Biomarkers for diagnosis of lung cancers

Abstract

To provide a method for diagnosing lung cancer, or a method for determining the prognosis of a subject having lung cancer.SOLUTION: A subject can be screened for lung cancer based on expression of specific mRNAs or miRNAs, or on detection of a nucleic acid, protein, peptide, or other biological molecules in blood, serum, or plasma. Embodiments include 29 specific miRNAs for use as biomarkers to screen or distinguish healthy individuals from individuals affected with a disease including lung cancer, and 81 additional nucleic acids, proteins, or peptides for use as biomarkers to screen or distinguish healthy individuals from individuals affected with a disease including lung cancer. Scores of levels of more than one of the mRNAs, miRNAs, or proteins can be compared to one or more threshold values to diagnose lung cancer or determine the prognosis thereof.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to the diagnosis of diseases using biomarkers, and more specifically, to a system and method for diagnosing adenocarcinoma based on specific miRNAs or other nucleotides or polypeptide hybridizations that identify altered expression levels.

Background Art

[0002] Lung cancer, also known as lung carcinoma, is a malignant lung tumor characterized by uncontrolled cell growth in lung tissue. This growth can spread beyond the lungs through the process of metastasis to nearby tissues or other parts of the body. Subtypes of lung cancer include adenocarcinoma lung cancer, squamous cell lung cancer, and small cell lung cancer. The treatment of lung cancer varies depending on the subtype of cancer.

[0003] Adenocarcinoma is a type of cancerous tumor that can occur in several parts of the body. It is defined as a neoplasm of epithelial tissue that has glandular origin, glandular characteristics, or both. Approximately 40% of lung cancers are adenocarcinomas, which usually originate from peripheral lung tissue. This cancer is usually found in the periphery of the lung, in contrast to small cell lung cancer and lung squamous cell cancer, which both tend to be more centrally located.

[0004] Most cases of lung adenocarcinoma (also called non-small cell lung cancer or NSCLC) are associated with smoking. However, lung adenocarcinoma is the most common form of lung cancer in non-smokers. Lung adenocarcinoma is one of the tumor types with the highest number of mutations. Common somatic mutations in lung adenocarcinoma affect many cancer genes and tumor suppressor genes.

[0005] Squamous cell carcinoma begins in squamous cells, which are thin, flat cells that line the inside of the airways in the lungs. About 30% of all lung cancers are classified as squamous cell carcinomas. This cancer is more strongly associated with smoking than any other type of non-small cell lung cancer. Other risk factors for lung squamous cell carcinoma include age, family history, secondhand smoke, mineral and metal dusts, asbestos, or radon exposure. Squamous cell carcinomas often spread (i.e., metastasize) to other parts of the body because the body fluids (e.g., blood and lymph) that constantly flow through the lungs carry the cancer cells with them.

[0006] Small cell lung cancer accounts for about 15% of all lung cancers and is most commonly seen in people with a smoking history. Small cell lung cancer usually begins in the bronchi, the main airways in the center of the chest that lead to the lungs, but it can also be found in the periphery of the lungs. Small cell lung cancer is a type of neuroendocrine tumor and can grow and spread rapidly.

[0007] Lung cancer can be detected by chest X-ray and computed tomography (CT) scan. Diagnosis is typically confirmed by a biopsy of the suspicious tissue. A biopsy can also determine the subtype of lung cancer. Most patients with lung cancer, such as adenocarcinoma, are diagnosed at a late stage of the disease, which has a low survival rate. When the cancer has spread to distant parts of the body (i.e., metastatic lung cancer), the 5-year survival rate is about 6%. When lung cancer is detected at its early stage, more treatment options are available, and the survival rate is longer. Therefore, there is a need for methods that can enable the detection of the disease at its early, pre-metastatic stage.

[0008] MicroRNA (miRNA) is a small non-coding RNA that functions in RNA silencing and post-transcriptional regulation of gene expression. miRNAs are evolutionarily conserved, endogenously expressed, non-coding small RNAs that are 20-25 nucleotides in size. miRNAs function by base pairing with complementary sequences within mRNA molecules. As a result, these mRNA molecules are silenced by one or more of the following processes: cleavage into two parts of the mRNA strand, destabilization of the mRNA by shortening of its poly(A) tail, and / or a decrease in the efficiency of translation of the mRNA into protein. miRNAs are involved in important functions in cell development, differentiation, proliferation, and apoptosis.

[0009] Recent studies have provided evidence of abnormal expression patterns of miRNAs in patients with cancer. This indicates the potential for their use as diagnostic and prognostic biomarkers. Other biomolecules or markers that can also be used to identify changes in the genomic sequences of genes associated with the development of lung cancer include single nucleotide polymorphisms, also known as SNPs. Additionally, changes in the genomic sequences of genes, including single or multiple point mutations or mutations in regions of the gene, have also been identified and may be associated with the development of lung cancer.

[0010] However, due to the variability in the expression of biomarkers, some diagnostic assays have been ineffective or unreliable. Additionally, conventional assays typically rely on a single molecular marker. Due to these limitations, diagnostic methods have not been able to reliably predict the presence of cancer or tumor progression. Therefore, there is a need to identify alternative molecular markers that overcome these limitations. SUMMARY OF THE INVENTION

[0011] The following summary is provided to facilitate easy understanding of some of the features of the present invention that are specific to the disclosed embodiments and is not intended as a complete description. A complete understanding of the various aspects of the embodiments disclosed herein can be obtained by considering the entire specification, the claims, and the entire abstract.

[0012] The present invention relates to a method for diagnosing a patient's lung cancer or predisposition to lung cancer, comprising: (a) determining the amount of at least one biomarker in a sample from the patient, wherein the at least one biomarker is selected from Table 1 or Table 2; and (b) comparing the amount of the at least one biomarker with a reference. A particular biomarker can identify the type of lung cancer as one of squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and adenocarcinoma lung cancer. In this method, additional biomedical information can be used in addition to the biomarker.

[0013] Embodiments are directed to a method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: (a) measuring the expression levels of at least two miRNAs in a test sample from the subject; (b) receiving the expression levels using a computer; (c) compiling the expression levels to generate a score; and (d) comparing the score with one or more thresholds to diagnose lung cancer or determine its prognosis.

[0014] Embodiments also include a method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: (a) measuring the expression levels of at least two nucleic acids, proteins, or peptides in a test sample from the subject; (b) receiving the expression levels using a computer; (c) compiling the expression levels to generate a score; and (d) comparing the score with one or more thresholds to diagnose lung cancer or determine its prognosis.

[0015] The methods described herein can use one or more of the biomarkers including, but not limited to, the mRNA and protein probes shown in Table 1, and the miRNAs shown in Table 2. Embodiments include methods of using one or more biomarkers to (1) identify a patient's lung cancer. Embodiments also include methods of using one or more biomarkers to identify in a patient (2) small cell lung cancer (SCLC), (3) non-small cell lung cancer (NSCLC, other than adenocarcinoma), (4) NSCLC adenocarcinoma, (5) NSCLC squamous cell carcinoma, and (6) NSCLC undifferentiated large cell type. As referenced in Table 3, specific biomarkers can be used to identify the type of cancer.

[0016] Additional embodiments include systems and methods for detecting and diagnosing lung cancer. Embodiments also include systems and methods for detecting and diagnosing lung cancer that cannot be identified by conventional methods (e.g., imaging and biopsy). Embodiments further include systems and methods for differentiating lung cancers including (SCLC, NSCLC, NSCLC adenocarcinoma, NSCLC squamous cell carcinoma, and NSCLC undifferentiated large cell type). The systems and methods can utilize biomarkers together with additional biomedical information of the patient. The biomarkers include those described in Tables 1 and 2 below.

[0017] The method described in this specification can use one or more mRNAs or proteins transcribed / translated from the following genes. TCTN3, DENND1A, FOS, MFSD11, PRPS1L1, F13A1, KLHL24, SSRP1, DDX24, KIF1B, RRP7A, MICALL1, C9orf16, SEPHS1, DMAC2L, ITGA2B, PURA, PAFAH1B3, PDXK, ARAF, TBCD, UBA1, EED, PARVB, RCN2, PGAP3, REX1BD(619or60), MED27, PIK3IP1, YTHDF3, BHMT2, ASF1A, ANXA8, ETFA, NMT1, EPHB3, KIF3C, LOH11CR2A(VWA5A), SLC48A1, MAPKAPK5-AS1, PLA2G4B, CALHM2, SENP5, SIDT2, R3HDM4, MARK4, SSH3, ATOH1, AXIN2, TAS2R13, PCDHB1, VWA7, TRIM49, CNTD2, TSHZ2, CDHR5, KIF26B, PADI4, TRIM36, LGI2, KCNMB4, TTTY14, ELAVL3, PAGE4, PER2, ZNF142, CD4, CCS, NELL2, RNF44, KLHL21, DNAJB12, CDC123, GNAI3, TRADD and THRA.

[0018] The method described in this specification can use one or more of the following miRNAs: hsa-miR-1204, hsa-miR-141-3p, hsa-miR-1827, hsa-miR-938, hsa-miR-125b-5p, hsa-miR-297, hsa-miR-10a-5p, hsa-miR-145-5p, hsa-miR-217, hsa-miR-3185, hsa-miR-21-5p, hsa-miR-363-3p, hsa-miR-631, hsa-miR-655, hsa-miR-1245b-5p, hsa-miR-369-3p, hsa-miR-875-3p, hsa-miR-105-5p, hsa-miR-1253, hsa-miR-1285-3p, hsa-miR-512-5p, hsa-miR-550b-3p, hsa-miR-571, hsa-miR-935, hsa-miR-145-5p, hsa-miR-3185, hsa-miR-1285-3p, hsa-miR-125b-5p, and hsa-miR-550b-3p.

[0019] Embodiments include a method of diagnosing cancer or determining the prognosis of a subject having cancer (such as lung adenocarcinoma), the method comprising: a) measuring the expression level of at least one miRNA in a test sample of the subject's plasma; b) comparing the expression level of at least one miRNA in the test sample with the level of a base sample; and c) diagnosing cancer or determining the prognosis based on the change in the expression of the miRNA in the test sample.

[0020] Embodiments further include a method of diagnosing cancer or determining the prognosis of a subject having cancer (such as lung adenocarcinoma), the method comprising: a) measuring the expression level of at least one miRNA in a test sample of the subject's plasma; b) comparing the expression level of at least one miRNA in the test sample with the level of a base sample; and c) diagnosing cancer or determining the prognosis based on the change in the expression of the miRNA in the test sample.

[0021] An embodiment is also a method for diagnosing cancer (such as lung adenocarcinoma) or determining the prognosis of a test subject having cancer, comprising: a) measuring the expression levels of two or more miRNAs in a buffy coat obtained from the blood of a subject having cancer; b) measuring the expression levels of two or more miRNAs in a buffy coat obtained from the blood of a sample of a healthy subject; c) comparing the expression levels of two or more miRNAs in a buffy coat obtained from the blood of a sample of a subject having cancer with the levels in a plasma sample of a healthy subject; d) identifying miRNAs whose expression levels have changed in a buffy coat obtained from the blood of a sample of a subject having cancer; e) generating a biomarker fingerprint from the miRNAs whose expression levels have changed; and f) diagnosing the cancer of the test subject or determining the prognosis of the cancer by comparing the level of miRNA from the plasma of the test subject with the levels in the biomarker fingerprint.

[0022] An embodiment is also a diagnostic kit for diagnosing cancer, comprising a plurality of nucleic acid molecules, each nucleic acid molecule encoding an miRNA sequence. The nucleic acid molecules identify fluctuations in the expression levels of one or more miRNAs in a plasma sample of a test subject. The expression levels of the one or more miRNAs can represent a nucleic acid expression fingerprint indicative of the presence of cancer. The kit can identify one or more target cells presenting lung cancer in the plasma of a test subject.

[0023] An embodiment is also a method for identifying one or more mammalian target cells presenting cancer, comprising: a) collecting a blood sample from a test control; b) hybridizing at least one nucleic acid molecule biomarker encoding an miRNA sequence to a portion of the blood sample; c) quantifying miRNA expression; d) determining the expression levels of a plurality of nucleic acid molecules, each nucleic acid molecule encoding an miRNA sequence; e) determining the expression levels of the plurality of nucleic acid molecules in one or more control cells; and f) identifying, from the plurality of nucleic acid molecules, one or more nucleic acid molecules differentially expressed in the target cells and the control cells by comparing the respective expression levels obtained in steps (d) and (e). One or more differentially expressed nucleic acid molecules can together represent a nucleic acid expression biomarker fingerprint indicating the presence of lung cancer.

Brief Description of the Drawings

[0024]

Figure 1

[0025] Definitions References to "an embodiment / aspect" or "embodiments / aspects" herein mean that the particular features, structures, or characteristics described in connection with that embodiment / aspect are included in at least one embodiment / aspect of the present disclosure. The use of the phrases "in one embodiment / aspect" or "in another embodiment / aspect" in various places in this specification does not necessarily refer to all the same embodiment / aspect, nor do they refer to separate or alternative embodiments / aspects that mutually exclude each other. Further, various features are described that may be shown by some embodiments / aspects but not by others. Similarly, various requirements are described that may be requirements of some embodiments / aspects but not of others. Embodiments and aspects can be used interchangeably in certain instances.

