2210222RNAHomo sapiens 102cuauacaaucuauugccuuccc 2210322RNAHomo sapiens 103uuuucaacucuaaugggagaga 2210421RNAHomo sapiens 104cuuuuugcggucugggcuugc 2110521RNAHomo sapiens 105ucguggccuggucuccauuau 2110622RNAHomo sapiens 106ccgcacuguggguacuugcugc 2210724RNAHomo sapiens 107gaccuggacauguuugugcccagu 2410822RNAHomo sapiens 108caaucagcaaguauacugcccu 2210921RNAHomo sapiens 109aauggcgccacuaggguugug 2111022RNAHomo sapiens 110aggcauugacuucucacuagcu 2211122RNAHomo sapiens 111acuguaguaugggcacuuccag 2211221RNAHomo sapiens 112caaaacgugaggcgcugcuau 2111322RNAHomo sapiens 113aucgugcaucccuuuagagugu 2211422RNAHomo sapiens 114ucuauacagacccuggcuuuuc 2211522RNAHomo sapiens 115acaguagucugcacauugguua 2211620RNAHomo sapiens 116guugugucaguuuaucaaac 2011721RNAHomo sapiens 117uaguagaccguauagcguacg 2111821RNAHomo sapiens 118aucacauugccagggauuacc 2111921RNAHomo sapiens 119uugggacauacuuaugcuaaa 2112022RNAHomo sapiens 120uggcaguguauuguuagcuggu 2212119RNAHomo sapiens 121aaaaacuguaauuacuuuu 1912222RNAHomo sapiens 122ugugucacucgaugaccacugu 2212322RNAHomo sapiens 123cacacacugcaauuacuuuugc 2212422RNAHomo sapiens 124acccgucccguucguccccgga 2212521RNAHomo sapiens 125uccugcgcgucccagaugccc 2112623RNAHomo sapiens 126ugaguaccgccaugucuguuggg 2312723RNAHomo sapiens 127ccuaguagguguccaguaagugu 2312823RNAHomo sapiens 128agguuacccgagcaacuuugcau 2312924RNAHomo sapiens 129uuuggcaaugguagaacucacacu 2413025RNAHomo sapiens 130ggcggagggaaguagguccguuggu 2513121RNAHomo sapiens 131augaccuaugaauugacagac 2113222RNAHomo sapiens 132gugugcggaaaugcuucugcua 2213322RNAHomo sapiens 133uguaaacauccccgacuggaag 2213422RNAHomo sapiens 134cuccugacuccagguccugugu 2213522RNAHomo sapiens 135accuggcauacaauguagauuu 2213622RNAHomo sapiens 136caaucacuaacuccacugccau 2213725RNAHomo sapiens 137aggcaccagccaggcauugcucagc 2513821RNAHomo sapiens 138aacaggugacugguuagacaa 2113921RNAHomo sapiens 139acuggacuuggagucagaagg 2114022RNAHomo sapiens 140ggugcagugcugcaucucuggu 2214123RNAHomo sapiens 141ccucagggcuguagaacagggcu 2314221RNAHomo sapiens 142gcuaguccugacucagccagu 2114321RNAHomo sapiens 143agaccuggcccagaccucagc 2114420RNAHomo sapiens 144aggguguuucucucaucucu 2014523RNAHomo sapiens 145uguaaacauccuacacucucagc 2314622RNAHomo sapiens 146aaggagcucacagucuauugag 2214721RNAHomo sapiens 147aucacauugccagggauuucc 2114819RNAHomo sapiens 148ucuaggcugguacugcuga 1914921RNAHomo sapiens 149guggcugcacucacuuccuuc 2115022RNAHomo sapiens 150acuuuaacauggaagugcuuuc 2215121RNAHomo sapiens 151guucaaauccagaucuauaac 2115223RNAHomo sapiens 152uggaguccaggaaucugcauuuu 2315324RNAHomo sapiens 153ccagacagaauucuaugcacuuuc 2415423RNAHomo sapiens 154uaaauuucaccuuucugagaagg 2315524RNAHomo sapiens 155uggggagcugaggcucugggggug 2415623RNAHomo sapiens 156uagcaccauuugaaaucaguguu 2315720RNAHomo sapiens 157accaggaggcugaggccccu 2015823RNAHomo sapiens 158uaaggugcaucuagugcagauag 2315919RNAHomo sapiens 159gugaggacucgggaggugg 1916022RNAHomo sapiens 160caaauucguaucuaggggaaua 2216122RNAHomo sapiens 161accaucgaccguugauuguacc 2216222RNAHomo sapiens 162caugguucugucaagcaccgcg 2216323RNAHomo sapiens 163aagugccgccaucuuuugagugu 2316422RNAHomo sapiens 164aucacacaaaggcaacuuuugu 2216522RNAHomo sapiens 165cagugguuuuacccuaugguag 2216623RNAHomo sapiens 166cagugcaauaguauugucaaagc 2316722RNAHomo sapiens 167uacguagauauauauguauuuu 2216822RNAHomo sapiens 168uucacauugugcuacugucugc 2216922RNAHomo sapiens 169uacccagagcaugcagugugaa 2217022RNAHomo sapiens 170aacuggcccucaaagucccgcu 2217122RNAHomo sapiens 171ugccugucuacacuugcugugc 2217222RNAHomo sapiens 172aaaucucugcaggcaaauguga 2217317RNAHomo sapiens 173uaauugcuuccauguuu 1717422RNAHomo sapiens 174cucuagagggaagcgcuuucug 2217522RNAHomo sapiens 175aaaaguaauugcggucuuuggu 2217620RNAHomo sapiens 176auguauaaauguauacacac 2017719RNAHomo sapiens 177aagugugcagggcacuggu 1917821RNAHomo sapiens 178ucccacguuguggcccagcag 2117921RNAHomo sapiens 179agcuacaucuggcuacugggu 2118022RNAHomo sapiens 180ugcuggaucagugguucgaguc 2218122RNAHomo sapiens 181ugcaacuuaccugagucauuga 2218223RNAHomo sapiens 182ucucacacagaaaucgcacccgu 2318322RNAHomo sapiens 183aagugcugucauagcugagguc 2218423RNAHomo sapiens 184aucccuugcaggggcuguugggu 2318522RNAHomo sapiens 185auaagacgaacaaaagguuugu 2218622RNAHomo sapiens 186ccaguauuaacugugcugcuga 2218721RNAHomo sapiens 187gcgacccauacuugguuucag 2118822RNAHomo sapiens 188ugcccuguggacucaguucugg 2218921RNAHomo sapiens

