Marker for discriminating benign and malignant pulmonary nodules and application thereof

Abstract

The invention relates to a marker for discriminating benign and malignant pulmonary nodules and application of the marker. The marker comprises, but is not limited to, a gene CEACAM4, a gene S100A9, a gene FCGR1A, a gene ROGDI, a gene ZNF266, a gene TNFSF10, a gene MORF, a gene MCEMP1, a gene TRAF5, a gene SLC38A1, a gene TARBP1, a gene SMA4, a gene HNRNPD or a gene SMC1A.

Description

[0001] This application is a divisional application of the patent application with application number 202010368425.3 and application date April 30, 2020. Technical Field

[0002] The present invention relates to the technical field of molecular biology, and relates to the identification of benign and malignant pulmonary nodules, and specifically to markers and uses thereof for the identification of benign and malignant pulmonary micronodules. Background Art

[0003] The prognosis of lung cancer is closely related to the clinical stage at the time of diagnosis. The treatment effect is best for ultra-early stage Ia peripheral in situ lung cancer, with a 5-year survival rate of up to 90% after surgery, while the overall 5-year survival rate of patients in stages II-IV drops from 34% to less than 5%. Therefore, the key to improving the cure rate of lung cancer and reducing the mortality rate is early detection.

[0004] Currently, the use of high-resolution imaging examination methods such as low-dose spiral CT (LDCT) to examine high-risk groups is a major means of early screening for lung cancer. LDCT has a high sensitivity. In large-scale early screening for lung cancer, about 20-30% of the subjects will be found to have micro-nodules (micro-nodules, nodules less than 10mm in size), but only 3.8% of them are malignant. At present, it is very difficult to judge the benign or malignant nature of micro-nodules in the clinic. This is mainly because the size of micro-nodules is less than 10mm, making it difficult to perform biopsy and pathological examinations through fine needle puncture. Even if PET-CT examinations are performed, the diagnostic value of the results obtained is very limited.

[0005] Blood is the largest tissue organ in the human body, and blood cells are one of the few cell types that can exchange information with almost all tissue cells. If tissues and organs in the body are damaged, inflamed, or have malignant diseases such as tumors, a series of specific changes will occur in the microenvironment around the diseased tissue cells. When blood flows through various tissues and organs, the microenvironment of the diseased tissue cells exchanges information with blood cells, and blood cells will directly or indirectly respond to these changes, undergo corresponding gene expression changes, and participate in the information transmission and exchange of various systems in the body such as the immune system. This gene expression change of blood cells occurs long before the body produces obvious signs and changes, and includes some disease-specific gene expression changes. Therefore, by closely monitoring the expression spectrum of blood cell genes, molecular information of early malignant diseases such as tumors in the body can be sensitively captured, and disease-specific gene expression signals (markers) can be screened out, providing reliable evidence for early detection and monitoring of the disease. Moreover, as a simple and non-invasive examination method, peripheral blood gene expression detection is easy for the examinees to accept and has high compliance, and has great application value for early screening / diagnosis of malignant tumors.

[0006] Serum tumor marker tests related to lung cancer, such as carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), squamous cell carcinoma antigen (SCC-Ag), cytokeratin 19 fragment, etc., have certain reference value for auxiliary diagnosis of advanced lung cancer, but have little diagnostic value for stage Ia lung cancer. In addition, serum protein markers have poor specificity for lung cancer detection. Other benign lesions such as pneumonia can also cause abnormal protein marker concentrations, leading to false positive test results. Therefore, in order to accurately distinguish between benign and malignant micronodules and detect ultra-early micronodule lung cancer as early as possible, the art needs a blood test technology and product that can accurately distinguish between benign and malignant micronodules. Summary of the invention

[0007] In view of the lack of biomarkers for accurately distinguishing benign and malignant pulmonary nodules, especially micronodules, in the prior art, the present invention provides a peripheral blood gene marker suitable for distinguishing benign and malignant pulmonary nodules. The marker has good sensitivity and specificity for distinguishing benign and malignant pulmonary nodules, and can accurately distinguish malignant nodules, which is of great significance for timely diagnosis, improving prognosis, and reducing mortality. Using the marker of the present invention, only 2 ml of peripheral venous blood needs to be collected for detection, and the detection process is simple, which is particularly suitable for ultra-early lung cancer screening for large-scale populations in conjunction with imaging examinations such as CT.