[0026] The terms used in this specification generally have their ordinary meanings in the context of the present disclosure and in the particular context in which each term is used. Specific terms used to describe the present disclosure are discussed below or elsewhere in this specification to provide additional guidance to practitioners with respect to the description of the present disclosure. It should be understood that the same thing can be said in multiple ways.

[0027] Accordingly, alternative languages and synonyms can be used for any one or more of the terms discussed in this specification. Also, the fact that a term is detailed or discussed in this specification has no special significance. Synonyms for particular terms are provided. The citation of one or more synonyms does not exclude the use of other synonyms. The use of examples anywhere in this specification, including examples of any of the terms discussed herein, is for illustration only and is not intended to further limit the scope and meaning of the present disclosure or any of the exemplified terms. Similarly, the present disclosure is not limited to the various embodiments given in this specification.

[0028] Without further intending to limit the scope of the present disclosure, examples of devices, apparatuses, methods, and the results associated therewith according to embodiments of the present disclosure are shown below. Note that the headings or subheadings may be used in the examples for the convenience of the reader and they are in no way intended to limit the scope of the disclosure. Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the technical field to which the present invention pertains. In case of conflict, this specification, including the definitions, will prevail.

[0029] When applicable, the terms "about" or "generally" as used herein in the specification and the appended claims mean a difference of + / - 20% unless otherwise specified. Also, when applicable, the term "substantially" as used herein in the specification and the appended claims means a difference of + / - 10% unless otherwise specified. It should be recognized that all uses of the above terms may not be quantifiable such that the referenced ranges are applicable.

[0030] The term "algorithm" refers to a specific set of instructions or a well - defined list of distinct instructions for performing a procedure, typically proceeding through a well - defined series of successive states and ultimately terminating in a final state.

[0031] The term "biomarker" generally refers to DNA, RNA, protein, carbohydrate, or glycolipid - based molecular markers, the expression or presence of which can be detected by standard methods (or methods disclosed herein) in a sample of a subject and which predict or indicate the effective responsiveness or susceptibility of a mammalian subject having cancer. A biomarker may be present in a test sample but not in a control sample, may not be present in a test sample but may be present in a control sample, or the amount of the biomarker may differ between a test sample and a control sample. For example, an evaluated genetic biomarker (e.g., a specific mutation and / or SNP) may be present in such a sample but not in a control sample, or a specific biomarker may be serum - positive in a sample but serum - negative in a control sample. Optionally, the expression of such a biomarker may also be determined to be higher than the expression observed for a control sample. The terms "marker" and "biomarker" are used interchangeably herein.

[0032] The amount of a biomarker can be measured in a test sample and compared to a "normal control level" using techniques such as reference limits, discrimination limits, or risk definition thresholds to define a cut-off point and outliers for lung cancer. A normal control level means the level of one or more biomarkers or a combined biomarker index typically seen in subjects not suffering from lung cancer. Such normal control levels and cut-off points can vary depending on whether the biomarker is used alone or as part of an index in combination with other biomarkers. Alternatively, the normal control level can be a database of biomarker patterns of previously tested subjects who have not experienced lung cancer over a clinically meaningful period.

[0033] After selecting a biomarker set, well-known techniques such as correlation, principal component analysis (PCA), factor rotation, logistic regression (LogReg), linear discriminant analysis (LDA), eigen-gene linear discriminant analysis (ELDA), support vector machine (SVM), random forest (RF), recursive partitioning tree (RPART), related decision tree classification techniques, shrinkage centroid method (SC), StepAIC, KNN (Kth-Nearest Neighbor), boosting, decision trees, neural networks, Bayesian networks, support vector machines, and hidden Markov models, linear regression or classification algorithms, non-linear regression or classification algorithms, analysis of variance (ANOVA), hierarchical analysis or clustering algorithms; hierarchical algorithms using decision trees; kernel-based machine algorithms such as kernel partial least squares algorithm, kernel matching pursuit algorithm, kernel Fisher's discriminant analysis algorithm, kernel principal component analysis algorithm, or other mathematical and statistical techniques can be used to develop a formula for calculating a risk score. If information on the values of biomarkers within a population and their clinical outcomes in terms of medical history is available, a selected population of individuals is used. When calculating the risk score for a specific individual, biomarker values are obtained from one or more samples collected from the individual and used as input data.

[0034] Tests for measuring biomarkers and biomarker panels can be implemented in various diagnostic test systems. A diagnostic test system is typically a device that includes means for obtaining test results from a biological sample. Examples of such means include modules that automate the tests (e.g., biochemical, immunological, nucleic acid detection assays). Some diagnostic test systems are designed to process multiple biological samples and can be programmed to perform the same test or different tests on each sample. A diagnostic test system typically includes means for collecting, storing, and / or tracking the test results for each sample, usually in a data structure or database. Examples include well-known physical and electronic data storage devices (e.g., hard drives, flash memories, magnetic tapes, paper printouts, etc.). Also, typically, a diagnostic test system includes means for reporting the test results. Examples of reporting means include visual displays, links to data structures or databases, or printers. The reporting means can be a data link for transmitting the test results to an external device such as a data structure, database, visual display, or printer.

[0035] As used herein, "additional biomedical information" refers to one or more evaluations of an individual other than using any of the biomarkers described herein that are relevant to lung cancer risk. Examples of "additional biomedical information" include any of the following: physical descriptors of the individual, physical descriptors of lung nodules observed by CT imaging, the height and / or weight of the individual, the gender of the individual, the ethnicity of the individual, smoking history, occupational history, exposure to known carcinogens (e.g., exposure to any of asbestos, radon gas, chemicals, smoke from fires, and air pollution, which may include emissions from stationary or mobile sources such as industrial / factory or automobile / marine / aircraft emissions), exposure to secondhand smoke, family history of lung cancer (or other cancers), presence of lung nodules, size of nodules, location of nodules, morphology of nodules (e.g., as observed by CT imaging, ground-glass opacity (GGO), solid, non-solid), edge characteristics of nodules (e.g., smooth, lobulated, sharply marginated, spiculated, infiltrative), etc. Smoking history is typically quantified in terms of "pack-years". This is the number of years a person has smoked multiplied by the average number of packs of cigarettes smoked per day. For example, a person who has smoked an average of 1 pack of cigarettes per day for 35 years is said to have a smoking history of 35 pack-years. Additional biomedical information can be obtained from an individual using routine techniques known in the art, e.g., using a routine patient questionnaire or health history questionnaire, or from a physician, etc. Alternatively, additional biomedical information can be obtained from routine imaging techniques such as CT imaging (e.g., low-dose CT imaging) and X-rays. A biomarker-level test combined with the evaluation of any additional biomedical information can improve the sensitivity, specificity, and / or AUC for the detection of lung cancer (or other lung cancer-related uses), as compared to, for example, a biomarker test alone or the evaluation of only any specific item of additional biomedical information (e.g., CT imaging only).

[0036] The term "area under the curve" or "AUC" refers to the area under the receiver operating characteristic (ROC) curve, both of which are well-known in the art. The AUC measurement is useful for comparing the accuracy of classifiers over the full data range. A classifier with a high AUC has a high ability to correctly classify unknowns between two target groups (e.g., lung cancer samples and normal or control samples). The ROC curve is useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and / or any item of additional biomedical information) when distinguishing between two populations (e.g., cases with lung cancer and controls without lung cancer). Typically, the feature data for the entire population (such as cases and controls) is sorted in ascending order based on the value of a single feature. Next, for each value of that feature, the true positive rate and false positive rate of the data are calculated. The true positive rate is determined by counting the number of cases that exceed the value of that feature and dividing by the total number of cases. The false positive rate is determined by counting the number of controls that exceed the value of that feature and dividing by the total number of controls. This definition refers to the scenario when the feature is high compared to the control, but this definition also applies to the scenario when the feature is low compared to the control (in such a scenario, samples below the value of that feature are counted). The ROC curve can be created for a single feature and a single other output, for example, by mathematically combining two or more features (e.g., addition, subtraction, multiplication, etc.) to provide a single total value, which can be plotted on the ROC curve. Further, any combination of multiple features that results in a single output value can be plotted on the ROC curve. These combinations of features may include tests. The ROC curve is a plot of the true positive rate (sensitivity) of the test against the false positive rate (1 - specificity) of the test.

[0037] As used herein, "detecting" or "determining" with respect to a biomarker value includes the use of both the equipment necessary to observe and record the signal corresponding to the biomarker value and the materials necessary to generate that signal. In various embodiments, the biomarker value is detected using any suitable method including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection, nuclear magnetic resonance, quantum dots.

[0038] The terms "fingerprint," "disease fingerprint," or "biomarker signature" refer to a plurality or pattern of biomarkers that have an elevated or decreased level in a subject having a disease. A fingerprint is generated by comparing a subject having a disease to a healthy subject and can be used for screening / diagnosis of the disease.

[0039] The term "miRNA" or "microRNA", "miRNA biomarker", or "MicroRNA" refers to small endogenous RNA molecules that can be used as serum diagnostic biomarkers for diseases including cancer. They are small non-coding RNA molecules (containing about 22 nucleotides) found in plants, animals, and some viruses and function in RNA silencing and post-transcriptional regulation of gene expression. miRNAs function through base pairing with complementary sequences within mRNA molecules. As a result, these mRNA molecules are silenced by one or more of the following processes: (1) cleavage into two small pieces of the mRNA strand, (2) destabilization of the mRNA by shortening of the poly(A) tail, and (3) reduction in the efficiency of translation of the mRNA into protein by ribosomes.

[0040] The terms "polypeptide", "peptide" and "protein" are used interchangeably herein and refer to polymers of amino acid residues. This term applies to amino acid polymers in which one or more amino acid residues are artificial chemical mimics of the corresponding naturally occurring amino acids, as well as to both naturally occurring and non-naturally occurring amino acid polymers. Methods for obtaining (e.g., producing, isolating, purifying, synthesizing, and manufacturing recombinantly) polypeptides are well known to those skilled in the art.

[0041] The term "amino acid" refers to naturally occurring and synthetic amino acids, as well as amino acid analogs and mimics that function in a manner similar to naturally occurring amino acids. Naturally occurring amino acids are those encoded by the genetic code, as well as those that are later modified, such as hydroxyproline, γ-carboxyglutamate, and O-phosphoserine. Amino acid analogs are compounds that have the same basic chemical structure as a naturally occurring amino acid, i.e., a carbon bonded to a hydrogen, a carboxyl group, an amino group, and an R group, such as homoserine, norleucine, methionine sulfoxide, and methionine methyl sulfonium. Such analogs have a modified R group (e.g., norleucine) or a modified peptide backbone, but retain the same basic chemical structure as a naturally occurring amino acid. Amino acid mimics refer to compounds that have a structure different from the general chemical structure of an amino acid, but function in the same manner as a naturally occurring amino acid.

[0042] Amino acids can be referred to herein by either their generally known three-letter notations or the one-letter notations recommended by the IUPAC-IUB Biochemical Nomenclature Commission. Similarly, nucleotides can be referred to by their generally accepted one-letter codes.

[0043] Amino acids and their derivatives may include cysteine, cystine, cysteine sulfoxide, allicin, selenocysteine, methionine, isoleucine, leucine, lysine, phenylalanine, threonine, tryptophan, 5-hydroxytryptophan, valine, arginine, histidine, alanine, asparagine, aspartic acid, glutamic acid, glutamine, glycine, proline, serine, tyrosine, ornithine, carnosine, citrulline, carnitine, ornithine, theanine, and taurine.

[0044] The proteins and peptides described herein may have amino acid substitutions that do not change the activity of the protein or peptide (H. Neurath, R. L. Hill, The Proteins, Academic Press, New York, 1979). In one embodiment, these substitutions are "conservative" amino acid substitutions. The most commonly occurring substitutions are Ala / Ser, Val / Ile, Asp / Glu, Thr / Ser, Ala / Gly, Ala / Thr, Ser / Asn, Ala / Val, Ser / Gly, Ala / Pro, Lys / Arg, Asp / Asn, Leu / Ile, Leu / Val, Ala / Glu and Asp / Gly in both directions.

[0045] With respect to "conservatively modified variants" of an amino acid sequence, one of ordinary skill in the art will recognize that individual substitutions, deletions or additions to a nucleic acid, peptide, polypeptide, or protein sequence that change, add, or delete a single amino acid or a small percentage of amino acids in the encoded sequence are "conservatively modified variants" where the change results in the substitution of an amino acid with a chemically similar amino acid. Tables of conservative substitutions providing functionally similar amino acids are well known in the art. Such conservatively modified variants exist in addition to and do not exclude the polymorphic variants, interspecies homologs, and alleles of the present invention.