189aaagugcuuccuuuuagaggg 2119022RNAHomo sapiens 190aggcaguguauuguuagcuggc 2219124RNAHomo sapiens 191acaaagugcuucccuuuagagugu 2419222RNAHomo sapiens 192ugccuacugagcugaaacacag 2219321RNAHomo sapiens 193gaaagcgcuucucuuuagagg 2119422RNAHomo sapiens 194aaaccuguguuguucaagaguc 2219522RNAHomo sapiens 195uauugcacauuacuaaguugca 2219621RNAHomo sapiens 196cuagacugaagcuccuugagg 2119723RNAHomo sapiens 197uagugcaauauugcuuauagggu 2319821RNAHomo sapiens 198uacaguacugugauaacugaa 2119923RNAHomo sapiens 199aguuuugcagguuugcauccagc 2320021RNAHomo sapiens 200uacugcagacaguggcaauca 2120120RNAHomo sapiens 201uacaguauagaugauguacu 2020223RNAHomo sapiens 202uacuccagagggcgucacucaug 2320321RNAHomo sapiens 203aguuaaugaauccuggaaagu 2120423RNAHomo sapiens 204ugugcuugcucgucccgcccgca 2320522RNAHomo sapiens 205auccgcgcucugacucucugcc 2220623RNAHomo sapiens 206ugucugcccgcaugccugccucu 2320721RNAHomo sapiens 207uaaggcacccuucugaguaga 2120822RNAHomo sapiens 208uauguaacaugguccacuaacu 2220923RNAHomo sapiens 209ccugcagcgacuugauggcuucc 2321023RNAHomo sapiens 210uucauuugguauaaaccgcgauu 2321122RNAHomo sapiens 211uggguuccuggcaugcugauuu 2221222RNAHomo sapiens 212augggugaauuuguagaaggau 2221322RNAHomo sapiens 213uugcauagucacaaaagugauc 2221421RNAHomo sapiens 214aaagugcuuccuuuuugaggg 2121519RNAHomo sapiens 215gugucugcuuccuguggga 1921622RNAHomo sapiens 216cugcaauguaagcacuucuuac 2221722RNAHomo sapiens 217ugcuaugccaacauauugccau 2221822RNAHomo sapiens 218cagugccucggcagugcagccc 2221922RNAHomo sapiens 219uaugucugcugaccaucaccuu 2222022RNAHomo sapiens 220caagcucgugucuguggguccg 2222122RNAHomo sapiens 221uaguacugugcauaucaucuau 2222223RNAHomo sapiens 222uauggcuuuucauuccuauguga 2322322RNAHomo sapiens 223uagaguuacacccugggaguua 2222421RNAHomo sapiens 224cuccguuugccuguuucgcug 2122522RNAHomo sapiens 225cuuagcagguuguauuaucauu 2222621RNAHomo sapiens 226auugacacuucugugaguaga 2122721RNAHomo sapiens 227uaauuuuauguauaagcuagu 2122821RNAHomo sapiens 228aaacuacugaaaaucaaagau 2122921RNAHomo sapiens 229aauaauacaugguugaucuuu 2123021RNAHomo sapiens 230uugaaaggcuauuucuugguc 2123121RNAHomo sapiens 231ucacagugaaccggucucuuu 2123224RNAHomo sapiens 232aauccuuggaaccuaggugugagu 2423323RNAHomo sapiens 233aggaagcccuggaggggcuggag 2323422RNAHomo sapiens 234cugcccuggcccgagggaccga 2223520RNAHomo sapiens 235caccaggcauuguggucucc 2023625RNAHomo sapiens 236ugggaacggguuccggcagacgcug 2523722RNAHomo sapiens 237uguaacagcaacuccaugugga 2223822RNAHomo sapiens 238uagcagcacaucaugguuuaca 2223921RNAHomo sapiens 239aggggugcuaucugugauuga 2124022RNAHomo sapiens 240ucacaagucaggcucuugggac 2224117RNAHomo sapiens 241uucaaguaauucaggug 1724222RNAHomo sapiens 242ugugcgcagggagaccucuccc 2224322RNAHomo sapiens 243uuguacaugguaggcuuucauu 2224423RNAHomo sapiens 244ucaaaugcucagacuccuguggu 2324522RNAHomo sapiens 245uaacacugucugguaaagaugg 2224622RNAHomo sapiens 246aaccaucgaccguugaguggac 2224722RNAHomo sapiens 247aacuggccuacaaagucccagu 2224823RNAHomo sapiens 248uaagugcuuccauguuucagugg 2324922RNAHomo sapiens 249agaggcuggccgugaugaauuc 2225022RNAHomo sapiens 250aacaucacagcaagucugugcu 2225122RNAHomo sapiens 251ucagcaaacauuuauugugugc 2225222RNAHomo sapiens 252aaaaguaauugugguuuuggcc 2225321RNAHomo sapiens 253ugacaacuauggaugagcucu 2125422RNAHomo sapiens 254auucuaauuucuccacgucuuu 2225521RNAHomo sapiens 255uagauaaaauauugguaccug 2125621RNAHomo sapiens 256caaagaggaaggucccauuac 2125721RNAHomo sapiens 257uuuccauaggugaugagucac 2125821RNAHomo sapiens 258cacaagguauugguauuaccu 2125919RNAHomo sapiens 259aagcagcugccucugaggc 1926022RNAHomo sapiens 260auaauacaugguuaaccucuuu 2226122RNAHomo sapiens 261uccauuacacuacccugccucu 2226222RNAHomo sapiens 262ccaguggggcugcuguuaucug 2226322RNAHomo sapiens 263ugguuuaccgucccacauacau 2226422RNAHomo sapiens 264cuccuauaugaugccuuucuuc 2226522RNAHomo sapiens 265ugaaggucuacugugugccagg 2226622RNAHomo sapiens 266caaaccacacugugguguuaga 2226722RNAHomo sapiens 267aaagugcauccuuuuagagugu 2226822RNAHomo sapiens 268caagcucgcuucuaugggucug 2226917RNAHomo sapiens 269ucccaccgcugccaccc 1727022RNAHomo sapiens 270cucuagagggaagcgcuuucug 2227122RNAHomo sapiens 271gguccagaggggagauagguuc 2227222RNAHomo sapiens 272ugucuacuacuggagacacugg 2227322RNAHomo sapiens 273cuuucagucagauguuugcugc 2227422RNAHomo sapiens 274cucaucugcaaagaaguaagug 2227522RNAHomo sapiens 275caagaaccucaguugcuuuugu 2227622RNAHomo sapiens 276uaugcauuguauuuuuaggucc 2227722RNAHomo sapiens 277uauugcacucgucccggccucc 2227822RNAHomo sapiens 278ucgugcaucccuuuagaguguu 2227922RNAHomo sapiens 279caaaacuggcaauuacuuuugc 2228022RNAHomo sapiens 280uauaccucaguuuuaucaggug 2228122RNAHomo sapiens 281caggucgucuugcagggcuucu 2228220RNAHomo sapiens 282auuccuagaaauuguucaua 2028319RNAHomo sapiens 