[0008] The present invention also provides a method for screening the above markers, and the markers obtained by this method have good sensitivity and specificity for lung nodules.

[0009] The present invention also provides a model for identifying micro-nodules in the lungs and a method for constructing the model.

[0010] Therefore, the present invention provides an isolated nucleic acid molecule, which is a lung nodule marker, wherein the nucleic acid molecule is 13-15000 bp in length, and the nucleic acid molecule has a DNA sequence selected from one, any multiple or all of the following, or a variant thereof having at least 70%, at least 80%, at least 90%, at least 95%, at least 97%, at least 99% identity, or a corresponding RNA sequence of the DNA sequence or variant: (1) the coding sequence of CEACAM4 or a fragment thereof matching SEQ ID NO: 43, or a complementary sequence thereof, (2) the coding sequence of S100A9 or a fragment thereof matching SEQ ID NO: 44, or a complementary sequence thereof, (3) the coding sequence of FCGR1A or a fragment thereof matching SEQ ID NO: 45, or a complementary sequence thereof, (4) the coding sequence of ROGDI or a fragment thereof matching SEQ ID NO: 46, or a complementary sequence thereof, (5) the coding sequence of ZNF266 or a fragment thereof matching SEQ ID NO: 47, or a complementary sequence thereof, (6) the coding sequence of TNFSF10 or a fragment thereof matching SEQ ID NO: NO:48, or their complementary sequences, (7) the coding sequence of MORF or a fragment thereof matching SEQ ID NO:49, or their complementary sequences, (8) the coding sequence of MCEMP1 or a fragment thereof matching SEQ ID NO:50, or their complementary sequences, (9) the coding sequence of TRAF5 or a fragment thereof matching SEQ ID NO:51, or their complementary sequences, (10) the coding sequence of SLC38A1 or a fragment thereof matching SEQ ID NO:52, or their complementary sequences, (11) the coding sequence of TARBP1 or a fragment thereof matching SEQ ID NO:53, or their complementary sequences, (12) the coding sequence of SMA4 or a fragment thereof matching SEQ ID NO:54, or their complementary sequences, (13) the coding sequence of HNRNPD or a fragment thereof matching SEQ ID NO:55, or their complementary sequences, and (14) the coding sequence of SMC1A or a fragment thereof matching SEQ ID NO:56, or their complementary sequences.

[0011] In one or more embodiments, the nucleic acid molecule is DNA or RNA.

[0012] In one or more embodiments, the length of the nucleic acid molecule is 13-50 bp, 13-45 bp, 13-40 bp, 13-35 bp, 13-31 bp.

[0013] In one or more embodiments, the length of the nucleic acid molecule is 13-14000 bp, 13-13000 bp, 13-12000 bp, 13-11000 bp, 13-10000 bp, 13-9710 bp.

[0014] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule.

[0015] In one or more embodiments, the pulmonary nodule is a pulmonary nodule having a diameter less than 10 mm.

[0016] In one or more embodiments, the coding sequence of CEACAM4 is as shown in SEQ ID NO:1.

[0017] In one or more embodiments, the coding sequence of S100A9 is as shown in SEQ ID NO:2.

[0018] In one or more embodiments, the coding sequence of FCGR1A is shown in SEQ ID NO:3.

[0019] In one or more embodiments, the coding sequence of ROGDI is shown in SEQ ID NO:4.

[0020] In one or more embodiments, the coding sequence of ZNF266 is shown in SEQ ID NO:5.

[0021] In one or more embodiments, the coding sequence of TNFSF10 is as shown in SEQ ID NO:6.