[0046] The following eight groups each contain amino acids that are conservative substitutions of one another: 1) alanine (A), glycine (G); 2) aspartic acid (D), glutamic acid (E); 3) asparagine (N), glutamine (Q); 4) arginine (R), lysine (K); 5) isoleucine (I), leucine (L), methionine (M), valine (V); 6) phenylalanine (F), tyrosine (Y), tryptophan (W); 7) serine (S), threonine (T); and 8) cysteine (C), methionine (M) (see, e.g., Creighton, Proteins (1984)).

[0047] As used herein, an analog refers to a peptide, polypeptide, or protein sequence that differs from a reference peptide, polypeptide, or protein sequence. Such differences can be an addition, deletion, or substitution of an amino acid, phosphorylation, sulfation, acrylation, glycosylation, methylation, farnesylation, acetylation, amidation, use of non-natural amino acid structures, or other such modifications known in the art.

[0048] The term "agent" refers to an active drug for treating cancer, such as adenocarcinoma, or the signs or symptoms or side effects of cancer.

[0049] The term "plasma" or "blood plasma" refers to the liquid portion of blood that carries cells and proteins throughout the body. Plasma can be separated from blood by spinning a tube of fresh blood containing an anticoagulant in a centrifuge until the blood cells fall to the bottom of the tube.

[0050] The term "PCR" or "polymerase chain reaction" refers to a common method used to create many copies of a specific DNA segment. Variant forms of the technique can be used to determine the presence and amount of one or more miRNAs in a sample. For example, a hydrolysis probe-based stem-loop quantitative reverse transcription PCR (RT-qPCR) assay can be performed to confirm and / or quantify the concentration of selected miRNAs in patient and control serum samples.

[0051] The term "sample" refers to a biological sample obtained from an individual, body fluid, body tissue, cell line, tissue culture, or other source. Body fluids include, for example, lymph, serum, fresh whole blood, peripheral blood mononuclear cells, frozen whole blood, plasma (fresh or frozen, etc.), urine, saliva, semen, synovial fluid, and cerebrospinal fluid. Samples also include synovial tissue, skin, hair follicles, and bone marrow. Methods for obtaining tissue biopsies and body fluids from mammals are well known in the art.

[0052] The term "subject" or "patient" refers to any single animal in which treatment is desired, more preferably a mammal (including non-human animals such as dogs, cats, horses, rabbits, zoo animals, cows, pigs, sheep, and non-human primates). Most preferably, the patients herein are humans.

[0053] The term "prognosis" refers to the predicted or expected outcome of a disease. As used herein, it refers to the possible outcome of a liver disease, including whether the disease (e.g., lung cancer) responds to treatment or palliative efforts and / or the likelihood of the disease progressing.

[0054] As used herein, "optional" or "optionally" means that the subsequently described situation may or may not occur, and thus this description includes both the case where the situation occurs and the case where it does not occur.

[0055] When used in calculations, the notation "array number 1" represents the value of the amount of array number 1 present in the sample (i.e., the amount of mRNA sequences containing array number 1). As can be understood, this notation can include other numerical expressions.

[0056] Other technical terms used herein have their ordinary meanings in the art in which they are used, as exemplified by various technical dictionaries. The specific values and configurations discussed in these non-limiting examples can vary and are cited merely to illustrate at least one embodiment and are not intended to limit the scope. Detailed Description of the Invention

[0057] It should be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the claimed subject technology. Additional features and advantages of the subject technology are described in the following description, and will be apparent in part from the description, or can be learned by practice of the subject technology. The advantages of the subject technology will be realized and achieved by the structures particularly pointed out in the written description and claims of this specification.

[0058] MicroRNAs (miRNAs) are small non-coding RNA molecules that function in RNA silencing and post-transcriptional regulation of gene expression. Recent research has demonstrated the presence of miRNAs in circulating blood and the potential use of miRNAs as biomarkers in the diagnosis of various diseases. Specifically, attempts have been proposed to use them as biomarkers for the early detection of cancers such as lung adenocarcinoma. Studies have demonstrated the potential of miRNA expression profiling in the diagnosis and prognosis of human lung cancer. Certain miRNAs are abnormally expressed in malignant tissues compared to non-malignant lung tissues. Thus, such miRNAs can provide insights into the cellular processes involved in cancer malignant transformation and progression.

[0059] Conventional methods for diagnosing the initial stage of lung cancer are generally unreliable. In chest X-ray examinations and imaging tests, lung cancer is diagnosed at an advanced stage. Biopsies are invasive and rely on subjective observations, so there is a high possibility of errors. The inventors have discovered that there are differences in mRNA expression between healthy patients and patients with lung cancer. Certain mRNAs are abnormally expressed in diseased lungs compared to healthy lungs. Also, certain miRNAs are expressed at different levels in diseased and healthy lungs. mRNA / miRNA can be detected in the patient's blood, serum, or plasma. Therefore, the present invention is based in part on the discovery that liver diseases can be reliably identified and different subtypes of liver diseases can be distinguished based on specific mRNA / miRNA expression profiles with high sensitivity and specificity.

[0060] The present invention is based on the discovery that lung cancer can be reliably identified and different subtypes of lung cancer can be discriminated based on specific miRNA expression profiles with high sensitivity and specificity. The expression of biomarkers typically includes both upregulated and downregulated miRNA levels. However, some useful biomarkers do not have variable expression levels. Biomarkers can be used as a normalization group for batch effects or other technical variations in sample handling, sample preparation, sample extraction, biomarker measurement, instrument data processing, etc. Such biomarkers may or may not indicate cancer (in other words, some normalization groups retain diagnostic information, while others are used simply to adjust for technical variations in biomarker measurement).

[0061] Embodiments include biomarker sets for the diagnosis, prognosis determination, and / or treatment of lung cancer. The methods described herein may include the combined measurement of at least two mRNA / miRNA / protein / peptide biomarkers and / or fragments of protein biomarkers as referenced in Tables 1 and 2 from human serum, plasma, or blood preparations, or from the blood itself.

[0062] Embodiments also include diagnostic markers or molecular fingerprint sets for the rapid and reliable identification and / or treatment of cells presenting or having a predisposition to develop different subtypes of lung cancer. Embodiments further include methods of diagnosing cancer based on specific miRNAs having altered expression levels. While individual miRNAs can be monitored, the present invention includes 29 miRNAs of specific values as biomarkers for screening or differentiating healthy individuals from diseased individuals. Particularly targeted miRNAs include hsa-miR-1204, hsa-miR-141-3p, hsa-miR-1827, hsa-miR-938, hsa-miR-125b-5p, hsa-miR-297, hsa-miR-10a-5p, hsa-miR-145-5p, hsa-miR-217, hsa-miR-3185, hsa-miR-21-5p, hsa-miR-363-3p, hsa-miR-63, hsa-miR-655, hsa-miR-1245b-5p, hsa-miR-369-3p, hsa-miR-875-3p, hsa-miR-105-5p, hsa-miR-1253, hsa-miR-1285-3p, hsa-miR-512-5p, hsa-miR-550b-3p, hsa-miR-571, hsa-miR-935, hsa-miR-145-5p, hsa-miR-3185, hsa-miR-1285-3p, hsa-miR-125b-5p, and hsa-miR-550b-3p.

[0063] The methods and materials can be used to evaluate cancer subjects (e.g., human patients) such as lung adenocarcinoma. For example, embodiments include materials and methods for a physician to use markers that can be identified to assist in evaluating adenocarcinoma disease activity, assessing the likelihood and outcome of treatment, and predicting long-term disease outcome. Further, a subject having adenocarcinoma can be diagnosed based on the presence of specific diagnostic indicators in the subject's plasma. This technique provides a diagnostic method for predicting and / or foreseeing the effectiveness of treatment. In particular, the subject technology relates to the diagnosis of adenocarcinoma based on one or more combinations of markers.

[0064] Multiple miRNA biomarkers can be used from a single serum sample collected from a subject. According to some embodiments, multiple biomarkers are evaluated and measured from different samples collected from a patient. According to some embodiments, the subject technology is used in a kit for predicting, diagnosing, or monitoring cancer treatment or treatment responsiveness, and the kit is calibrated to measure marker levels in a sample from a patient.

[0065] According to some embodiments, the amount of a biomarker can be determined, for example, by using a reagent that specifically binds to the biomarker protein or a fragment thereof (e.g., an antibody, an antibody fragment, or an antibody derivative). The expression level can be determined using methods common in the art such as proteomics, flow cytometry, immunocytochemistry, immunohistochemistry, enzyme-linked immunosorbent assay, multi-channel enzyme-linked immunosorbent assay, and variants thereof. The expression level of a biomarker in a biological sample can also be determined by detecting the expression level of a transcribed biomarker polynucleotide or a fragment thereof that can be cDNA, mRNA, or heteronuclear RNA (hnRNA) encoded by a biomarker gene. The detecting step can include amplifying the transcribed biomarker polynucleotide, and a method of quantitative reverse transcriptase polymerase chain reaction can be used. The expression level of a biomarker can be evaluated by detecting the presence of the transcribed biomarker polynucleotide or a fragment thereof in a sample using a probe that anneals to the transcribed biomarker polynucleotide or a fragment thereof under stringent hybridization conditions.

[0066] Compositions and kits for practicing this method are also provided herein. For example, in some embodiments, reagents specific to one or more markers (e.g., primers, probes) are provided alone or in sets (e.g., sets of primer pairs for amplifying multiple markers). Additional reagents for performing detection assays (e.g., enzymes, buffers, positive and negative controls for performing QuARTS, PCR, sequencing, bisulfite, or other assays) may also be provided. In some embodiments, kits are provided that include one or more reagents necessary, sufficient, or useful for practicing the method. Reaction mixtures containing the reagents are also provided. Further, master mix reagent sets are provided that include a plurality of reagents that can be added to one another and / or to a test sample to complete the reaction mixture.

[0067] In some embodiments, the techniques described herein relate to a programmable machine designed to perform a series of arithmetic or logical operations as provided by the methods described herein. For example, some embodiments of the techniques are associated with (e.g., implemented in) computer software and / or computer hardware. In one aspect, the techniques relate to a computer that includes, for reading, manipulating, and storing data, a form of memory, elements for performing arithmetic and logical operations, and a processing element (e.g., a microprocessor) for executing a series of instructions (e.g., the methods provided herein). Thus, certain embodiments use a process that includes data stored in or transferred through one or more computer systems or other processing systems. The embodiments disclosed herein also relate to an apparatus for performing these operations. This apparatus can be specially constructed for the required purposes or can be a general-purpose computer (or group of computers) selectively activated or reconfigured by a computer program and / or data structures stored in the computer. In some embodiments, a group of processors cooperatively (e.g., via a network or cloud computing) and / or in parallel perform some or all of the recited analysis operations.

[0068] In some embodiments, the microprocessor determines the presence of one or more miRNAs (plural) related to cancer (labeled herein as hsa-miR or has-miR); generates a standard curve; determines the specificity and / or sensitivity of an assay or marker; calculates an ROC curve; and is part of a system for performing sequence analysis, all as described herein or known in the art.

[0069] In some embodiments, the microprocessor determines the amount, e.g., concentration, of one or more miRNAs related to cancer (labeled herein as hsa-miR or has-miR); generates a standard curve; determines the specificity and / or sensitivity of an assay or marker; calculates an ROC curve; and is part of a system for performing sequence analysis, all as described herein or known in the art. The amount of one or more miRNAs or multiple miRNAs can be determined by the abundance measured per mole or millimole. The amount of miRNA or multiple miRNAs can be determined by fluorescence, other measurements using optical signals, or other measurements known to those skilled in the art for measuring miRNA or multiple miRNAs.

[0070] In some embodiments, the microprocessor or computer uses methylation state data in an algorithm to predict the type or location of cancer.

[0071] In some embodiments, the microprocessor or computer uses an algorithm to measure the amount of miRNA or multiple miRNAs. The algorithm can include, but is not limited to, mathematical interactions between marker measurements or mathematical transformations of marker measurements. The mathematical interactions and / or mathematical transformations can be presented in linear, non-linear, discontinuous, or discrete ways.

[0072] In some embodiments, software or hardware components receive the results of multiple assays and determine and report to the user a single value result indicating the risk of cancer based on the results of the multiple assays. Related embodiments calculate risk factors based on mathematical combinations (e.g., weighted combinations, linear combinations) of results from multiple assays as disclosed herein.

[0073] Some embodiments include a memory medium and memory components. The memory components (e.g., volatile and / or non-volatile memory) are used when storing instructions (e.g., embodiments of the processes provided herein) and / or data (e.g., workpieces such as methylation measurements, arrays, and statistical descriptions related thereto). Some embodiments relate to a system comprising one or more of a CPU, a graphics card, and a user interface (e.g., including an output device such as a display and an input device such as a keyboard).

[0074] Programmable machines related to the technology include conventional existing technologies and technologies under development or planned for development (e.g., quantum computers, chemical computers, DNA computers, optical computers, spinronics-based computers, etc.).