283aguguggcuuucuuagagc 1928421RNAHomo sapiens 284agaggauacccuuuguauguu 2128522RNAHomo sapiens 285cugguuucacaugguggcuuag 2228622RNAHomo sapiens 286gugacaucacauauacggcagc 2228719RNAHomo sapiens 287gggcgccugugaucccaac 1928823RNAHomo sapiens 288cggcccgggcugcugcuguuccu 2328922RNAHomo sapiens 289cacuagauugugagcuccugga 2229022RNAHomo sapiens 290cuauacagucuacugucuuucc 2229119RNAHomo sapiens 291gaugaugcugcugaugcug 1929218RNAHomo sapiens 292ugaggcaguagauugaau 1829322RNAHomo sapiens 293cugccaauuccauaggucacag 2229417RNAHomo sapiens 294uaagugcuuccaugcuu 1729522RNAHomo sapiens 295uauguaacacgguccacuaacc 2229622RNAHomo sapiens 296cagcagcaauucauguuuugaa 2229722RNAHomo sapiens 297uaacugguugaacaacugaacc 2229822RNAHomo sapiens 298guucucccaacguaagcccagc 2229922RNAHomo sapiens 299cuuaugcaagauucccuucuac 2230022RNAHomo sapiens 300aaagugcauccuuuuagagguu 2230121RNAHomo sapiens 301uaggacacauggucuacuucu 2130222RNAHomo sapiens 302cugaagcucagagggcucugau 2230321RNAHomo sapiens 303ugcaggaccaagaugagcccu 2130422RNAHomo sapiens 304accguggcuuucgauuguuacu 2230520RNAHomo sapiens 305gugcauugcuguugcauugc 2030621RNAHomo sapiens 306aaaacggugagauuuuguuuu 2130720RNAHomo sapiens 307auggagauagauauagaaau 2030823RNAHomo sapiens 308aaggagcuuacaaucuagcuggg 2330922RNAHomo sapiens 309cacuggcuccuuucuggguaga 2231022RNAHomo sapiens 310acaaagugcuucccuuuagagu 2231122RNAHomo sapiens 311augcaccugggcaaggauucug 2231222RNAHomo sapiens 312gaaaucaagcgugggugagacc 2231322RNAHomo sapiens 313caaagaauucuccuuuugggcu 2231419RNAHomo sapiens 314ugagcugcuguaccaaaau 1931522RNAHomo sapiens 315uucaaguaauccaggauaggcu 2231622RNAHomo sapiens 316augguacccuggcauacugagu 2231722RNAHomo sapiens 317uucccuuugucauccuucgccu 2231822RNAHomo sapiens 318uuugaggcuacagugagaugug 2231921RNAHomo sapiens 319ccaccaccgugucugacacuu 2132022RNAHomo sapiens 320ugcaacgaaccugagccacuga 2232121RNAHomo sapiens 321agagaagaagaucagccugca 2132220RNAHomo sapiens 322ucugcaggguuugcuuugag 2032323RNAHomo sapiens 323uuauugcuuaagaauacgcguag 2332422RNAHomo sapiens 324aaucauacacgguugaccuauu 2232521RNAHomo sapiens 325aggguaagcugaaccucugau 2132622RNAHomo sapiens 326gugaacgggcgccaucccgagg 2232722RNAHomo sapiens 327aauugcacgguauccaucugua 2232822RNAHomo sapiens 328aaaaccgucuaguuacaguugu 2232922RNAHomo sapiens 329agaguugagucuggacgucccg 2233021RNAHomo sapiens 330ccacaccguaucugacacuuu 2133122RNAHomo sapiens 331cucaguagccaguguagauccu 2233221RNAHomo sapiens 332cacauuacacggucgaccucu 2133322RNAHomo sapiens 333aucauagaggaaaauccauguu 2233420RNAHomo sapiens 334ccauggaucuccaggugggu 2033523RNAHomo sapiens 335gaacgcgcuucccuauagagggu 2333623RNAHomo sapiens 336acuuaaacguggauguacuugcu 2333722RNAHomo sapiens 337agagcuuagcugauuggugaac 2233820RNAHomo sapiens 338agaccauggguucucauugu 2033921RNAHomo sapiens 339uacucaaaaagcugucaguca 2134022RNAHomo sapiens 340guagauucuccuucuaugagua 2234123RNAHomo sapiens 341aaacucuacuuguccuucugagu 2334223RNAHomo sapiens 342gagggucuugggagggaugugac 2334322RNAHomo sapiens 343ccucccacacccaaggcuugca 2234423RNAHomo sapiens 344aacauucauugcugucggugggu 2334522RNAHomo sapiens 345aacgcacuucccuuuagagugu 2234622RNAHomo sapiens 346ucaguaaauguuuauuagauga 2234722RNAHomo sapiens 347auaaagcuagauaaccgaaagu 2234820RNAHomo sapiens 348ggggagcuguggaagcagua 2034921RNAHomo sapiens 349ugaguuggccaucugagugag 2135023RNAHomo sapiens 350acuugggcacugaaacaaugucc 2335122RNAHomo sapiens 351uaauacugccugguaaugauga 2235222RNAHomo sapiens 352uaugugccuuuggacuacaucg 2235322RNAHomo sapiens 353uggugguuuacaaaguaauuca 2235422RNAHomo sapiens 354acucaaaaugggggcgcuuucc 2235522RNAHomo sapiens 355ccucugaaauucaguucuucag 2235622RNAHomo sapiens 356aacgccauuaucacacuaaaua 2235722RNAHomo sapiens 357uugggaucauuuugcauccaua 2235822RNAHomo sapiens 358uggcucaguucagcaggaacag 2235922RNAHomo sapiens 359ucaggcucaguccccucccgau 2236023RNAHomo sapiens 360ucauagcccuguacaaugcugcu 2336122RNAHomo sapiens 361uauguaauaugguccacaucuu 2236218RNAHomo sapiens 362uucacagggaggugucau 1836322RNAHomo sapiens 363uacugcagacguggcaaucaug 2236421RNAHomo sapiens 364gcaggaacuugugagucuccu 2136522RNAHomo sapiens 365gaugagcucauuguaauaugag 2236622RNAHomo sapiens 366agaucgaccguguuauauucgc 2236721RNAHomo sapiens 367guguugaaacaaucucuacug 2136823RNAHomo sapiens 368ucugcucauaccccaugguuucu 2336918RNAHomo sapiens 369ugcuuccuuucagagggu 1837022RNAHomo sapiens 370aaagugcuuccuuuuagagggu 2237122RNAHomo sapiens 371caacuagacugugagcuucuag 2237220RNAHomo sapiens 372agagucuugugaugucuugc 2037320RNAHomo sapiens 373cuacaaagggaagcccuuuc 2037423RNAHomo sapiens 374cacucagccuugagggcacuuuc 2337522RNAHomo sapiens 375cuuaucagauuguauuguaauu 2237625RNAHomo sapiens 376cuagugagggacagaaccaggauuc 2537721RNAHomo sapiens 377uguucauguagauguuuaagc