[0022] In one or more embodiments, the coding sequence of MORF is shown as SEQ ID NO:7.

[0023] In one or more embodiments, the coding sequence of MCEMP1 is shown as SEQ ID NO:8.

[0024] In one or more embodiments, the coding sequence of TRAF5 is shown in SEQ ID NO:9.

[0025] In one or more embodiments, the coding sequence of SLC38A1 is as shown in SEQ ID NO:10.

[0026] In one or more embodiments, the coding sequence of TARBP1 is shown in SEQ ID NO:11.

[0027] In one or more embodiments, the coding sequence of SMA4 is shown as SEQ ID NO:12.

[0028] In one or more embodiments, the coding sequence of HNRNPD is shown in SEQ ID NO:13.

[0029] In one or more embodiments, the coding sequence of SMC1A or is as shown in SEQ ID NO:14.

[0030] In one or more embodiments, the nucleic acid molecule has a DNA sequence selected from any of the following groups, or a variant thereof having at least 70%, at least 80%, at least 90%, at least 95%, at least 97%, at least 99% identity, or their corresponding RNA sequences:

[0031] a1: (3), (7), (8), (12) and (13),

[0032] a2: (1), (7), (8), (10) and (11),

[0033] a3: (1), (5), (7), (9) and (11),

[0034] a4: (3), (6), (7), (8), (11) and (12),

[0035] a5: (1), (3), (6), (8), (11) and (13),

[0036] a6: (6), (9), (10), (11), (12) and (14),

[0037] a7: (5), (6), (7), (8), (9), (10) and (13),

[0038] a8: (4), (8), (9), (10), (11), (13) and (14),

[0039] a9: (1), (3), (5), (7), (10), (12) and (13),

[0040] a10: (2), (4), (7), (9), (10), (12) and (13),

[0041] a11: (5), (6), (7), (8), (9), (10) and (13),

[0042] a12: (3), (7), (8), (9), (11), (12), (13) and (14),

[0043] a13: (1), (4), (7), (8), (9), (10), (12) and (13),

[0044] a14: (6), (7), (8), (9), (10), (11), (12) and (14),

[0045] a15: (1), (3), (4), (7), (9), (10), (12), (13) and (14),

[0046] a16: (1), (4), (5), (8), (9), (10), (12), (13) and (14); a17: (2), (3), (8), (9), (10), (11), (12), (13) and (14);

[0047] a18: (3), (4), (6), (7), (8), (10), (11), (12), (13) and (14),

[0048] a19: (1), (2), (3), (4), (5), (9), (10), (11), (13) and (14),

[0049] a20: (1), (2), (4), (5), (6), (8), (9), (10), (11) and (14),

[0050] a21: (1), (2), (4), (5), (6), (7), (8), (11), (12),

[0051] (13) and (14),

[0052] a22: (1), (2), (3), (6), (8), (9), (10), (11), (12),

[0053] (13) and (14),

[0054] a23: (1), (2), (3), (4), (5), (6), (7), (8), (12),

[0055] (13) and (14),

[0056] a24: (3), (4), (5), (6), (7), (8), (9), (10), (11),

[0057] (12), (13) and (14),

[0058] a25: (1), (2), (3), (4), (6), (7), (8), (9), (10),

[0059] (11), (13) and (14),

[0060] a26: (1), (3), (4), (5), (6), (7), (8), (9), (10),

[0061] (11), (12) and (13),

[0062] a27: (1), (2), (3), (4), (5), (7), (8), (9), (10),

[0063] (11)(12), (13) and (14),

[0064] a28: (1), (2), (3), (4), (5), (6), (7), (8), (9),

[0065] (10), (12), (13) and (14),

[0066] a29: (1), (2), (3), (4), (5), (6), (7), (8), (9),

[0067] (10), (11), (13) and (14), or

[0068] a30: (1), (2), (3), (4), (5), (6), (7), (8), (9),

[0069] (10), (11), (12), (13) and (14).