[0075] In some embodiments, the technology includes wired (e.g., metal cables, optical fibers) or wireless transmission media for transmitting data. For example, some embodiments relate to data transmission via a network (e.g., a local area network (LAN), a wide area network (WAN), an ad hoc network, the Internet, etc.). In some embodiments, the programmable machine exists on a network such as a peer, and in some embodiments, the programmable machine has a client / server relationship.

[0076] In some embodiments, the data is stored on a computer-readable storage medium such as a hard disk, flash memory, memory stick, optical disk, floppy disk, etc.

[0077] In some embodiments, the techniques provided herein are associated with a plurality of programmable devices that operate in concert to execute the methods described herein. For example, in some embodiments, when implementing some other distributed computer architectures that rely on multiple computers (e.g., connected by a network) to operate in parallel to collect and process data, such as in cluster computing, grid computing, or conventional network interfaces such as Ethernet, fiber optic, or wireless network technologies to connect to a network (private, public, or the Internet), complete computers (with on-board CPUs, storage, power supplies, network interfaces, etc.).

[0078] For example, some embodiments provide a computer comprising a computer-readable medium. This embodiment includes a random access memory (RAM) coupled to a processor. The processor executes computer-executable program instructions stored in the memory. Such processors can include microprocessors, ASICs, state machines, or other processors, and can be any of several computer processors such as those of Intel Corporation of Santa Clara, Calif and Motorola Corporation of Schaumburg, Ill. Such processors can include or communicate with a medium, such as a computer-readable medium, that stores instructions that, when executed by the processor, cause the processor to perform the steps described herein.

[0079] Examples of computer-readable media include, but are not limited to, electronic, optical, magnetic, or other memory or transmission devices that can provide computer-readable instructions to a processor. Other examples of suitable media include, but are not limited to, floppy disks, CD-ROMs, DVDs, magnetic disks, memory chips, ROMs, RAMs, ASICs, configured processors, all optical media, all magnetic tapes or other magnetic media, or any other media from which a computer processor can read instructions. Also, various other forms of computer-readable media including routers, private or public networks, or other transmission devices or channels, both wired and wireless, can transmit or carry instructions to a computer. The instructions can include code from any suitable computer programming language including, for example, C, C++, C#, Visual Basic, Java, Python, Perl, and JavaScript.

[0080] The computer is connected to a network in some embodiments. The computer may also include a plurality of external or internal devices such as a mouse, CD-ROM, DVD, keyboard, display, or other input or output devices. Examples of computers are personal computers, digital assistants, personal digital assistants, cellular phones, mobile phones, smartphones, pagers, digital tablets, laptop computers, Internet appliances, and other processor-based devices. In general, a computer related to the aspects of the technology provided herein may be any type of processor-based platform operating on any operating system such as Microsoft Windows, Linux, UNIX, Mac OS X that is capable of supporting one or more programs including the technology provided herein. Some embodiments include a personal computer that executes other application programs (e.g., applications). The application may be stored in memory and may include, for example, a word processing application, a spreadsheet application, an email application, an instant messenger application, a presentation application, an Internet browser application, a calendar / organizer application, and any other application executable by a client device. All such components, computers, and systems described herein as related to the technology may be logical or virtual, whether physical or not.

[0081] Embodiments are also contemplated to be achieved as computer signals embodied in a carrier wave, as well as signals propagated through a transmission medium (e.g., electrical and optical). Thus, the various types of information discussed above may be formatted into a structure such as a data structure and transmitted as an electrical signal through a transmission medium or stored on a computer-readable medium.

[0082] In some embodiments, the present disclosure provides a system for predicting the progression of lung cancer. In another embodiment, the lung cancer is squamous cell lung cancer (e.g., non-small cell squamous lung cancer or non-small cell undifferentiated large cell lung cancer), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer. In one embodiment, lung cancer including any of the aforementioned lung cancers can be identified and lung cancer can be predicted in an individual. The system includes an apparatus configured to determine the expression levels of nucleic acids, proteins, peptides, or other molecules derived from a biological sample collected from an individual; and hardware logic designed or configured to perform operations including (a) receiving the expression levels of a collection of signature genes of a biological sample collected from the individual, wherein the collection of signature genes includes at least two genes selected from the group consisting of the sequences listed in Table 1 or the mi-RNAs listed in Table 2.

[0083] Information related to the diagnosis of a patient includes, but is not limited to, age, ethnicity, tumor location, past medical history directly related to co-existing diseases, other oncological history, family history of cancer, physical examination findings, radiological findings, biopsy date, biopsy results, type of surgery performed (radical perineal or radical retropubic prostatectomy), neoadjuvant therapy (i.e., chemotherapy, hormone), adjuvant or salvage radiation therapy, hormone therapy, local to distant disease recurrence, and survival outcomes. These clinical variables may be included in the prediction model in various embodiments.

[0084] When a biomarker or biomarker panel is selected, a method for diagnosing an individual who may have lung cancer. In one embodiment, a biomarker or biomarker panel is selected, and a method for diagnosing an individual who may have lung cancer such as squamous cell lung cancer (e.g., non-small cell lung squamous cell carcinoma or non-small cell lung undifferentiated large cell carcinoma), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer and / or adenocarcinoma lung cancer includes the following steps: 1) collecting or obtaining by other means a biological sample; 2) performing an analytical method for simultaneously detecting and measuring the biomarkers within the panel in the biological sample; 3) performing any normalization or standardization of any data required by the method used to collect biomarker values; 4) calculating a biomarker score; 5) combining the biomarker scores to obtain a total diagnostic score; and 6) reporting the diagnostic score of the individual, and may include one or more of these. This diagnostic method can be implemented using a computer and software program to analyze data collected from nucleic acids, proteins, peptides, or other biomolecules. In this approach, the diagnostic score can be a single numerical value obtained from the sum of all marker calculations, which is compared to a pre-set threshold indicating the presence or absence of the disease. Or, the diagnostic score may be a series of bars each representing a biomarker value, and the pattern of response may be compared to a pre-set pattern for determining the presence or absence of the disease.

[0085] For both DNA and RNA, the nucleic acids can be isolated from plasma or blood samples. DNA or RNA can be extracellular or can be extracted from cells in plasma or blood samples. DNA or RNA can also be extracted from a cell biopsy including tumors including solid tumors in the lung.

[0086] In the case of proteins, peptides, or other biomolecules, they can be isolated from plasma or blood samples. The protein, peptide, or other biomolecule can be extracellular or can be extracted from cells in the plasma or blood sample. The protein, peptide, or other biomolecule can also be extracted from a cell biopsy containing tumors, including solid tumors in the lung.

[0087] It should also be noted that many of the structures, materials, and acts described herein can be described as means for performing functions or steps for performing functions. Thus, such language is intended to cover all such structures, materials, or acts and their equivalents disclosed herein, including matters incorporated by reference.

[0088] A lung cancer biomarker analysis system, including squamous cell lung cancer (e.g., non-small cell lung squamous cell carcinoma or non-small cell lung undifferentiated large cell carcinoma), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer, can provide functions and operations for completing data analysis such as data collection, processing, analysis, reporting, and / or diagnosis. For example, in one embodiment, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information related to adenocarcinoma biomarkers. The computer program can include multiple modules that perform various functions or operations, such as a processing module for processing raw data to generate supplementary data, and an analysis module for analyzing the raw data and supplementary data to generate the status and / or diagnosis of adenocarcinoma. Diagnosing the status of adenocarcinoma can include generating or collecting any other information, including additional biomedical information related to the status of an individual with the disease, identifying whether further tests may be desirable, or otherwise assessing the health status of the individual.

[0089] A lung cancer biomarker analysis system including squamous cell lung cancer (e.g., non-small cell lung squamous cell carcinoma or non-small cell lung undifferentiated large cell carcinoma), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer and / or adenocarcinoma lung cancer can provide functions and operations for completing data analysis such as data collection, processing, analysis, reporting and / or diagnosis. For example, in one embodiment, a computer system can execute a computer program that can receive, store, retrieve, analyze, and report information related to lung cancer biomarkers. The computer program can include a plurality of modules that perform various functions or operations, such as a processing module for processing raw data to generate supplementary data, and an analysis module for analyzing the raw data and the supplementary data to generate a state and / or diagnosis of lung cancer. Diagnosing the state of lung cancer can include generating or collecting any other information including additional biomedical information related to the state of the individual with the disease, identifying whether further tests may be desirable, or otherwise evaluating the health state of the individual.

[0090] As used herein, "computer program product" refers to an organized series of instructions in the form of natural or programming language statements stored on a physical medium of any nature (e.g., written, electronic, magnetic, optical, or other) and capable of being used in a computer or other automated data processing system. Such programming language statements, when executed by a computer or data processing system, cause the computer or data processing system to operate in accordance with the specific content of the statements. A computer program product includes, but is not limited to, programs in source code and object code, and / or inspection or data libraries embedded in a computer-readable medium. Further, a computer program product that enables a computer system or data processing device to operate in a preselected manner includes, but is not limited to, the original source code, assembly code, object code, machine language, the encrypted or compressed versions described above, and any and all equivalents, and may be provided in multiple forms.

[0091] In one embodiment, a computer program product is provided for indicating the likelihood of lung cancer including squamous cell lung cancer (e.g., non-small cell lung squamous cell cancer or non-small cell lung undifferentiated large cell lung cancer), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer. The computer program product includes a computer-readable medium embodying program code executable by a processor of a computing device or system, the program code including code for retrieving data resulting from a biological sample from an individual, the data including biomarker values each corresponding to at least one of at least N biomarkers in a biological sample selected from the group of biomarkers provided in Table 1 or the mi-RNAs identified in Table 2, and code for performing a classification method indicating the individual's adenocarcinoma status as a function of the biomarker values.

[0092] In yet another embodiment, a computer program product is provided for indicating the likelihood of lung cancer including squamous cell lung cancer (e.g., non-small cell lung squamous cell carcinoma or non-small cell lung undifferentiated large cell carcinoma), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer. The computer program product includes a computer-readable medium embodying program code executable by a processor of a computing device or system, the program code including code for retrieving data resulting from a biological sample from an individual, the data including biomarker values corresponding to biomarkers selected from the group of biomarkers provided in Table 1 or miRNAs described in Table 2 in the biological sample, and code for performing a classification method showing the adenocarcinoma status of the individual as a function of the biomarker values.

[0093] A kit (i.e., a diagnostic kit) can include reagents for determining the amount of miRNA or mutations in a gene from a plasma sample of a subject, based on an assay of nucleic acids, proteins, peptides, or other biological molecules isolated from lung cancer including squamous cell lung cancer (e.g., non-small cell lung squamous cell carcinoma or non-small cell lung undifferentiated large cell carcinoma), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer, circulating cells or remnants of circulating cells (such as proteins, peptides, or other biological molecules) present in plasma. The nucleic acids can be deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and / or artificial nucleic acids including artificial nucleic acid analogs. In addition to miRNAs, other RNAs include non-coding RNA (ncRNA), transfer RNA (tRNA), messenger RNA (mRNA), small interfering RNA (siRNA), piwi RNA (piRNA), small nucleolar RNA (snoRNA), small nuclear (snRNA), extracellular RNA (exRNA), and ribosomal RNA (rRNA).

[0094] The disclosed methods and assays provide a convenient, efficient, and potentially cost-effective means for obtaining data and information useful for evaluating appropriate or effective treatment for a patient. The kits can use conventional methods to detect biomarkers whether the protein, peptide, other biomolecule, or RNA or DNA being evaluated includes a protocol for examining the presence and / or expression of a desired nucleic acid, e.g., SNP, in a sample. Tissue or cell samples from mammals can be readily assayed for gene marker RNA, in one embodiment miRNA or DNA, for example, using Northern, dot blot, or polymerase chain reaction (PCR) analysis, array hybridization, RNase protection assay, or using a commercially available DNA SNP chip microarray such as DNA microarray snapshot. Real-time PCR (RT-PCR) assays, such as quantitative PCR assays, are well known in the art.

[0095] The probes used in PCR can be labeled with a detectable marker such as, for example, a radioisotope, a fluorescent compound, a bioluminescent compound, a chemiluminescent compound, a metal chelator, or an enzyme. Such probes and primers can be used as a means for detecting the presence of mutations in DNA, RNA, and in one embodiment, miRNA in a sample and for detecting cells that express miRNA. As will be appreciated by those skilled in the art, a very large number of different primers and probes can be prepared based on known sequences and can be effectively used to amplify, clone, and / or determine the presence and / or level of miRNA.

[0096] Another DNA test for determining whether a mutation is present is fluorescence in situ hybridization (FISH). By using FISH, mutations can be detected in cells, tissues including lung tissue, including tumors, and even including lung tumors.

[0097] The identification of proteins or peptides can be performed via the use of Western blotting using techniques known in the art.