2137821RNAHomo sapiens 378auauaugaugacuuagcuuuu 2137921RNAHomo sapiens 379cuccagagggaugcacuuucu 2138022RNAHomo sapiens 380caucuuaccggacagugcugga 2238122RNAHomo sapiens 381uggguggucuggagauuugugc 2238223RNAHomo sapiens 382aaagugcugcgacauuugagcgu 2338322RNAHomo sapiens 383aaaaguaauugcgaguuuuacc 2238422RNAHomo sapiens 384aaaaguacuugcggauuuugcu 2238518RNAHomo sapiens 385uugagaaggaggcugcug 1838623RNAHomo sapiens 386caagucuuauuugagcaccuguu 2338721RNAHomo sapiens 387gcgacccacucuugguuucca 2138822RNAHomo sapiens 388uagguaguuuccuguuguuggg 2238922RNAHomo sapiens 389caauuuagugugugugauauuu 2239021RNAHomo sapiens 390gugcauuguaguugcauugca 2139122RNAHomo sapiens 391aaaaguaauugugguuuuugcc 2239222RNAHomo sapiens 392agucauuggaggguuugagcag 2239322RNAHomo sapiens 393uggauuucuuugugaaucacca 2239423RNAHomo sapiens 394ugauuguagccuuuuggaguaga 2339522RNAHomo sapiens 395ccuauucuugauuacuuguuuc 2239622RNAHomo sapiens 396ucgugucuuguguugcagccgg 2239722RNAHomo sapiens 397acaguagucugcacauugguua 2239822RNAHomo sapiens 398aaucaugugcagugccaauaug 2239923RNAHomo sapiens 399uaaggugcaucuagugcaguuag 2340021RNAHomo sapiens 400cuggauggcuccuccaugucu 2140122RNAHomo sapiens 401ugauugguacgucuguggguag 2240227RNAHomo sapiens 402cacuguaggugauggugagagugggca 2740319RNAHomo sapiens 403agcugucugaaaaugucuu 1940422RNAHomo sapiens 404uucacaaggaggugucauuuau 2240522RNAHomo sapiens 405agacuucccauuugaagguggc 2240623RNAHomo sapiens 406ucuuugguuaucuagcuguauga 2340722RNAHomo sapiens 407aagugccuccuuuuagaguguu 2240822RNAHomo sapiens 408uucccuuugucauccuaugccu 2240922RNAHomo sapiens 409uagcaccauuugaaaucgguua 2241018RNAHomo sapiens 410cgggcguggugguggggg 1841122RNAHomo sapiens 411uggagugugacaaugguguuug 2241222RNAHomo sapiens 412caacaaaucccagucuaccuaa 2241322RNAHomo sapiens 413caggccauauugugcugccuca 2241423RNAHomo sapiens 414aacauucauuguugucggugggu 2341521RNAHomo sapiens 415ugauuguccaaacgcaauucu 2141623RNAHomo sapiens 416uaagugcuuccauguuugagugu 2341722RNAHomo sapiens 417uggcagugucuuagcugguugu 2241821RNAHomo sapiens 418aauauaacacagauggccugu 2141922RNAHomo sapiens 419caauguuuccacagugcaucac 2242022RNAHomo sapiens 420aaugcaccugggcaaggauuca 2242121RNAHomo sapiens 421ugguagacuauggaacguagg 2142223RNAHomo sapiens 422uuucaagccagggggcguuuuuc 2342322RNAHomo sapiens 423cucuagagggaagcacuuucug 2242422RNAHomo sapiens 424auauuaccauuagcucaucuuu 2242521RNAHomo sapiens 425auccuugcuaucugggugcua 2142621RNAHomo sapiens 426aggcaagaugcuggcauagcu 2142722RNAHomo sapiens 427aacccguagauccgaacuugug 2242822RNAHomo sapiens 428gaggguuggguggaggcucucc 2242922RNAHomo sapiens 429ggggguccccggugcucggauc 2243021RNAHomo sapiens 430caacaccagucgaugggcugu 2143123RNAHomo sapiens 431ggcagguucucacccucucuagg 2343222RNAHomo sapiens 432uuuaggauaagcuugacuuuug 2243321RNAHomo sapiens 433uggaggagaaggaaggugaug 2143421RNAHomo sapiens 434caaagguauuugugguuuuug 2143522RNAHomo sapiens 435agaauuguggcuggacaucugu 2243622RNAHomo sapiens 436aaugcacccgggcaaggauucu 2243723RNAHomo sapiens 437uaagugcuuccauguuuugguga 2343822RNAHomo sapiens 438uuccuaugcauauacuucuuug 2243922RNAHomo sapiens 439uggaauguaaggaagugugugg 2244022RNAHomo sapiens 440aaagugcuucucuuuggugggu 2244122RNAHomo sapiens 441aaaaguaauugcggauuuugcc 2244221RNAHomo sapiens 442gugucuuuugcucugcaguca 2144322RNAHomo sapiens 443uguaaacauccucgacuggaag 2244421RNAHomo sapiens 444ccccaccuccucucuccucag 2144522RNAHomo sapiens 445gaaggcgcuucccuuuagagcg 2244622RNAHomo sapiens 446guggguacggcccagugggggg 2244722RNAHomo sapiens 447cguguauuugacaagcugaguu 2244822RNAHomo sapiens 448uccgagccugggucucccucuu 2244922RNAHomo sapiens 449cgaaaacagcaauuaccuuugc 2245022RNAHomo sapiens 450aaaagcuggguugagagggcga 2245123RNAHomo sapiens 451uccaguaccacgugucagggcca 2345223RNAHomo sapiens 452uuacaguuguucaaccaguuacu 2345322RNAHomo sapiens 453gagcuuauucauaaaagugcag 2245422RNAHomo sapiens 454cuugguucagggagggucccca 2245521RNAHomo sapiens 455acucuagcugccaaaggcgcu 2145623RNAHomo sapiens 456uauucauuuauccccagccuaca 2345721RNAHomo sapiens 457cccagauaauggcacucucaa 2145822RNAHomo sapiens 458aaaacuguaauuacuuuuguac 2245923RNAHomo sapiens 459caguaacaaagauucauccuugu 2346023RNAHomo sapiens 460ucggggaucaucaugucacgaga 2346122RNAHomo sapiens 461ugauauguuugauauauuaggu 2246222RNAHomo sapiens 462augguuccgucaagcaccaugg 2246322RNAHomo sapiens 463acuguugcuaauaugcaacucu 2246422RNAHomo sapiens 464uuuugcgauguguuccuaauau 2246522RNAHomo sapiens 465aauugcacuuuagcaaugguga 2246620RNAHomo sapiens 466uaaggcacgcggugaaugcc 2046723RNAHomo sapiens 467ugcaccaugguugucugagcaug 2346823RNAHomo sapiens 468uaauacugccggguaaugaugga 2346920RNAHomo sapiens 469guccgcucggcgguggccca 2047022RNAHomo sapiens 470cucuagagggaagcacuuucug 2247122RNAHomo sapiens 471acaguagagggaggaaucgcag 