[0070] In one or more embodiments, the nucleic acid molecule has a sequence as shown in SEQ ID NO: 1-14 or a corresponding RNA sequence thereof. In one or more embodiments, the nucleic acid molecule has a sequence as shown in SEQ ID NO: 43-56 or a corresponding RNA sequence thereof.

[0071] In one or more of the above embodiments, the DNA sequence includes the sense strand or antisense strand of genomic DNA.

[0072] In one or more of the above embodiments, the nucleic acid molecule is used as an internal standard or control for detecting the transcription level, translation level, content, or methylation of the corresponding DNA sequence or RNA sequence in a sample.

[0073] The second aspect of the present invention provides a reagent for detecting

[0074] (1) the transcription level, translation level, content or methylation of one or more of the nucleic acid molecules described in the first aspect of this invention; or

[0075] (2) The content or activity of the expression product of (1).

[0076] In one or more embodiments, the sample is from a mammal, preferably a human.

[0077] In one or more embodiments, the reagent is a reagent used in one or more of the following methods: PCR, DNA sequencing, RNA sequencing, DNA hybridization, RNA hybridization, Southern, Northern, Western, immunoprecipitation, ELISA, restriction endonuclease analysis, high-resolution melting curve method, mass spectrometry, preferably RT-qPCR, RNA sequencing or chip-based methods.

[0078] In one or more embodiments, the reagent is selected from one or more of the following: buffer, reverse transcriptase, polymerase, dNTP, primer, probe, restriction endonuclease, fluorescent dye, fluorescence quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard, control.

[0079] In one or more embodiments, the reagent is a primer. In one or more embodiments, the primer recognizes one or more of the nucleic acid molecules described in the first aspect of the present invention. In one or more embodiments, the primer can be a primer for genome sequencing, such as a full genome sequencing primer or a sequencing primer for a portion of a genome, or a primer for amplifying the nucleic acid molecules described herein. Preferably, the primer has a sequence shown in one or more or all of SEQ ID NOs: 15-42.

[0080] In one or more embodiments, the reagent is a nucleic acid probe. In one or more embodiments, the probe hybridizes with the nucleic acid molecule described in the first aspect of this invention under stringent conditions. Preferably, the two ends of the sequence of the probe respectively contain a fluorescent reporter group and a quencher group. Preferably, the nucleic acid probe has a sequence shown in one or more or all of SEQ ID NOs: 43-56 or its corresponding RNA sequence.

[0081] The present invention also provides a composition comprising the reagent described in the second aspect of this invention. Preferably, the composition comprises the primer and / or nucleic acid probe described in the second aspect of this invention.

[0082] The present invention also provides a gene chip comprising the nucleic acid probes and / or polypeptide probes described herein.

[0083] The present invention also provides a kit, comprising the reagent described in the second aspect of this article and the optional nucleic acid molecule described in the first aspect of this article. The kit is used to distinguish benign and malignant pulmonary nodules. In one or more embodiments, the pulmonary nodules are pulmonary micronodules. In one or more embodiments, the pulmonary nodules are pulmonary nodules with a diameter of less than 10 mm.

[0084] In one or more embodiments, the kit further comprises primers and / or probes for detecting a reference sequence. Preferably, the reference sequence is a sequence of GAPDH or a fragment thereof. Preferably, the primers for the reference sequence have the sequences shown in SEQ ID NO:57 and / or 58; the probes for the reference sequence have the sequences shown in SEQ ID NO:59 or their corresponding RNA sequences.

[0085] In one or more embodiments, the kit further comprises a fluorescent probe that specifically binds to the PCR amplified fragment or a dye that non-specifically binds to the fragment.

[0086] The third aspect of the present invention also provides the use of one or more or all of the following genes or their DNA or RNA sequences in the animal genome in the preparation of reagents and / or kits for distinguishing benign and malignant lung nodules: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A.

[0087] In one or more embodiments, the reagent detects (1) the transcription level, translation level, content or methylation of the gene in the sample; or (2) the content or activity of the expression product of (1); preferably, the reagent is a primer or a probe.