[0098] Other methods include protocols for examining or detecting mutations in DNA or RNA. These other methods include protocols for examining or detecting miRNAs in tissue or cell samples by microarray technology. In one embodiment, using a nucleic acid microarray, test and control RNAs, including miRNA samples from test and control tissue samples, are reverse transcribed and labeled to generate cDNA probes. The probes are then hybridized to an array of nucleic acids immobilized on a solid support. The array is configured such that the sequence and position of each member of the array are known. For example, selected genes having the potential to be expressed in a particular disease state can be arranged on the solid support. Hybridization of the labeled probe to a particular array member indicates that the sample from which the probe is derived expresses that gene. Differential gene expression analysis of diseased tissue can provide valuable information. Microarray technology utilizes nucleic acid hybridization technology and computing technology to evaluate the mRNA expression profiles of thousands of genes in a single experiment.

[0099] Biomarkers are particularly useful for the diagnosis of cancer because their expression patterns differ when comparing healthy subjects to subjects with lung adenocarcinoma. The expression of biomarkers typically includes both upregulated and downregulated miRNA levels.

[0100] For other forms of cancer, additional biomarkers are shown in Table 1 below. In one embodiment, the biomarkers described herein can determine whether a patient has lung cancer or does not have lung cancer. In one embodiment, each is a form of lung cancer. In another embodiment, cancer, and in one embodiment, lung cancer is squamous cell lung cancer (e.g., non-small cell lung squamous cancer or non-small cell lung undifferentiated large cell lung cancer), non-small cell non-adenocarcinoma lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, adenocarcinoma lung cancer.

[0101] In one embodiment, the following biomarkers can be detected in DNA, including one or more mutations, snps related to regions of genes, or one or more mutations on one or more chromosomes. In one embodiment, the following biomarkers can also be detected in RNA such as miRNA, tRNA, mRNA or other forms of RNA.

Table 1

[0102] In addition to RT-PCR or another PCR-based method, other methods for determining the level of a biomarker include proteomics techniques and individualized gene profiles. The individualized gene profile can be used for the diagnosis, prognosis determination, and / or treatment of lung cancer based on the patient's response at the molecular level. The special microarrays herein (e.g., oligonucleotide microarrays or cDNA microarrays) can include one or more biomarkers having an expression profile correlated with either sensitivity or resistance to one or more antibodies.

[0103] One biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 biomarkers can be stored in liquid or dry form (such as after lyophilization).If a combination of one biomarker or two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty - one, twenty - two, twenty - three, twenty - four, twenty - five, twenty - six, twenty - seven, twenty - eight, twenty - nine, thirty, thirty - one, thirty - two, thirty - three, thirty - four, thirty - five, thirty - six, thirty - seven, thirty - eight, thirty - nine, forty, forty - one, forty - two, forty - three, forty - four, forty - five, forty - six, forty - seven, forty - eight, forty - nine, fifty, fifty - one, fifty - two, fifty - three, fifty - four, fifty - five, fifty - six, fifty - seven, fifty - eight, fifty - nine, sixty, sixty - one, sixty - two, sixty - three, sixty - four, sixty - five, sixty - six, sixty - seven, sixty - eight, sixty - nine, seventy, seventy - one, seventy - two, seventy - three, seventy - four, seventy - five, seventy - six, seventy - seven, seventy - eight, seventy - nine, eighty, eighty - one, eighty - two, eighty - three, eighty - four, eighty - five, eighty - six, eighty - seven, eighty - eight, eighty - nine, ninety, ninety - one, ninety - two, ninety - three, ninety - four, ninety - five, ninety - six, ninety - seven, ninety - eight, ninety - nine, one hundred, one hundred and one, one hundred and two, one hundred and three, one hundred and four, one hundred and five, one hundred and six, one hundred and seven, one hundred and eight, one hundred and nine, one hundred and ten, one hundred and eleven, one hundred and twelve, one hundred and thirteen, one hundred and fourteen, one hundred and fifteen, one hundred and sixteen, one hundred and seventeen, one hundred and eighteen, one hundred and nineteen, one hundred and twenty, one hundred and twenty - one, one hundred and twenty - two, one hundred and twenty - three, one hundred and twenty - four, one hundred and twenty - five, one hundred and twenty - six and / or one hundred and twenty - seven biomarkers is stored in a dry state, the combination of one biomarker or two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty - one, twenty - two, twenty - three, twenty - four, twenty - five, twenty - six, twenty - seven, twenty - eight, twenty - nine, thirty, thirty - one, thirty - two, thirty - three, thirty - four, thirty - five, thirty - six, thirty - seven, thirty - eight, thirty - nine, forty, forty - one, forty - two, forty - three, forty - four, forty - five, forty - six, forty - seven, forty - eight, forty - nine, fifty, fifty - one, fifty - two, fifty - three, fifty - four, fifty - five, fifty - six, fifty - seven, fifty - eight, fifty - nine, sixty, sixty - one, sixty - two, sixty - three, sixty - four, sixty - five, sixty - six, sixty - seven, sixty - eight, sixty - nine, seventy, seventy - one, seventy - two, seventy - three, seventy - four, seventy - five, seventy - six, seventy - seven, seventy - eight, seventy - nine, eighty, eighty - one, eighty - two, eighty - three, eighty - four, eighty - five, eighty - six, eighty - seven, eighty - eight, eighty - nine, ninety, ninety - one, ninety - two, ninety - three, ninety - four, ninety - five, ninety - six, ninety - seven, ninety - eight, ninety - nine, one hundred, one hundred and one, one hundred and two, one hundred and three, one hundred and four, one hundred and five, one hundred and six, one hundred and seven, one hundred and eight, one hundred and nine, one hundred and ten, one hundred and eleven, one hundred and twelve, one hundred and thirteen, one hundred and fourteen, one hundred and fifteen, one hundred and sixteen, one hundred and seventeen, one hundred and eighteen, one hundred and nineteen, one hundred and twenty, one hundred and twenty - one, one hundred and twenty - two, one hundred and twenty - three, one hundred and twenty - four, one hundred and twenty - five, one hundred and twenty - six and / or one hundred and twenty - seven biomarkers can be resuspended using water or a solution.A person skilled in the art would know that it would result in a stable resuspension of one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 biomarkers. miRNA as a biomarker

[0104] Table 2 includes a list of miRNA biomarkers.

Table 2

[0105] Diagnostic method using miRNA as a biomarker One or more biomarkers can be used in a method of diagnosing cancer or in a method of determining the prognosis of a test subject having cancer. In this way, a single biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 biomarkers can be used in a method of diagnosing cancer or in a method of determining the prognosis of a test subject having cancer.In this way, at least one biomarker or a combination of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 biomarkers can be used in a method for diagnosing cancer or in a method for determining the prognosis of a test subject having cancer.

[0106] In this way, a combination of one or fewer biomarkers or two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty-six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety-four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred and one, one hundred and two, one hundred and three, one hundred and four, one hundred and five, one hundred and six, one hundred and seven, one hundred and eight, one hundred and nine, one hundred and ten, one hundred and eleven, one hundred and twelve, one hundred and thirteen, one hundred and fourteen, one hundred and fifteen, one hundred and sixteen, one hundred and seventeen, one hundred and eighteen, one hundred and nineteen, one hundred and twenty, one hundred and twenty-one, one hundred and twenty-two, one hundred and twenty-three, one hundred and twenty-four, one hundred and twenty-five, one hundred and twenty-six and / or one hundred and twenty-seven or fewer biomarkers can be used in a method for diagnosing cancer or a method for determining the prognosis of a test subject having cancer. In this way, a combination of about one biomarker or about two, three, four, five, six, seven, eight, nine, ten, eleven, twelve, thirteen, fourteen, fifteen, sixteen, seventeen, eighteen, nineteen, twenty, twenty-one, twenty-two, twenty-three, twenty-four, twenty-five, twenty-six, twenty-seven, twenty-eight, twenty-nine, thirty, thirty-one, thirty-two, thirty-three, thirty-four, thirty-five, thirty-six, thirty-seven, thirty-eight, thirty-nine, forty, forty-one, forty-two, forty-three, forty-four, forty-five, forty-six, forty-seven, forty-eight, forty-nine, fifty, fifty-one, fifty-two, fifty-three, fifty-four, fifty-five, fifty-six, fifty-seven, fifty-eight, fifty-nine, sixty, sixty-one, sixty-two, sixty-three, sixty-four, sixty-five, sixty-six, sixty-seven, sixty-eight, sixty-nine, seventy, seventy-one, seventy-two, seventy-three, seventy-four, seventy-five, seventy-six, seventy-seven, seventy-eight, seventy-nine, eighty, eighty-one, eighty-two, eighty-three, eighty-four, eighty-five, eighty-six, eighty-seven, eighty-eight, eighty-nine, ninety, ninety-one, ninety-two, ninety-three, ninety-four, ninety-five, ninety-six, ninety-seven, ninety-eight, ninety-nine, one hundred, one hundred and one, one hundred and two, one hundred and three, one hundred and four, one hundred and five, one hundred and six, one hundred and seven, one hundred and eight, one hundred and nine, one hundred and ten, one hundred and eleven, one hundred and twelve, one hundred and thirteen, one hundred and fourteen, one hundred and fifteen, one hundred and sixteen, one hundred and seventeen, one hundred and eighteen, one hundred and nineteen, one hundred and twenty, one hundred and twenty-one, one hundred and twenty-two, one hundred and twenty-three, one hundred and twenty-four, one hundred and twenty-five, one hundred and twenty-six and / or one hundred and twenty-seven biomarkers can be used in a method for diagnosing cancer or a method for determining the prognosis of a test subject having cancer.

[0107] In the initial step, the expression levels of one or more miRNAs are measured in plasma samples of patients having cancer. In one embodiment, the expression levels of a combination of one biomarker or 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 biomarkers can be used to generate a footprint or signature for subsequent diagnosis of the patient.In one embodiment, a footprint or signature for subsequent diagnosis of a patient can be generated using a combination of at least one biomarker or at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 biomarkers.

[0108] In one embodiment, the expression levels of one or fewer biomarkers or combinations of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 or fewer biomarkers can be used to generate a footprint or signature for subsequent diagnosis of a patient. In one embodiment, the expression levels of about one biomarker or about 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 biomarkers can be used to generate a footprint or signature for subsequent diagnosis of a patient.

[0109] Next, measure the expression levels of the same nucleic acid containing DNA and / or RNA and further containing miRNA in plasma, blood, or tissue samples of healthy subjects. Use this as a control. Subsequently, miRNAs with fluctuating expression levels can be identified in plasma samples of cancer patients compared to samples from healthy patients. A biomarker fingerprint or signature can be generated from the miRNAs with fluctuating expression levels. This can be used to diagnose cancer or determine its prognosis in a test subject by comparing the levels of miRNAs from the plasma of the test subject. Conventional statistical analysis can be used to determine, for example, the confidence level.

[0110] Figure 1 shows a method of combining results from biomarkers to achieve a final category determination. In a patient, multiple biomarkers can be measured. The results can be compiled to generate a single category determination according to the following steps. 1. For a patient or sample, a biomarker set was measured (each biomarker is b i and i is at least 1). 2. Optionally, use mathematical or logical operations to transform each biomarker. 3. For a subset of i biomarkers and j biomarkers within the subset (at least two member biomarkers per set), optionally use mathematical or logical operations (such as b1 / b2) to integrate the biomarker measurements to form an "integrated biomarker". 4. Optionally, perform step 3 for k subsets. 5. Optionally, repeat steps 2, 3, and 4 with the original biomarkers and the integrated biomarkers to obtain a final continuous score. 6. Apply a t threshold (when t is 1 or greater) to classify the patient or sample into a determination category (diagnosis, prognosis, treatment responder, etc.).

[0111] An alternative approach includes the following steps. 1. For a patient or sample, a biomarker set was measured (each biomarker is b i where i is at least 1). 2. Mathematically transform each biomarker (the definition of the transformation may not be no transformation, such as a non - modified operation like a mathematical operation or multiplying by 1). 3. Optionally, mathematically integrate biomarkers from 2 to i into a final score (e.g., by the added results such as weights applied to each and linear regression, or by other algorithmic steps such as iterative steps). 4. Use a t - threshold (t is at least 1) to classify the patient or sample into one of t + 1 categories.

[0112] Diagnostic categories of biomarkers The applicant has discovered that certain biomarkers are effective for distinguishing categories of lung cancer. Embodiments include systems and methods for distinguishing lung cancers (including SCLC, NSCLC, NSCLC adenocarcinoma, NSCLC squamous cell carcinoma, and NSCLC large cell undifferentiated type). Categories and corresponding biomarkers are shown in Table 3. As shown in Table 4, multiple biomarkers in multiple categories can be used.

Table 3

Table 4

[0113] Diagnostic kit for rapid screening of lung cancer The following working examples are based on the above configuration. Embodiments of the present invention can be summarized into a diagnostic kit for diagnosing lung adenocarcinoma. This kit can identify one or more target cells having biomarkers of lung cancer in the plasma of a test subject.