2247222RNAHomo sapiens 472caaaaguaauuguggauuuugu 2247322RNAHomo sapiens 473uagcuuaucagacugauguuga 2247421RNAHomo sapiens 474ugguucuagacuugccaacua 2147523RNAHomo sapiens 475aggcaguguaguuagcugauugc 2347622RNAHomo sapiens 476uaauacugucugguaaaaccgu 2247722RNAHomo sapiens 477augcugacauauuuacuagagg 2247822RNAHomo sapiens 478acugauuucuuuugguguucag 2247922RNAHomo sapiens 479gccugcugggguggaaccuggu 2248021RNAHomo sapiens 480cuauacaaucuacugucuuuc 2148122RNAHomo sapiens 481caguuaucacagugcugaugcu 2248221RNAHomo sapiens 482uaaaguaaauaugcaccaaaa 2148323RNAHomo sapiens 483uacugcaucaggaacugauugga 2348422RNAHomo sapiens 484cucuagagggaagcgcuuucug 2248522RNAHomo sapiens 485cuuucagucggauguuuacagc 2248620RNAHomo sapiens 486guguguggaaaugcuucugc 2048722RNAHomo sapiens 487aaucguacagggucauccacuu 2248822RNAHomo sapiens 488gacugacaccucuuugggugaa 2248922RNAHomo sapiens 489uccuucauuccaccggagucug 2249021RNAHomo sapiens 490agugaaugauggguucugacc 2149123RNAHomo sapiens 491uggaagacuagugauuuuguugu 2349221RNAHomo sapiens 492agggcccccccucaauccugu 2149323RNAHomo sapiens 493aggaugagcaaagaaaguagauu 2349422RNAHomo sapiens 494ugguugaccauagaacaugcgc 2249517RNAHomo sapiens 495gugggggagaggcuguc 1749622RNAHomo sapiens 496ucucugggccugugucuuaggc 2249722RNAHomo sapiens 497aacuggaucaauuauaggagug 2249822RNAHomo sapiens 498caucaucgucucaaaugagucu 2249922RNAHomo sapiens 499caucuuccaguacaguguugga 2250022RNAHomo sapiens 500aucgugcauccuuuuagagugu 2250121RNAHomo sapiens 501ggcuagcaacagcgcuuaccu 2150222RNAHomo sapiens 502accuugccuugcugcccgggcc 2250322RNAHomo sapiens 503uggugggcacagaaucuggacu 2250422RNAHomo sapiens 504aaacauucgcggugcacuucuu 2250522RNAHomo sapiens 505ucuucucuguuuuggccaugug 2250622RNAHomo sapiens 506ccuauucuugguuacuugcacg 2250723RNAHomo sapiens 507aguauguucuuccaggacagaac 2350822RNAHomo sapiens 508uggacggagaacugauaagggu 2250921RNAHomo sapiens 509aucauagaggaaaauccacgu 2151022RNAHomo sapiens 510cguguucacagcggaccuugau 2251124RNAHomo sapiens 511agccuggaagcuggagccugcagu 2451221RNAHomo sapiens 512uggcagggaggcugggagggg 2151322RNAHomo sapiens 513uugagaaugaugaaucauuagg 2251422RNAHomo sapiens 514cuauacaaccuacugccuuccc 2251522RNAHomo sapiens 515ggagaaauuauccuuggugugu 2251622RNAHomo sapiens 516aaagugcuucccuuuggacugu 2251719RNAHomo sapiens 517ugggcguaucuguaugcua 1951820RNAHomo sapiens 518cuguaugcccucaccgcuca 2051921RNAHomo sapiens 519cugacuguugccguccuccag 2152024RNAHomo sapiens 520cugaagugauguguaacugaucag 2452123RNAHomo sapiens 521caaagugcuguucgugcagguag 2352222RNAHomo sapiens 522agggcuuagcugcuugugagca 2252320RNAHomo sapiens 523aggaauguuccuucuuugcc 2052422RNAHomo sapiens 524acacagggcuguugugaagacu 2252521RNAHomo sapiens 525gaaggcgcuucccuuuggagu 2152623RNAHomo sapiens 526uaauccuugcuaccugggugaga 2352724RNAHomo sapiens 527agccugauuaaacacaugcucuga 2452822RNAHomo sapiens 528acugcauuaugagcacuuaaag 2252922RNAHomo sapiens 529ggaggggucccgcacugggagg 2253022RNAHomo sapiens 530aucgggaaugucguguccgccc 2253122RNAHomo sapiens 531gagugccuucuuuuggagcguu 2253222RNAHomo sapiens 532agagguugcccuuggugaauuc 2253322RNAHomo sapiens 533agacccuggucugcacucuauc 2253422RNAHomo sapiens 534caaaaaucucaauuacuuuugc 2253520RNAHomo sapiens 535uaaagagcccuguggagaca 2053623RNAHomo sapiens 536agcugguguugugaaucaggccg 2353721RNAHomo sapiens 537ugucuugcaggccgucaugca 2153822RNAHomo sapiens 538ugaaacauacacgggaaaccuc 2253922RNAHomo sapiens 539uugcauauguaggaugucccau 2254023RNAHomo sapiens 540cuaauaguaucuaccacaauaaa 2354122RNAHomo sapiens 541aaucauacagggacauccaguu 2254223RNAHomo sapiens 542ucuggcuccgugucuucacuccc 2354322RNAHomo sapiens 543uauacaagggcagacucucucu 2254427RNAHomo sapiens 544auugaucaucgacacuucgaacgcaau 2754522RNAHomo sapiens 545ucggauccgucugagcuuggcu 2254622RNAHomo sapiens 546uccuguacugagcugccccgag 2254722RNAHomo sapiens 547ucagugcacuacagaacuuugu 2254822RNAHomo sapiens 548ugugagguuggcauuguugucu 2254922RNAHomo sapiens 549aaaaguauuugcggguuuuguc 2255021RNAHomo sapiens 550cauaaaguagaaagcacuacu 2155121RNAHomo sapiens 551uuaauaucggacaaccauugu 2155222RNAHomo sapiens 552uaaugccccuaaaaauccuuau 2255322RNAHomo sapiens 553cacccguagaaccgaccuugcg 2255422RNAHomo sapiens 554caucuuacugggcagcauugga 2255522RNAHomo sapiens 555uaacacugucugguaacgaugu 2255621RNAHomo sapiens 556aaagcgcuucccuucagagug 2155725RNAHomo sapiens 557gcugggcagggcuucugagcuccuu 2555822RNAHomo sapiens 558gugaauuaccgaagggccauaa 2255922RNAHomo sapiens 559ucagugcaucacagaacuuugu 2256023RNAHomo sapiens 560agcagcauuguacagggcuauga 2356122RNAHomo sapiens 561ccaaaacugcaguuacuuuugc 2256221RNAHomo sapiens 562cccggagccaggaugcagcuc 2156322RNAHomo sapiens 563uauagggauuggagccguggcg 2256422RNAHomo sapiens 564agaucagaaggugauuguggcu 2256522RNAHomo sapiens 565ucugcccccuccgcugcugcca