[0088] In one or more embodiments, the DNA or RNA sequence of the gene in the animal genome is as described in the nucleic acid molecule of the first aspect herein.

[0089] In one or more embodiments, the gene is selected from any of the following groups:

[0090] a1: FCGR1A, MCEMP1, MORF, SMA4 and HNRNPD,

[0091] a2: CEACAM4, MCEMP1, MORF, SLC38A1 and TARBP1,

[0092] a3: CEACAM4, TRAF5, MORF, ZNF266 and TARBP1,

[0093] a4: FCGR1A, MCEMP1, MORF, TARBP1, TNFSF10 and SMA4,

[0094] a5: CEACAM4, FCGR1A, MCEMP1, TARBP1, TNFSF10 and HNRNPD,

[0095] a6: SLC38A1, TARBP1, TNFSF10, TRAF5, SMA4 and SMC1A,

[0096] a7: HNRNPD, TNFSF10, SLC38A1, MCEMP1, ZNF266, TRAF5 and MORF,

[0097] a8: MCEMP1, ROGDI, SLC38A1, TARBP1, TRAF5, HNRNPD and SMC1A,

[0098] a9: CEACAM4, FCGR1A, MORF, SLC38A1, ZNF266, SMA4 and HNRNPD,

[0099] a10: MORF, ROGDI, S100A9, SLC38A1, TRAF5, SMA4 and HNRNPD,

[0100] a14: TRAF5, MCEMP1, MORF, SLC38A1, TNFSF10, ZNF266 and HNRNPD,

[0101] a12: FCGR1A, MCEMP1, MORF, TARBP1, TRAF5, SMA4, HNRNPD and SMC1A,

[0102] a13: CEACAM4, MCEMP1, MORF, ROGDI, SLC38A1, TRAF5, SMA4, and HNRNPD,

[0103] a14: MCEMP1, MORF, SLC38A1, TARBP1, TNFSF10, TRAF5, SMA4 and SMC1A,

[0104] a15: CEACAM4, FCGR1A, MORF, ROGDI, SLC38A1, TRAF5, SMA4, HNRNPD and SMC1A,

[0105] a16: CEACAM4, MCEMP1, ROGDI, SLC38A1, TRAF5, ZNF266, SMA4, HNRNPD, and SMC1A,

[0106] a17: FCGR1A, A, C, EMP1, S100A9, SLC38A1, TARBP1, TRAF5, SMA4, HNRNPD, and SMC1A,

[0107] a18: FCGR1A, MCEMP1, MORF, ROGDI, SLC38A1, TARBP1, TNFSF10, SMA4, HNRNPD, and SMC1A,

[0108] a19: CEACAM4, FCGR1A, ROGDI, S100A9, SLC38A1, TARBP1, TRAF5, ZNF266, HNRNPD, and SMC1A,

[0109] a20: CEACAM4, MCEMP1, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, and SMC1A,

[0110] a21: CEACAM4, MCEMP1, MORF, ROGDI, S100A9, TARBP1, TNFSF10, ZNF266, SMA4, HNRNPD, and SMC1A,

[0111] a22: CEACAM4, FCGR1A, MCEMP1, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, SMA4, HNRNPD, and SMC1A,

[0112] a23: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, TNFSF10, ZNF266, SMA4, HNRNPD, and SMC1A,

[0113] a24: FCGR1A, MCEMP1, MORF, ROGDI, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, SMA4, HNRNPD, and SMC1A,

[0114] a25: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, HNRNPD, and SMC1A,

[0115] a26: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, SMA4, and HNRNPD,

[0116] a27: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TRAF5, ZNF266, SMA4, HNRNPD and SMC1A,

[0117] a28: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TNFSF10, TRAF5, ZNF266, SMA4, HNRNPD and SMC1A,

[0118] a29: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, HNRNPD, and SMC1A or

[0119] a30: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, SMA4, HNRNPD and SMC1A.

[0120] In one or more embodiments, the animal is a mammal, preferably a human.

[0121] In one or more embodiments, the genome is human genome hg19.