[0114] The kit may include a collection of nucleic acid molecules such that each nucleic acid molecule encodes an miRNA sequence. The nucleic acid molecules can be used to identify fluctuations in the expression levels of one or more miRNAs in a plasma sample of a test subject. The expression levels of the miRNAs can be used in the comparison / analysis of test samples by a fingerprint indicating the presence of cancer.

[0115] In certain embodiments, the present disclosure provides a kit for diagnosing lung cancer. In one embodiment, the lung cancer is adenocarcinoma. The kit may include one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers. Those skilled in the art will understand that the number of biomarkers can be changed without departing from the nature of the present disclosure, and thus other combinations of biomarkers are also included in the present disclosure. A person skilled in the art will be able to determine one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers to be used based on the symptoms of a patient suffering from lung cancer.

[0116] In certain embodiments, the kit comprises one biomarker disclosed herein or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers. In certain embodiments, the kit is for diagnosing lung cancer. In another embodiment, the kit is for diagnosing adenocarcinoma. The kit may optionally further comprise instructions for use. The kit may further optionally include tubes, applicators, vials, or other storage containers containing the above biomarkers, and / or vials containing one or more biomarkers (e.g., including these, consisting essentially of these, consisting of these). In one embodiment, each biomarker is in its own tube, applicator, vial or storage container, or 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, and / or 24 biomarkers are in a tube, applicator, vial, or storage container.

[0117] Regardless of type, the kit typically includes one or more containers in which one biomarker or a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers are disposed internally and preferably appropriately aliquoted. The components of the kit can be packaged either in an aqueous medium or in lyophilized form.

[0118] Examples The following non-limiting examples are provided for illustrative purposes only to facilitate a more complete understanding of the presently contemplated representative embodiments. These examples are intended to be merely a subset of all possible contexts in which the components of the formulation can be combined. Accordingly, these examples should not be construed as limiting any of the embodiments described herein, including those related to the type and amount of components of the formulation and / or its methods and uses.

[0119] Example 1 Diagnosis of Lung Cancer Using miRNA Biomarkers A 56-year-old male smoker tells his healthcare provider that he has had repeated coughing and sputum, sometimes mixed with blood. The healthcare provider takes a blood sample and sends it to the laboratory for testing for lung cancer. The blood sample is prepared to obtain plasma. Next, the plasma is tested to identify the presence of biomarkers associated with lung cancer. In the laboratory, during the test, one or more of the following biomarkers are used: hsa-miR-1204, hsa-miR-141-3p, hsa-miR-1827, hsa-miR-938, hsa-miR-125b-5p, hsa-miR-297, hsa-miR-10a-5p, hsa-miR-145-5p, hsa-miR-217, hsa-miR-3185, hsa-miR-21-5p, hsa-miR-363-3p, hsa-miR-631, hsa-miR-655, hsa-miR-1245b-5p, hsa-miR-369-3p, hsa-miR-875-3p, hsa-miR-105-5p, hsa-miR-1253, hsa-miR-1285-3p, hsa-miR-512-5p, hsa-miR-550b-3p, hsa-miR-571, hsa-miR-935, hsa-miR-145-5p, hsa-miR-3185, hsa-miR-1285-3p, hsa-miR-125b-5p, and hsa-miR-550b-3p. A common hybridization-based assay is used to determine the level of each biomarker in the patient sample.

[0120] Based on the test results, the healthcare provider determines that one or more biomarkers used in the test for lung cancer, more specifically adenocarcinoma, indicate its presence. The patient is informed that he has lung cancer, more specifically adenocarcinoma. A chest X-ray can assist in determining the location and extent of the cancer.

[0121] Biomarkers can identify cancer at an early stage when more treatment options are available. Based on these results, patients are referred to specialists for further evaluation and treatment.

[0122] Example 2 Diagnosis of Lung Cancer Using mRNA Biomarkers In this example, patients wish to undergo screening for lung cancer. Biomarkers can accurately identify cancer at an early stage. Furthermore, the use of biomarkers is less invasive than conventional methods (i.e., chest X-ray and biopsy). The healthcare provider collects a blood sample from the patient. Subsequently, the levels of the mRNA biomarkers can be determined in blood, plasma, serum, or derivatives of blood.

[0123] In the laboratory, during the test, two or more of the following biomarkers are used (identified by genes): DDX24, SSRP1, RCN2, TBCD, PDXK, RRP7A, F13A1, KIF3C, PURA, PARVB, ITGA2B, KLHL24, DMAC2L, PRPS1L1, SEPHS1, FOS, KIF1B, EED, LOH11CR2A (alias VWA5A), TCTN3, SLC48A1, DENND1A, MFSD11, and PIK3IP. General hybridization-based assays are used to determine the levels of each biomarker in the patient sample. The mRNA expression levels are input into a computer such as a laptop or tablet computer. The computer compiles the expression levels to generate a score. The score is compared with one or more thresholds to diagnose lung cancer or determine its prognosis.

[0124] Based on the score, the healthcare provider determines that the patient has lung cancer. Chest X-ray can determine the location and extent of the cancer. The patient is referred to a specialist for further evaluation and treatment.

[0125] Example 3 Diagnosis of Squamous Cell Lung Cancer Using mRNA Biomarkers Squamous cell adenocarcinoma is a type of non-small cell lung cancer. The others are adenocarcinoma and large cell carcinoma. In this example, the healthcare provider wishes to screen the patient for squamous cell lung cancer. The biomarker can distinguish squamous cell adenocarcinoma from other types of lung cancer (i.e., adenocarcinoma, large cell carcinoma, and SCLC). Further, the use of the biomarker is less invasive than conventional methods (i.e., chest X-ray and biopsy). The healthcare provider takes a blood sample from the patient. Thereafter, the level of the mRNA biomarker can be determined in blood, plasma, serum, or a blood preparation.

[0126] In the laboratory, during the test, two or more of the following biomarkers are used (identified by genes): UBA1, ARAF, DNAJB12, KLHL21, ANXA8, CCS, ZNF142, PER2, ELAVL3, PADI4, TRIM49, YTHDF3, PIK3IP1, C9orf16, AXIN2, MED27, REX1BD, alias 619orf60, MARK4, PGAP3, MAPKAPK5-AS1. A common hybridization-based assay is used to determine the level of each biomarker in the patient sample. The mRNA expression level is input into a computer such as a laptop or a tablet computer. The computer compiles the expression levels and generates a score. The score is compared with one or more thresholds to diagnose lung cancer or determine its prognosis.

[0127] Based on the score, the healthcare provider determines that the patient has squamous cell cancer. The location and extent of the cancer can be determined by chest X-ray. The patient is referred to a specialist for further evaluation and treatment.

[0128] Example 4 Diagnosis of Small Cell Lung Cancer (SCLC) Using Biomarkers In this example, the patient has lung cancer. Biomarkers are used to identify the patient as having small cell lung cancer (SCLC). Further, the use of biomarkers is less invasive than conventional methods (i.e., chest X-ray and biopsy). The healthcare provider takes a blood sample from the patient. Thereafter, the levels of the mRNA biomarkers can be determined in blood, plasma, serum, or blood products.

[0129] In the laboratory, during the test, two or more of the following biomarkers are used (identified by genes): UBA1, THRA, EPHB3, NMT1, PAFAH1B3, RNF44, ASF1A, BHMT2, YTHDF3, C9orf16, REX1BD (alias 619orf60), N53536, MICALL1, CALHM2. A general hybridization-based assay is used to determine the level of each biomarker in the patient sample. The mRNA expression levels are input into a computer such as a laptop or a tablet computer. The computer compiles the expression levels to generate a score. The score is compared with one or more thresholds to diagnose SCLC or to determine its prognosis.

[0130] Based on the score, the healthcare provider determines that the patient has SCLC. Additional biomarkers can be analyzed to determine the type of SCLC. The location and extent of the cancer can be determined by chest X-ray. The patient is referred to a specialist for further evaluation and treatment.

[0131] Example 5 Diagnosis of non-small cell lung cancer (NSCLC, not adenocarcinoma) using biomarkers In this example, the patient has lung cancer. Using biomarkers, the patient is identified as having non-small cell lung cancer (NSCLC) in the category. Furthermore, the use of biomarkers is less invasive than conventional methods (i.e., chest X-ray and biopsy). The healthcare provider collects a blood sample from the patient. Subsequently, the level of the mRNA biomarker can be determined in blood, plasma, serum, or a blood preparation.

[0132] In the laboratory, during the test, two or more of the following biomarkers are used (identified by genes): UBA1, EPHB3, CDC123, ETFA, PAFAH1B3, ASF1A, LGI2, TSHZ2, PCDHB1, TAS2R13, MICALL1, and R3HDM4. A general hybridization-based assay is used to determine the level of each biomarker in the patient sample. The mRNA expression level is input into a computer such as a laptop or a tablet computer. The computer compiles the expression levels and generates a score. The score is compared with one or more thresholds to diagnose NSCLC or determine its prognosis.

[0133] Based on the score, the healthcare provider determines that the patient has SCLC. The location and extent of the cancer can be determined by chest X-ray. The patient is referred to a specialist for further evaluation and treatment.

[0134] Example 6 Diagnosis of large cell lung cancer using biomarkers In this example, the patient has lung cancer. Using biomarkers, the type is identified as large cell lung cancer in the patient. The use of biomarkers is less invasive than conventional methods (i.e., chest X-ray and biopsy). The healthcare provider collects a blood sample from the patient. Subsequently, the level of the mRNA biomarker can be determined in blood, plasma, serum, or a blood preparation.

[0135] In the examination room, during the examination, two or more of the following biomarkers are used (identified by genes): ARAF, ETFA, CD4, PAGE4, TTTY14, KCNMB4, KIF26B, CDHR5, CNTD2, VWA7, ATOH1, SSH3, and SIDT2. A general hybridization-based assay is used to determine the level of each biomarker in the patient sample. The mRNA expression level is input into a computer such as a laptop or a tablet computer. The computer compiles the expression levels and generates a score. The score is compared with one or more thresholds to diagnose large cell lung cancer or determine its prognosis.

[0136] Based on the score, the healthcare provider determines that the patient has large cell lung cancer. Chest X-ray can determine the location and extent of the cancer. The patient is referred to a specialist for further evaluation and treatment.

[0137] Example 7 Diagnosis of adenocarcinoma using biomarkers Adenocarcinoma is a type of NSCLC. In this example, the patient has NSCLC. Biomarkers are used to identify the category as adenocarcinoma. The use of biomarkers is less invasive than conventional methods (i.e., chest X-ray and biopsy). The healthcare provider collects a blood sample from the patient. Subsequently, the level of the mRNA biomarker can be determined in blood, plasma, serum, or blood products.

[0138] In the examination room, during the examination, two or more of the following biomarkers are used (identified by genes): TRADD, NMT1, ARAF, ANXA8, PAFAH1B3, NELL2, TRIM36, BHMT2, PIK3IP1, C9orf16, MED27, MICALL1, PGAP3, SENP5, PLA2G4B. General hybridization-based assays are used to determine the levels of each biomarker in patient samples. The mRNA expression levels are input into a computer such as a laptop or a tablet computer. The computer compiles the expression levels and generates a score. The score is compared with one or more thresholds to diagnose adenocarcinoma or determine its prognosis.

[0139] Based on the score, the healthcare provider determines that the patient has adenocarcinoma. Chest X-ray can determine the location and extent of the cancer. The patient is referred to a specialist for further evaluation and treatment.

[0140] Finally, while the aspects of this specification are emphasized by reference to specific embodiments, it will be understood by those skilled in the art that these disclosed embodiments are merely illustrative examples of the principles of the subject matter disclosed herein. Accordingly, it is to be understood that the disclosed subject matter is in no way limited to the specific methodologies, protocols, and / or reagents, etc. described herein. Thus, various modifications, alterations, or alternative configurations of the disclosed subject matter can be made in accordance with the teachings herein without departing from the spirit of this specification. Finally, the terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of the invention, which is defined only by the claims. Accordingly, the invention is not limited to what is precisely shown and described.

[0141] Specific embodiments of the invention are described herein, including the best mode known to the inventors for carrying out the invention. It will of course be apparent to those skilled in the art that modifications of these described embodiments will become apparent upon reading the foregoing description. The inventors expect those skilled in the art to appropriately use such modifications, and the inventors intend for the invention to be practiced otherwise than as specifically described herein. Accordingly, the invention includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Further, any combination of the above-described embodiments in all possible variations thereof is included in the invention unless otherwise indicated herein or otherwise clearly contradicted by context.

[0142] Groupings of alternative embodiments, elements, or steps of the invention should not be construed as limiting. Each group member may be referred to and claimed individually or in any combination with other group members disclosed herein. It is anticipated that one or more members of a group may be included in or deleted from the group for reasons of convenience and / or patentability. When such inclusion or deletion occurs, the specification is considered to include the modified group and to satisfy all written descriptions of Markush groups used in the appended claims.