2256623RNAHomo sapiens 566gaagugcuucgauuuuggggugu 2356720RNAHomo sapiens 567acucaaacugugggggcacu 2056824RNAHomo sapiens 568agcagaagcagggagguucuccca 2456922RNAHomo sapiens 569uuugugaccugguccacuaacc 2257023RNAHomo sapiens 570acuucaccugguccacuagccgu 2357123RNAHomo sapiens 571caaagcgcuucucuuuagagugu 2357224RNAHomo sapiens 572ucagaacaaaugccgguucccaga 2457322RNAHomo sapiens 573acuuguaugcuagcucagguag 2257422RNAHomo sapiens 574uugugucaauaugcgaugaugu 2257521RNAHomo sapiens 575cacuguguccuuucugcguag 2157622RNAHomo sapiens 576aaauuauuguacaucggaugag 2257722RNAHomo sapiens 577aagauguggaaaaauuggaauc 2257821RNAHomo sapiens 578ucuuguguucucuagaucagu 2157922RNAHomo sapiens 579gacuauagaacuuucccccuca 2258018RNAHomo sapiens 580aucccaccucugccacca 1858117RNAHomo sapiens 581ucgccuccuccucuccc 1758221RNAHomo sapiens 582gaacggcuucauacaggaguu 2158322RNAHomo sapiens 583uuugguccccuucaaccagcua 2258422RNAHomo sapiens 584ggguggggauuuguugcauuac 2258522RNAHomo sapiens 585caagcuuguaucuauagguaug 2258622RNAHomo sapiens 586ugagaaccacgucugcucugag 2258721RNAHomo sapiens 587uugugcuugaucuaaccaugu 2158821RNAHomo sapiens 588caagucacuagugguuccguu 2158922RNAHomo sapiens 589ccaauauuacugugcugcuuua 2259023RNAHomo sapiens 590cagugcaaugauauugucaaagc 2359121RNAHomo sapiens 591ugauauguuugauauuggguu 2159222RNAHomo sapiens 592uuuguucguucggcucgcguga 2259322RNAHomo sapiens 593uagcaaaaacugcaguuacuuu 2259422RNAHomo sapiens 594aggggcuggcuuuccucugguc 2259522RNAHomo sapiens 595caaagugccucccuuuagagug 2259623RNAHomo sapiens 596uaaaucccauggugccuucuccu 2359720RNAHomo sapiens 597guagaggagauggcgcaggg 2059822RNAHomo sapiens 598acaggugagguucuugggagcc 2259922RNAHomo sapiens 599cuguugccacuaaccucaaccu 2260022RNAHomo sapiens 600cucuagagggaagcacuuucug 2260122RNAHomo sapiens 601aaaguucugagacacuccgacu 2260221RNAHomo sapiens 602uaacagucuccagucacggcc 2160322RNAHomo sapiens 603cgucaacacuugcugguuuccu 2260422RNAHomo sapiens 604ugaguauuacauggccaaucuc 2260522RNAHomo sapiens 605ucaaaacugaggggcauuuucu 2260622RNAHomo sapiens 606aaaaacugagacuacuuuugca 2260721RNAHomo sapiens 607ucuaguaagaguggcagucga 2160822RNAHomo sapiens 608cccugugcccggcccacuucug 2260922RNAHomo sapiens 609uuaugguuugccugggacugag 2261022RNAHomo sapiens 610auguagggcuaaaagccauggg 2261121RNAHomo sapiens 611uuaggccgcagaucuggguga 2161222RNAHomo sapiens 612uucaacggguauuuauugagca 2261322RNAHomo sapiens 613uuugguccccuucaaccagcug 2261422RNAHomo sapiens 614gucauacacggcucuccucucu 2261525RNAHomo sapiens 615aaaggauucugcugucggucccacu 2561622RNAHomo sapiens 616auauaauacaaccugcuaagug 2261722RNAHomo sapiens 617aacacaccugguuaaccucuuu 2261822RNAHomo sapiens 618aagacgggaggaaagaagggag 2261922RNAHomo sapiens 619aggcagcgggguguaguggaua 2262022RNAHomo sapiens 620cugcgcaagcuacugccuugcu 2262123RNAHomo sapiens 621ccaguuaccgcuuccgcuaccgc 2362222RNAHomo sapiens 622cagugcaaugaugaaagggcau 2262318RNAHomo sapiens 623gucccuguucaggcgcca 1862422RNAHomo sapiens 624ucaccagcccuguguucccuag 2262522RNAHomo sapiens 625cucuagagggaagcgcuuucug 2262622RNAHomo sapiens 626ugagccccugugccgcccccag 2262721RNAHomo sapiens 627gucagcggaggaaaagaaacu 2162822RNAHomo sapiens 628cggcaacaagaaacugccugag 2262923RNAHomo sapiens 629cuggagauauggaagagcugugu 2363022RNAHomo sapiens 630cuuggcaccuagcaagcacuca 2263121RNAHomo sapiens 631ugagcuaaaugugugcuggga 2163222RNAHomo sapiens 632cacgcucaugcacacacccaca 2263320RNAHomo sapiens 633ucguuugccuuuuucugcuu 2063422RNAHomo sapiens 634acagauucgauucuaggggaau 2263522RNAHomo sapiens 635uaaucucagcuggcaacuguga 2263622RNAHomo sapiens 636ggauaucaucauauacuguaag 2263722RNAHomo sapiens 637gggguuccuggggaugggauuu 2263821RNAHomo sapiens 638uuaagacuugcagugauguuu 2163922RNAHomo sapiens 639uauggcacugguagaauucacu 2264022RNAHomo sapiens 640caaccuggaggacuccaugcug 2264123RNAHomo sapiens 641gcaaagcacacggccugcagaga 2364221RNAHomo sapiens 642cuguacaggccacugccuugc 2164321RNAHomo sapiens 643ucacuccucuccucccgucuu 2164422RNAHomo sapiens 644acagcaggcacagacaggcagu 2264523RNAHomo sapiens 645uaggcagugucauuagcugauug 2364622RNAHomo sapiens 646acuuuaacauggaggcacuugc 2264722RNAHomo sapiens 647gaaguuguucgugguggauucg 2264822RNAHomo sapiens 648acccuaucaauauugucucugc 2264921RNAHomo sapiens 649gugccagcugcagugggggag 2165020RNAHomo sapiens 650agagguauagggcaugggaa 2065122RNAHomo sapiens 651auucugcauuuuuagcaaguuc 2265219RNAHomo sapiens 652ugucucugcugggguuucu 1965320RNAHomo sapiens 653cggcucugggucugugggga 2065421RNAHomo sapiens 654aaggcagggcccccgcucccc 2165522RNAHomo sapiens 655cuauacggccuccuagcuuucc 2265621RNAHomo sapiens 656uccuucugcuccgucccccag 2165722RNAHomo sapiens 657ugcccuaaaugccccuucuggc 2265822RNAHomo sapiens 658aguauucuguaccagggaaggu 2265922RNAHomo sapiens 659uucuccaaaagggagcacuuuc 2266022RNAHomo sapiens 660aacuguuugcagaggaaacuga 2266122RNAHomo sapiens 661ccuguucuccauuacuuggcuc 2266222RNAHomo sapiens 662aucuggagguaagaagcacuuu 2266322RNAHomo sapiens 663uaugugggaugguaaaccgcuu 2266422RNAHomo sapiens 664uauacaagggcaagcucucugu 2266522RNAHomo sapiens 665uuauaaagcaaugagacugauu 2266622RNAHomo sapiens 666uaacagucuacagccauggucg 2266723RNAHomo sapiens 667uguaguguuuccuacuuuaugga 2366822RNAHomo sapiens 668acggguuaggcucuugggagcu 2266922RNAHomo sapiens 669cugggagaaggcuguuuacucu 2267022RNAHomo sapiens 670gugagucucuaagaaaagagga 2267121RNAHomo sapiens 671cggcggggacggcgauugguc 2167221RNAHomo sapiens 672ccuguugaaguguaaucccca 2167321RNAHomo sapiens 673uuuugcaccuuuuggagugaa 2167421RNAHomo sapiens 674caucccuugcaugguggaggg 2167521RNAHomo sapiens 675cggggcagcucaguacaggau 2167621RNAHomo sapiens 676aagccugcccggcuccucggg 2167722RNAHomo sapiens 677ugggucuuugcgggcgagauga 2267821RNAHomo sapiens 678uccgguucucagggcuccacc 2167922RNAHomo sapiens 679ugccuacugagcugauaucagu 2268022RNAHomo sapiens 680aguuuugcagguuugcauuuca 2268118RNAHomo sapiens 681gcaugggugguucagugg 1868222RNAHomo sapiens 682auaagacgagcaaaaagcuugu 2268323RNAHomo sapiens 683uauggcuuuuuauuccuauguga 2368422RNAHomo sapiens 684cuagguauggucccagggaucc 2268522RNAHomo sapiens 685aacauucaaccugucggugagu 2268621RNAHomo sapiens 686augauccaggaaccugccucu 2168722RNAHomo sapiens 687cgcaggggccgggugcucaccg 2268821RNAHomo sapiens 688uggguuuacguugggagaacu 2168923RNAHomo sapiens 689uacccuguagauccgaauuugug 2369022RNAHomo sapiens 690aguggggaacccuuccaugagg 2269123RNAHomo sapiens 691aggaccugcgggacaagauucuu 2369223RNAHomo sapiens 692uucucgaggaaagaagcacuuuc 2369322RNAHomo sapiens 693uacucaggagaguggcaaucac 2269420RNAHomo sapiens 694ccccagggcgacgcggcggg 2069523RNAHomo sapiens 695ucucuggagggaagcacuuucug 2369625RNAHomo sapiens 696gggcgacaaagcaagacucuuucuu 2569721RNAHomo sapiens 697aggcggagacuugggcaauug 2169822RNAHomo sapiens 698ugcggggcuagggcuaacagca 2269923RNAHomo sapiens 699agugccugagggaguaagagccc 2370021RNAHomo sapiens 700uacuuggaaaggcaucaguug 2170122RNAHomo sapiens 701uuuagagacggggucuugcucu 2270221RNAHomo sapiens 702aggaggcagcgcucucaggac 2170320RNAHomo sapiens 703cgugccacccuuuuccccag 2070421RNAHomo sapiens 704gaagugugccguggugugucu 2170522RNAHomo sapiens 705cggaugagcaaagaaagugguu 2270622RNAHomo sapiens 706agaaggaaauugaauucauuua 2270721RNAHomo sapiens 707gcaguccaugggcauauacac 2170822RNAHomo sapiens 708gcugacuccuaguccagggcuc 2270923RNAHomo sapiens 709uuuggcacuagcacauuuuugcu 2371018RNAHomo sapiens 710cagggaggugaaugugau 1871122RNAHomo sapiens 711uucucaaggaggugucguuuau 2271222RNAHomo sapiens 712aaaaguaauugcgguuuuugcc 2271322RNAHomo sapiens 713aggcggggcgccgcgggaccgc 2271420RNAHomo sapiens 714aaaagcuggguugagagggu 2071522RNAHomo sapiens 715aaaagcuggguugagagggcaa 2271622RNAHomo sapiens 716uggugggccgcagaacaugugc 2271720RNAHomo sapiens 717ccucugggcccuuccuccag 2071818RNAHomo sapiens 718uccagugcccuccucucc 1871922RNAHomo sapiens 719cuggcccucucugcccuuccgu 2272022RNAHomo sapiens 720ugagaacugaauuccauaggcu 2272121RNAHomo sapiens 721cgcgggugcuuacugacccuu 2172221RNAHomo sapiens 722ugagugccggugccugcccug 2172322RNAHomo sapiens 723cucggcgcggggcgcgggcucc 2272422RNAHomo sapiens 724uccagcaucagugauuuuguug 2272523RNAHomo sapiens 725cgggucggaguuagcucaagcgg 2372622RNAHomo sapiens 726uggucuaggauuguuggaggag 2272722RNAHomo sapiens 727uucauucggcuguccagaugua 2272821RNAHomo sapiens 728ccaguccugugccugccgccu 2172926RNAHomo sapiens 729gugagggcaugcaggccuggaugggg 2673023RNAHomo sapiens 730aucaacagacauuaauugggcgc 2373122RNAHomo sapiens 731gcccgcguguggagccaggugu 2273222RNAHomo sapiens 732cugguacaggccugggggacag 2273323RNAHomo sapiens 733cucucaccacugcccucccacag 2373422RNAHomo sapiens 734acugcagugaaggcacuuguag 2273519RNAHomo sapiens 735aaaagcuggguugagagga 1973623RNAHomo sapiens 736uacccuguagaaccgaauuugug 2373722RNAHomo sapiens 737acuccagccccacagccucagc 2273823RNAHomo sapiens 738acuuacagacaagagccuugcuc 2373924RNAHomo sapiens 739aaagacauaggauagagucaccuc 2474022RNAHomo sapiens 740uccgucucaguuacuuuauagc 2274122RNAHomo sapiens 741acucaaaacccuucagugacuu 2274221RNAHomo sapiens 742cuccagagggaaguacuuucu 2174321RNAHomo sapiens 743aagcauucuuucauugguugg 2174421RNAHomo sapiens 744uugcucacuguucuucccuag 2174522RNAHomo sapiens 745cugggagguggauguuuacuuc 2274622RNAHomo sapiens 746cuccuacauauuagcauuaaca 2274722RNAHomo sapiens 747gcuacuucacaacaccagggcc 2274822RNAHomo sapiens 748aauccuuugucccugggugaga 2274923RNAHomo sapiens 749caacggaaucccaaaagcagcug 2375023RNAHomo sapiens 750agcagcauuguacagggcuauca 2375123RNAHomo sapiens 751aucgcugcgguugcgagcgcugu 2375221RNAHomo sapiens 752caaagcgcuucccuuuggagc 2175321RNAHomo sapiens 753uaaagugcugacagugcagau

2175422RNAHomo sapiens 754aagcccuuaccccaaaaagcau 2275518RNAHomo sapiens 755acguuggcucugguggug 1875622RNAHomo sapiens 756ggcuacaacacaggacccgggc 2275722RNAHomo sapiens 757ucccugagacccuaacuuguga 2275822RNAHomo sapiens 758gucccucuccaaaugugucuug 2275922RNAHomo sapiens 759cuuucagucggauguuugcagc 2276022RNAHomo sapiens 760ucuacagugcacgugucuccag 2276122RNAHomo sapiens 761acucggcguggcgucggucgug 2276223RNAHomo sapiens 762cugggaucuccggggucuugguu 2376322RNAHomo sapiens 763caugccuugaguguaggaccgu 2276422RNAHomo sapiens 764caacaaaucacagucugccaua 2276522RNAHomo sapiens 765uagguaguuucauguuguuggg 2276622RNAHomo sapiens 766uuagggcccuggcuccaucucc 2276722RNAHomo sapiens 767gcugcgcuuggauuucgucccc 2276823RNAHomo sapiens 768agcuacauugucugcuggguuuc 2376923RNAHomo sapiens 769agguugggaucgguugcaaugcu 2377022RNAHomo sapiens 770ucugggcaacaaagugagaccu 2277122RNAHomo sapiens 771cucuagagggaagcacuuucuc 2277220RNAHomo sapiens 772ugagcccuguccucccgcag 2077319RNAHomo sapiens 773uggauuuuuggaucaggga 1977422RNAHomo sapiens 774uacgucaucguugucaucguca 2277522RNAHomo sapiens 775ugagaccucuggguucugagcu 2277623RNAHomo sapiens 776gaacgccuguucuugccaggugg 2377721RNAHomo sapiens 777cuucuugugcucuaggauugu 2177824RNAHomo sapiens 778uugcagcugccugggagugacuuc 2477924RNAHomo sapiens 779uucuccaaaagaaagcacuuucug 2478019RNAHomo sapiens 780aggcacggugucagcaggc 1978122RNAHomo sapiens 781aaccagcaccccaacuuuggac 2278222RNAHomo sapiens 782caaagcgcuccccuuuagaggu 2278323RNAHomo sapiens 783cacccggcugugugcacaugugc 2378421RNAHomo sapiens 784aacauagaggaaauuccacgu 2178522RNAHomo sapiens 785ccaauauuggcugugcugcucc 2278620RNAHomo sapiens 786cugcaaagggaagcccuuuc 2078723RNAHomo sapiens 787guuugcacgggugggccuugucu 2378821RNAHomo sapiens 788gugggcgggggcaggugugug 2178922RNAHomo sapiens 789aguucuucaguggcaagcuuua 2279022RNAHomo sapiens 790ucggccugaccacccaccccac 2279121RNAHomo sapiens 791agggagggacgggggcugugc 2179222RNAHomo sapiens 792cugggagaggguuguuuacucc 2279322RNAHomo sapiens 793cgucuuacccagcaguguuugg 2279421RNAHomo sapiens 794ccgucgccgccacccgagccg 2179522RNAHomo sapiens 795aggugguccguggcgcguucgc 2279620RNAHomo sapiens 796gugucugggcggacagcugc 2079722RNAHomo sapiens 797gugaaauguuuaggaccacuag 2279822RNAHomo sapiens 798uuuaacauggggguaccugcug 2279922RNAHomo sapiens 799aacccguagauccgaucuugug 2280022RNAHomo sapiens 800ugagaacugaauuccauggguu 2280121RNAHomo sapiens 801aauauuauacagucaaccucu 2180222RNAHomo sapiens 802gaaagugcuuccuuuuagaggc 2280322RNAHomo sapiens 803aaguucuguuauacacucaggc 2280423RNAHomo sapiens 804aacauucaacgcugucggugagu 2380521RNAHomo sapiens 805acagucugcugagguuggagc 2180624RNAHomo sapiens 806ucccugagacccuuuaaccuguga 2480721RNAHomo sapiens 807ucagugcaugacagaacuugg 2180822RNAHomo sapiens 808uucaccaccuucuccacccagc 2280921RNAHomo sapiens 809uucacaguggcuaaguucugc 2181022RNAHomo sapiens 810ccucuuccccuugucucuccag 2281122RNAHomo sapiens 811aaacaaacauggugcacuucuu 2281221RNAHomo sapiens 812ugagaugaagcacuguagcuc 2181322RNAHomo sapiens 813aacacaccuauucaaggauuca 2281423RNAHomo sapiens 814uggugcggagagggcccacagug 2381517RNAHomo sapiens 815ucccuguucgggcgcca 1781622RNAHomo sapiens 816ggagacgcggcccuguuggagu 2281721RNAHomo sapiens 817acucuuucccuguugcacuac 2181820RNAHomo sapiens 818ucacaccugccucgcccccc 2081922RNAHomo sapiens 819uuuccggcucgcgugggugugu 2282019RNAHomo sapiens 820gagccaguuggacaggagc 1982122RNAHomo sapiens 821ugugacugguugaccagagggg 2282221RNAHomo sapiens 822ccuggaaacacugagguugug 2182322RNAHomo sapiens 823agggacgggacgcggugcagug 2282422RNAHomo sapiens 824uacccauugcauaucggaguug 2282523RNAHomo sapiens 825cucuugagggaagcacuuucugu 2382622RNAHomo sapiens 826acuggacuuagggucagaaggc 2282721RNAHomo sapiens 827acggugcuggauguggccuuu 2182822RNAHomo sapiens 828ugcccuuaaaggugaacccagu 2282925RNAHomo sapiens 829aggggugguguugggacagcuccgu 2583017RNAHomo sapiens 830ucauauugcuucuuucu 1783122RNAHomo sapiens 831acgcccuucccccccuucuuca 2283224RNAHomo sapiens 832ugccugggucucuggccugcgcgu 2483320RNAHomo sapiens 833ucacuguucagacaggcgga 2083422RNAHomo sapiens 834cagugcaauguuaaaagggcau 2283522RNAHomo sapiens 835uuuugcaauauguuccugaaua 2283623RNAHomo sapiens 836ucuuggaguaggucauugggugg 2383722RNAHomo sapiens 837gaauguugcucggugaaccccu 2283820RNAHomo sapiens 838cugcaaagggaagcccuuuc 2083921RNAHomo sapiens 839uccucuucucccuccucccag 2184020RNAHomo sapiens 840cuuccucgucugucugcccc 2084122RNAHomo sapiens 841ccucuagauggaagcacugucu 2284222RNAHomo sapiens 842cgggguuuugagggcgagauga 2284322RNAHomo sapiens 843cuacaaagggaagcacuuucuc 2284421RNAHomo sapiens 844aguuaggauuaggucguggaa 2184522RNAHomo sapiens 845uagguuauccguguugccuucg 2284624RNAHomo sapiens 846acugggggcuuucgggcucugcgu 2484721RNAHomo sapiens 847uuggccacaauggguuagaac 2184823RNAHomo sapiens 848uuaaugcuaaucgugauaggggu 2384924RNAHomo sapiens 849acuggcuagggaaaaugauuggau 2485021RNAHomo sapiens 850gcccuccgcccgugcaccccg 2185122RNAHomo sapiens 851acggauguuugagcaugugcua 2285223RNAHomo sapiens 852cgcauccccuagggcauuggugu 2385322RNAHomo sapiens 853aagcccuuaccccaaaaaguau 2285421RNAHomo sapiens 854agggggaaaguucuauagucc 2185522RNAHomo sapiens 855cucuagagggaagcgcuuucug 2285622RNAHomo sapiens 856accacugaccguugacuguacc 2285723RNAHomo sapiens 857cccaguguuuagacuaucuguuc 2385821RNAHomo sapiens 858uucacaguggcuaaguuccgc 2185922RNAHomo sapiens 859gaaagcgcuucccuuugcugga 2286022RNAHomo sapiens 860caggauguggucaaguguuguu 2286122RNAHomo sapiens 861uauugcacuugucccggccugu 2286224RNAHomo sapiens 862gcugguuucauauggugguuuaga 2486322RNAHomo sapiens 863ucucccaacccuuguaccagug 2286423RNAHomo sapiens 864ucaagagcaauaacgaaaaaugu 2386525RNAHomo sapiens 865agggaucgcgggcggguggcggccu 2586622DNAHomo sapiens 866aactatacaacctactacctca 22