[0143] Unless otherwise specified, all numbers expressing characteristics, items, quantities, parameters, properties, terms, etc. used in this specification and the claims should be understood to be modified in all cases by the term "about". As used herein, the term "about" means that the thus quantified characteristic, item, quantity, parameter, property, or term encompasses a range of plus or minus 10 percent above or below the value of the recited characteristic, item, quantity, parameter, property, or term. Accordingly, unless indicated to the contrary, the numerical parameters set forth in the specification and the appended claims are approximate values that may vary. At a minimum, and not as an attempt to limit the application of the doctrine of equivalents to the claims, each numerical representation should be construed at least in light of the reported number of significant digits and by applying ordinary rounding techniques. Despite the numerical ranges and values indicating broad scope of the present invention being approximate, the numerical ranges and values set forth in the specific examples are reported as precisely as possible. However, a numerical range or value inherently includes certain errors necessarily resulting from the standard deviation found in each test measurement. The recitation of numerical ranges of values herein is merely intended to serve as a simplified method of referring individually to each individual numerical value within the range. Unless otherwise indicated herein, each individual value of a numerical range is incorporated herein as if it were individually recited herein.

Claims

1. 1. A method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: a) measuring the expression levels of at least two miRNAs in a test sample of the subject; b) receiving said expression levels using a computer; c) compiling said expression levels to generate a score; and d) comparing said score to one or more thresholds to diagnose or determine the prognosis of lung cancer.

2. 2. The method of claim 1, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

3. The at least two miRNAs are selected from the group consisting of hsa-miR-1204, hsa-miR-141-3p, hsa-miR-1827, hsa-miR-938, hsa-miR-125b-5p, hsa-miR-297, hsa-miR-10a-5p, hsa-miR-145-5p, hsa-miR-217, hsa-miR-3185, hsa-miR-21-5p, hsa-miR-363-3p, hsa- The method of claim 1, comprising miR-631, hsa-miR-655, hsa-miR-1245b-5p, hsa-miR-369-3p, hsa-miR-875-3p, hsa-miR-105-5p, hsa-miR-1253, hsa-miR-1285-3p, hsa-miR-512-5p, hsa-miR-550b-3p, hsa-miR-571 and hsa-miR-935.

4. 2. The method of claim 1, wherein the at least two miRNAs are comprised of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers.

5. 2. The method of claim 1, wherein the at least two miRNAs consist of no more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers.

6. 1. A method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: a) measuring the expression levels of at least two mRNAs in a test sample of the subject; b) receiving said expression levels using a computer; c) compiling said expression levels to generate a score; and d) comparing said score to one or more thresholds to diagnose or determine the prognosis of lung cancer.

7. 7. The method of claim 6, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

8. The at least two mRNAs are selected from the group consisting of genes TCTN3, DENND1A, FOS, MFSD11, PRPS1L1, F13A1, KLHL24, SSRP1, DDX24, KIF1B, RRP7A, MICALL1, C9orf16, SEPHS1, DMAC2L, ITGA2B, PURA, PAFAH1B 3, PDXK, ARAF, TBCD, UBA1, EED, PARVB, RCN2, PGAP3, REX1BD (also known as 619orf60), MED27, P IK3IP1, YTHDF3, BHMT2, ASF1A, ANXA8, ETFA, NMT1, EPHB3, KIF3C, LOH11CR2A (also known as VWA 5A), SLC48A1, MAPKAPK5-AS1, PLA2G4B, CALHM2, SENP5, SIDT2, R3HDM4, MARK4, SSH3, ATOH1, AXIN2, TAS2R13, PCDHB1, VWA7, TRIM49, CNTD2, TSHZ2, CDHR5, KIF26B, PADI4, TRIM36, LGI2, KCNMB4, TTTY14, ELAVL3, PAGE4, ​​PER2, ZNF142, CD4, CCS, NELL2, RNF44, KLHL21, DNAJB12, CDC123, GNAI3, TRADD and / or THRA.

9. The at least two mRNAs are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72 7, 8, 9, 10, 11, 12 ...

10. The at least two mRNAs are 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 7. The method of claim 6, wherein the mRNA comprises up to 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different mRNAs.

11. 1. A method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: a) determining the expression levels of at least two proteins or peptides in a test sample of said subject; b) receiving said expression levels using a computer; c) compiling said expression levels to generate a score; and d) comparing said score to one or more thresholds to diagnose or determine the prognosis of lung cancer.

12. 12. The method of claim 11, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

13. The at least two proteins or peptides are selected from the group consisting of tectonic-3, DENN domain-containing protein 1A, proto-oncogene c-Fos, UNC93-like protein MFSD11, ribose phosphate pyrophosphokinase 3, coagulation factor XIII A chain, Kelch-like protein 24, FACT complex subunit SSRP1, ATP-dependent RNA helicase DDX24, kinesin-like protein KIF1B, ribosomal RNA processing protein 7 homolog A, MICAL-like protein 1, and UPF0. 184 proteins C9orf16, selenide, water dikinase 1, ATP synthase subunit s, mitochondria, integrin alpha-IIb, transcriptional activator protein Pur-alpha, platelet-activating factor acetylhydrolase IB subunit gamma, pyridoxal kinase, serine / threonine protein kinase A-Raf, tubulin-specific chaperone D, ubiquitin modifier activating enzyme 1, polycomb protein EED, beta-parvin, reticulocavin-2, post-GPI attachment to proteins factor 3, required for excision 1-B domain-containingprotein, mediator of RNA polymerase II transcription subunit 27, phosphoinositide-3-kinase interacting protein 1, YTH domain-containing family protein 3, S-methylmethionine-homocysteine ​​S-methyltransferase BHMT2, histone chaperone ASF1A, annexin A8, electron transfer flavoprotein subunit alpha mitochondrial, glycylpeptide N-tetradecanoyltransferase 1, ephrin type B receptor 3, kinesin-like protein KIF3C, loss of heterozygosity, 11, chromosome region 2, gene A, isoform CRA_b, heme transporter HRG1, putative uncharacterized protein encoded by MAPKAPK5-AS1, cytosolic phospholipase A2 beta, calcium homeostasis modulator protein 2, centrin-specific protease 5, SID1 transmembrane family member 2, R3H domain-containing protein 4, MAP / microtubule affinity regulation kinase 4, CYSRT1 gene, protein phosphatase slingshot homolog 3, protein atonal homolog 1, axin-2, taste receptor type 2 member 13, protocadherin beta-1, von Willebrand factor A domain-containing protein 7, tripartite motif-containing protein 49, cyclin N-terminal domain-containing protein 2, tee-shirt homolog 2, hypothetical protein FLJ20251, cadherin-related family member 5, hypothetical protein FLJ11457, kinesin-like protein KIF26B, protein arginine deiminase type 4, E3 ubiquitin protein ligase TRIM36, leucine-rich repeat LGI family member 2, calcium-activated potassium channel subunit beta-4, ELAV-like protein 3, P antigen family member 4, periodic circadian protein homolog 2, zinc finger protein 142 (clone pHZ-49), isoform CRA_b, U47924The method of claim 11, comprising: CD4 molecule, copper chaperone for superoxide dismutase, protein kinase C binding protein NELL2, RING finger protein 44, Kelch-like protein 21, DnaJ homolog subfamily B member 12, cell division cycle protein 123 homolog, guanine nucleotide-binding protein G(i) subunit alpha, tumor necrosis factor receptor type 1-related DEATH domain protein, and thyroid hormone receptor alpha.

14. The at least two proteins or polypeptides are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 12. The method of claim 11 , wherein the nucleic acid comprises 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different proteins or polypeptides.

15. The at least two nucleic acids, proteins or polypeptides may be 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73 , 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 or less different proteins or polypeptides.

16. 1. A method for diagnosing lung cancer or determining the prognosis of a lung cancer patient, comprising: a) measuring the expression levels of at least two miRNAs in a sample from a subject having lung cancer; b) measuring the expression levels of the same at least two miRNAs in a sample of healthy patients; c) calculating one or more threshold values ​​based on the expression levels of a) and the expression levels of b); d) generating a score from said measured levels of said at least two miRNAs in a test patient sample; e) comparing said score to said one or more threshold values ​​to diagnose or determine the prognosis of cancer in said test patient.

17. 17. The method of claim 16, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

18. The one or more miRNAs are hsa-miR-1204, hsa-miR-141-3p, hsa-miR-1827, hsa-miR-938, hsa-miR-125b-5p, hsa-miR- 297, hsa-miR-10a-5p, hsa-miR-145-5p, hsa-miR-217, hsa-miR-3185, hsa-miR-21-5p, hsa-miR-363-3p, hsa- 17. The method of claim 16, comprising miR-631, hsa-miR-655, hsa-miR-1245b-5p, hsa-miR-369-3p, hsa-miR-875-3p, hsa-miR-105-5p, hsa-miR-1253, hsa-miR-1285-3p, hsa-miR-512-5p, hsa-miR-550b-3p, hsa-miR-571 and hsa-miR-935.

19. 17. The method of claim 16, wherein the one or more miRNAs are comprised of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 mRNAs.

20. 17. The method of claim 16, wherein the at least one or more miRNAs are comprised of no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 mRNAs.

21. 1. A method for diagnosing lung cancer or determining the prognosis of a lung cancer patient, comprising: a) measuring the expression levels of at least two mRNAs in a sample from a subject having lung cancer; b) measuring the expression levels of the same at least two mRNAs in a sample of a healthy patient; c) calculating one or more threshold values ​​based on the expression levels of a) and the expression levels of b); d) generating a score from said measured levels of said at least two mRNAs in a test patient sample; e) comparing said score to said one or more threshold values ​​to diagnose or determine the prognosis of cancer in said test patient.

22. 22. The method of claim 21, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

23. The two or more mRNAs are selected from the group consisting of genes TCTN3, DENND1A, FOS, MFSD11, PRPS1L1, F13A1, KLHL24, SSRP1, DDX24, KIF1B, RRP7A, MICALL1, C9orf16, SEPHS1, DMAC2L, ITGA2B, PURA, PAFAH1B3, PDXK, ARAF, TBCD, UBA1, EED, PARVB, RCN2, PGAP3, REX1BD (also known as 619orf60), MED27, PIK 3IP1, YTHDF3, BHMT2, ASF1A, ANXA8, ETFA, NMT1, EPHB3, KIF3C, LOH11CR2A (also known as VWA5A ), SLC48A1, MAPKAPK5-AS1, PLA2G4B, CALHM2, SENP5, SIDT2, R3HDM4, MARK4, SSH3, ATOH1, AXIN2, TAS2R13, PCDHB1, VWA7, TRIM49, CNTD2, TSHZ2, CDHR5, KIF26B, PADI4, TRIM36, LGI2, KCNMB4, TTTY14, ELAVL3, PAGE4, ​​PER2, ZNF142, CD4, CCS, NELL2, RNF44, KLHL21, DNAJB12, CDC123, GNAI3, TRADD and / or THRA.

24. The at least two mRNAs are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 22. The method of claim 21 , wherein the mRNA comprises 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different mRNAs.

25. The at least two mRNAs are 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 22. The method of claim 21 , wherein the mRNA comprises no more than 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different mRNAs.

26. 1. A method for diagnosing lung cancer or determining the prognosis of a lung cancer patient, comprising: a) measuring the expression levels of at least two proteins or peptides in a sample from a subject having lung cancer; b) measuring the expression levels of the same at least two proteins or peptides in samples from healthy patients; c) calculating one or more threshold values ​​based on the expression levels of a) and the expression levels of b); d) generating a score from said measured levels of said at least two proteins or peptides in a test patient sample; e) comparing said score to said one or more threshold values ​​to diagnose or determine the prognosis of cancer in said test patient.