Claims

1.-24. (canceled)

25. A method for diagnosing lung cancer in a patient comprising the steps of: (a) determining an expression profile of a miRNA in a blood sample from a patient, wherein the miRNA has a nucleotide sequence selected from the group consisting of SEQ ID NO: 49, SEQ ID NO: 109, SEQ ID NO: 161, SEQ ID NO: 289, SEQ ID NO: 426, SEQ ID NO: 579, SEQ ID NO: 783, SEQ ID NO: 799, SEQ ID NO: 807, and SEQ ID NO: 857, (b) comparing said expression profile to a reference expression profile, wherein the comparison of said determined expression profile to said reference expression profile allows for the diagnosis of lung cancer.

26. The method of claim 25, wherein the blood sample is a blood cellular fraction.

27. The method of claim 26, wherein the blood cellular fraction comprises erythrocytes, leukocytes, and thrombocytes.

28. The method of claim 25, wherein the determination of the expression profile of the predetermined set of miRNAs comprises the steps of: extracting total RNA from said blood sample, (ii) reverse-transcribing the total RNA into cDNA, and (iii) amplifying the cDNA and thereby quantifying said miRNAs.

29. The method of claim 25, wherein the diagnosis comprises determining type, grade, and/or stage of cancer.

30. The method of claim 25, wherein the diagnosis comprises determining survival rate, responsiveness to drugs, and/or monitoring the course of the disease or the therapy, e.g. chemotherapy, staging of the disease, measuring the response of a patient to therapeutic intervention, segmentation of patients suffering from the disease, identifying of a patient who has a risk to develop the disease, predicting/estimating the occurrence, preferably the severity of the occurrence of the disease, predicting the response of a patient with the disease to therapeutic intervention.

31. The method of claim 25, wherein the lung cancer selected from the group consisting of lung carcinoid, lung pleural mesothelioma and lung squamous cell carcinoma, in particular non-small cell lung carcinoma.

32. The method of claim 25, wherein the determination of an expression profile in step (a) comprises nucleic acid hybridization, nucleic acid amplification, polymerase extension, sequencing, mass spectroscopy or any combinations thereof, wherein the nucleic acid hybridization is particularly performed using a solid-phase nucleic acid biochip array, in particular a microarray, a bead-based assay, or in situ hybridization or wherein the nucleic acid amplification method is real-time PCR (RT-PCR).

33. A method for diagnosing lung cancer in a patient comprising the steps of: (a) determining an expression profile of a set comprising at least two miRNAs in a blood sample from a patient, wherein the at least two miRNAs comprised in the set have a nucleotide sequence selected from the group consisting of SEQ ID NO: 49, SEQ ID NO: 109, SEQ ID NO: 161, SEQ ID NO: 289, SEQ ID NO: 426, SEQ ID NO: 579, SEQ ID NO: 783, SEQ ID NO: 799, SEQ ID NO: 807, and SEQ ID NO: 857, and (b) comparing said expression profile to a reference expression profile, wherein the comparison of said determined expression profile to said reference expression profile allows for the diagnosis of lung cancer.

34. The method of claim 33, wherein the blood sample is a blood cellular fraction.

35. The method of claim 34, wherein the blood cellular fraction comprises erythrocytes, leukocytes, and thrombocytes.

36. The method of claim 33, wherein the determination of the expression profile of the predetermined set of miRNAs comprises the steps of: (i) extracting total RNA from said blood sample, (iv) reverse-transcribing the total RNA into cDNA, and (v) amplifying the cDNA and thereby quantifying said miRNAs.

37. The method of claim 33, wherein the diagnosis comprises determining type, grade, and/or stage of cancer.

38. The method of claim 33, wherein the diagnosis comprises determining survival rate, responsiveness to drugs, and/or monitoring the course of the disease or the therapy, e.g. chemotherapy, staging of the disease, measuring the response of a patient to therapeutic intervention, segmentation of patients suffering from the disease, identifying of a patient who has a risk to develop the disease, predicting/estimating the occurrence, preferably the severity of the occurrence of the disease, predicting the response of a patient with the disease to therapeutic intervention.

39. The method of claim 33, wherein the lung cancer selected from the group consisting of lung carcinoid, lung pleural mesothelioma and lung squamous cell carcinoma, in particular non-small cell lung carcinoma.

40. The method of claim 33, wherein the determination of an expression profile in step (a) comprises nucleic acid hybridization, nucleic acid amplification, polymerase extension, sequencing, mass spectroscopy or any combinations thereof, wherein the nucleic acid hybridization is particularly performed using a solid-phase nucleic acid biochip array, in particular a microarray, a bead-based assay, or in situ hybridization or wherein the nucleic acid amplification method is real-time PCR (RT-PCR).