27. 27. The method of claim 26, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

28. The at least two proteins or peptides are selected from the group consisting of tectonic-3, DENN domain-containing protein 1A, proto-oncogene c-Fos, UNC93-like protein MFSD11, ribose phosphate pyrophosphokinase 3, coagulation factor XIII A chain, Kelch-like protein 24, FACT complex subunit SSRP1, ATP-dependent RNA helicase DDX24, kinesin-like protein KIF1B, ribosomal RNA processing protein 7 homolog A, MICAL-like protein 1, and UPF0. 184 proteins C9orf16, selenide, water dikinase 1, ATP synthase subunit s, mitochondria, integrin alpha-IIb, transcriptional activator protein Pur-alpha, platelet-activating factor acetylhydrolase IB subunit gamma, pyridoxal kinase, serine / threonine protein kinase A-Raf, tubulin-specific chaperone D, ubiquitin modifier activating enzyme 1, polycomb protein EED, beta-parvin, reticulocavin-2, post-GPI attachment to proteins factor 3, required for excision 1-B domain-containingprotein, mediator of RNA polymerase II transcription subunit 27, phosphoinositide-3-kinase interacting protein 1, YTH domain-containing family protein 3, S-methylmethionine-homocysteine ​​S-methyltransferase BHMT2, histone chaperone ASF1A, annexin A8, electron transfer flavoprotein subunit alpha mitochondrial, glycylpeptide N-tetradecanoyltransferase 1, ephrin type B receptor 3, kinesin-like protein KIF3C, loss of heterozygosity, 11, chromosome region 2, gene A, isoform CRA_b, heme transporter HRG1, putative uncharacterized protein encoded by MAPKAPK5-AS1, cytosolic phospholipase A2 beta, calcium homeostasis modulator protein 2, centrin-specific protease 5, SID1 transmembrane family member 2, R3H domain-containing protein 4, MAP / microtubule affinity regulation kinase 4, CYSRT1 gene, protein phosphatase slingshot homolog 3, protein atonal homolog 1, axin-2, taste receptor type 2 member 13, protocadherin beta-1, von Willebrand factor A domain-containing protein 7, tripartite motif-containing protein 49, cyclin N-terminal domain-containing protein 2, tee-shirt homolog 2, hypothetical protein FLJ20251, cadherin-related family member 5, hypothetical protein FLJ11457, kinesin-like protein KIF26B, protein arginine deiminase type 4, E3 ubiquitin protein ligase TRIM36, leucine-rich repeat LGI family member 2, calcium-activated potassium channel subunit beta-4, ELAV-like protein 3, P antigen family member 4, periodic circadian protein homolog 2, zinc finger protein 142 (clone pHZ-49), isoform CRA_b, U4792427. The method of claim 26, comprising: CD4 molecule, copper chaperone for superoxide dismutase, protein kinase C binding protein NELL2, RING finger protein 44, Kelch-like protein 21, DnaJ homolog subfamily B member 12, cell division cycle protein 123 homolog, guanine nucleotide binding protein G(i) subunit alpha, tumor necrosis factor receptor type 1-related DEATH domain protein, and thyroid hormone receptor alpha.

29. The at least two proteins or polypeptides are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 27. The method of claim 26, wherein the nucleic acid sequence comprises 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different proteins or polypeptides.

30. 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 27. The method of claim 26, wherein the method comprises up to 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different proteins or polypeptides.

31. 1. A method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: a) measuring the expression level of at least one miRNA in a test sample of the subject; b) receiving, by a computer, an expression level of at least one miRNA in the test sample; c) comparing the expression level of the at least one miRNA in the test sample with the level of the same at least one miRNA in a base sample; d) receiving a comparison of the expression level of the at least one miRNA in the test sample measured in a) and in the base sample measured in c); and e) diagnosing or determining a prognosis of lung cancer based on a change in expression of at least one miRNA in the test sample compared to the base sample, as determined by a computational method.

32. 32. The method of claim 31 , wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

33. The at least one miRNA is selected from the group consisting of hsa-miR-1204, hsa-miR-141-3p, hsa-miR-1827, hsa-miR-938, hsa-miR-125b-5p, hsa-miR-297, hsa-miR-10a-5p, hsa-miR-145-5p, hsa-miR-217, hsa-miR-3185, hsa-miR-21-5p, hsa-miR-363-3p, hsa- The method of claim 31, comprising miR-631, hsa-miR-655, hsa-miR-1245b-5p, hsa-miR-369-3p, hsa-miR-875-3p, hsa-miR-105-5p, hsa-miR-1253, hsa-miR-1285-3p, hsa-miR-512-5p, hsa-miR-550b-3p, hsa-miR-571 and hsa-miR-935.

34. 32. The method of claim 31 , wherein the at least one miRNA consists of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 miRNAs.

35. 32. The method of claim 31 , wherein the at least one miRNA consists of no more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 miRNAs.

36. 1. A method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: a) measuring the expression level of at least one mRNA in a test sample of the subject; b) receiving, by a computer, an expression level of at least one mRNA in the test sample; c) comparing the expression level of the at least one mRNA in the test sample with the level of the same at least one mRNA in a base sample; d) receiving a comparison of the expression level of the at least one mRNA in the test sample measured in a) and in the base sample measured in c); and e) diagnosing or determining a prognosis for lung cancer based on a change in expression of at least one mRNA in the test sample compared to the base sample, as determined by a computational method.

37. 37. The method of claim 36, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

38. The two or more mRNAs are selected from the group consisting of genes TCTN3, DENND1A, FOS, MFSD11, PRPS1L1, F13A1, KLHL24, SSRP1, DDX24, KIF1B, RRP7A, MICALL1, C9orf16, SEPHS1, DMAC2L, ITGA2B, PURA, PAFAH1B3, PDXK, ARAF, TBCD, UBA1, EED, PARVB, RCN2, PGAP3, REX1BD (also known as 619orf60), MED27, PIK 3IP1, YTHDF3, BHMT2, ASF1A, ANXA8, ETFA, NMT1, EPHB3, KIF3C, LOH11CR2A (also known as VWA5A ), SLC48A1, MAPKAPK5-AS1, PLA2G4B, CALHM2, SENP5, SIDT2, R3HDM4, MARK4, SSH3, ATOH1, AXIN2, TAS2R13, PCDHB1, VWA7, TRIM49, CNTD2, TSHZ2, CDHR5, KIF26B, PADI4, TRIM36, LGI2, KCNMB4, TTTY14, ELAVL3, PAGE4, ​​PER2, ZNF142, CD4, CCS, NELL2, RNF44, KLHL21, DNAJB12, CDC123, GNAI3, TRADD and / or THRA.

39. The at least two mRNAs are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 37. The method of claim 36, wherein the mRNA comprises 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different mRNAs.

40. The at least two mRNAs are 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 37. The method of claim 36, wherein the sequence comprises no more than 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different mRNAs.

41. 1. A method for diagnosing lung cancer or determining the prognosis of a subject having lung cancer, comprising: a) determining the expression level of at least one protein or peptide in a test sample of said subject; b) receiving, by a computer, an expression level of at least one protein or peptide in the test sample; c) comparing the expression level of the at least one protein or peptide in the test sample with the level of the same at least one protein or peptide in a base sample; d) receiving a comparison of the expression level of the at least one protein or peptide in the test sample measured in a) and in the base sample measured in c); and e) diagnosing or determining a prognosis for lung cancer based on a change in expression of at least one protein or peptide in the test sample compared to the base sample, as determined by a computer.

42. 42. The method of claim 41, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

43. The at least two proteins or peptides are selected from the group consisting of tectonic-3, DENN domain-containing protein 1A, proto-oncogene c-Fos, UNC93-like protein MFSD11, ribose phosphate pyrophosphokinase 3, coagulation factor XIII A chain, Kelch-like protein 24, FACT complex subunit SSRP1, ATP-dependent RNA helicase DDX24, kinesin-like protein KIF1B, ribosomal RNA processing protein 7 homolog A, MICAL-like protein 1, and UPF0. 184 proteins C9orf16, selenide, water dikinase 1, ATP synthase subunit s, mitochondria, integrin alpha-IIb, transcriptional activator protein Pur-alpha, platelet-activating factor acetylhydrolase IB subunit gamma, pyridoxal kinase, serine / threonine protein kinase A-Raf, tubulin-specific chaperone D, ubiquitin modifier activating enzyme 1, polycomb protein EED, beta-parvin, reticulocavin-2, post-GPI attachment to proteins factor 3, required for excision 1-B domain-containingprotein, mediator of RNA polymerase II transcription subunit 27, phosphoinositide-3-kinase interacting protein 1, YTH domain-containing family protein 3, S-methylmethionine-homocysteine ​​S-methyltransferase BHMT2, histone chaperone ASF1A, annexin A8, electron transfer flavoprotein subunit alpha mitochondrial, glycylpeptide N-tetradecanoyltransferase 1, ephrin type B receptor 3, kinesin-like protein KIF3C, loss of heterozygosity, 11, chromosome region 2, gene A, isoform CRA_b, heme transporter HRG1, putative uncharacterized protein encoded by MAPKAPK5-AS1, cytosolic phospholipase A2 beta, calcium homeostasis modulator protein 2, centrin-specific protease 5, SID1 transmembrane family member 2, R3H domain-containing protein 4, MAP / microtubule affinity regulation kinase 4, CYSRT1 gene, protein phosphatase slingshot homolog 3, protein atonal homolog 1, axin-2, taste receptor type 2 member 13, protocadherin beta-1, von Willebrand factor A domain-containing protein 7, tripartite motif-containing protein 49, cyclin N-terminal domain-containing protein 2, tee-shirt homolog 2, hypothetical protein FLJ20251, cadherin-related family member 5, hypothetical protein FLJ11457, kinesin-like protein KIF26B, protein arginine deiminase type 4, E3 ubiquitin protein ligase TRIM36, leucine-rich repeat LGI family member 2, calcium-activated potassium channel subunit beta-4, ELAV-like protein 3, P antigen family member 4, periodic circadian protein homolog 2, zinc finger protein 142 (clone pHZ-49), isoform CRA_b, U4792442. The method of claim 41, comprising: CD4 molecule, copper chaperone for superoxide dismutase, protein kinase C binding protein NELL2, RING finger protein 44, Kelch-like protein 21, DnaJ homolog subfamily B member 12, cell division cycle protein 123 homolog, guanine nucleotide binding protein G(i) subunit alpha, tumor necrosis factor receptor type 1-related DEATH domain protein, and thyroid hormone receptor alpha.

44. The at least two proteins or peptides are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 42. The method of claim 41 , wherein the nucleic acid sequence comprises 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different proteins or peptides.

45. The at least two proteins or peptides are 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 42. The method of claim 41 , wherein the method comprises up to 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different proteins or peptides.

46. A diagnostic kit for diagnosing lung cancer, comprising: the kit comprises a plurality of nucleic acid molecules, each nucleic acid molecule encoding an miRNA sequence; the plurality of nucleic acid molecules identifies a variation in the expression level of one or more miRNAs in a sample of a test subject; the at least a plurality of nucleic acid molecules comprises a plurality of base sample nucleic acid molecules; wherein the expression level of the one or more miRNAs is indicative of the presence of lung cancer; The kit identifies one or more target cells exhibiting the test subject miRNA expression as lung cancer.

47. 47. The diagnostic kit of claim 46, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

48. The one or more miRNAs are hsa-miR-1204, hsa-miR-141-3p, hsa-miR-1827, hsa-miR-938, hsa-miR-125b-5p, hsa-miR-2 97, hsa-miR-10a-5p, hsa-miR-145-5p, hsa-miR-217, hsa-miR-3185, hsa-miR-21-5p, hsa-miR-363-3p, hsa-m The diagnostic kit of claim 46, comprising iR-631, hsa-miR-655, hsa-miR-1245b-5p, hsa-miR-369-3p, hsa-miR-875-3p, hsa-miR-105-5p, hsa-miR-1253, hsa-miR-1285-3p, hsa-miR-512-5p, hsa-miR-550b-3p, hsa-miR-571 and hsa-miR-935.

49. 47. The diagnostic kit of claim 46, wherein the one or more miRNAs consist of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers.

50. 47. The diagnostic kit of claim 46, wherein the at least one or more miRNAs consist of no more than 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 and / or 24 biomarkers.

51. A diagnostic kit for diagnosing lung cancer, comprising: the kit comprises a plurality of nucleic acid molecules, each nucleic acid molecule encoding an mRNA sequence; the plurality of nucleic acid molecules identifies a variation in expression level of one or more mRNAs in a sample of a test subject; the at least a plurality of nucleic acid molecules comprises a plurality of base sample nucleic acid molecules; the expression level of the one or more mRNAs is indicative of the presence of lung cancer; The kit identifies one or more target cells exhibiting the test subject mRNA expression as lung cancer.

52. 52. The method of claim 51, wherein the lung cancer is squamous cell lung cancer, non-small cell lung cancer, large cell lung cancer, small cell lung cancer, and / or adenocarcinoma lung cancer.

53. The two or more mRNAs are selected from the group consisting of genes TCTN3, DENND1A, FOS, MFSD11, PRPS1L1, F13A1, KLHL24, SSRP1, DDX24, KIF1B, RRP7A, MICALL1, C9orf16, SEPHS1, DMAC2L, ITGA2B, PURA, PAFAH1B3, PDXK, ARAF, TBCD, UBA1, EED, PARVB, RCN2, PGAP3, REX1BD (also known as 619orf60), MED27, PIK 3IP1, YTHDF3, BHMT2, ASF1A, ANXA8, ETFA, NMT1, EPHB3, KIF3C, LOH11CR2A (also known as VWA5A ), SLC48A1, MAPKAPK5-AS1, PLA2G4B, CALHM2, SENP5, SIDT2, R3HDM4, MARK4, SSH3, ATOH1, AXIN2, TAS2R13, PCDHB1, VWA7, TRIM49, CNTD2, TSHZ2, CDHR5, KIF26B, PADI4, TRIM36, LGI2, KCNMB4, TTTY14, ELAVL3, PAGE4, ​​PER2, ZNF142, CD4, CCS, NELL2, RNF44, KLHL21, DNAJB12, CDC123, GNAI3, TRADD and / or THRA.

54. The at least two mRNAs are at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 52. The method of claim 51 , wherein the mRNA comprises 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different mRNAs.

55. 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72 52. The method of claim 51 , wherein the mRNA comprises no more than 4, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126 and / or 127 different mRNAs.

Images