[0122] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0123] The present invention also provides use of the nucleic acid molecule according to claim 1 in preparing a reagent and / or a kit for distinguishing benign or malignant pulmonary nodules.

[0124] The present invention also provides the use of the reagents described herein and optionally the nucleic acid molecules described herein in the preparation of a kit for distinguishing benign and malignant lung nodules, wherein the reagent detects (1) the transcription level, translation level, content or methylation of one or more of the nucleic acid molecules described in the first aspect of the present invention in a sample; or (2) the content or activity of the expression product of (1).

[0125] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0126] In one or more embodiments of the use, the sample is from a mammal, preferably a human. In one or more embodiments, the sample is from a tissue, a cell or a body fluid, such as a non-tissue or blood. In one or more embodiments, the sample comprises plasma, serum or blood.

[0127] In one or more embodiments, the reagent is a reagent used in one or more of the following methods: PCR, DNA sequencing, RNA sequencing, DNA hybridization, RNA hybridization, Southern, Western, immunoprecipitation, ELISA, restriction endonuclease analysis, high resolution melting curve method, mass spectrometry, preferably RT-qPCR, RNA sequencing or chip-based methods.

[0128] In one or more embodiments, the reagent is selected from one or more of the following: buffer, polymerase, dNTP, primer, probe, restriction endonuclease, fluorescent dye, fluorescence quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard, control.

[0129] In one or more embodiments, the reagent is a primer. In one or more embodiments, the primer recognizes one or more of the nucleic acid molecules described in the first aspect of the present invention. In one or more embodiments, the primer can be a primer for genome sequencing, such as a full genome sequencing primer or a sequencing primer for a portion of a genome, or a PCR primer for amplifying the nucleic acid molecules described herein.

[0130] In one or more embodiments, the reagent is a nucleic acid probe. In one or more embodiments, the probe hybridizes with the nucleic acid molecule described in the first aspect of the present invention under stringent conditions. Preferably, the two ends of the sequence of the probe contain a fluorescent reporter group and a quencher group, respectively.

[0131] In one or more embodiments, the agent is a polypeptide probe. In one or more embodiments, the polypeptide probe specifically binds to the expression product of the nucleic acid molecule described in the first aspect of the present invention.

[0132] In one or more embodiments, the kit is a real-time quantitative PCR kit, an RNA sequencing kit, or a kit containing a gene chip.

[0133] In one or more embodiments, the real-time quantitative PCR kit comprises primers that specifically amplify one or more sequences shown in SEQ ID NOs: 1 to 14. Preferably, the primers have sequences shown in one or more or all of SEQ ID NOs: 15 to 42.

[0134] In one or more embodiments, the kit containing a gene chip comprises a nucleic acid probe that hybridizes under stringent conditions with one or more of the sequences shown in SEQ ID NOs: 1 to 14. Preferably, the nucleic acid probe has a sequence shown in one or more or all of SEQ ID NOs: 43 to 56 or its corresponding RNA sequence.

[0135] The present invention also provides a method for distinguishing benign and malignant pulmonary nodules, comprising:

[0136] (1) obtaining expression detection results of one or more or all of the following genes in the sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A,

[0137] (2) obtaining a score by constructing a model using the expression detection results,

[0138] (3) Differentiate between benign and malignant pulmonary nodules based on the scores.

[0139] In one or more embodiments, the expression detection result is the RNA content, expression product content, or RT-PCR result of the gene.

[0140] In one or more embodiments, the expression detection result is the detection result of the nucleic acid molecule or its expression product described in the first aspect of this article, preferably the RNA content, the content of the nucleic acid molecule expression product, or the RT-PCR result of the nucleic acid molecule.

[0141] In one or more embodiments, step (1) includes testing the sample to obtain the expression detection result.

[0142] In one or more embodiments, the gene of step (1) is selected from any one of the groups a1-a30 described in the third aspect herein.

[0143] In one or more embodiments, the sample is a lung tissue sample or a lung nodule sample.

[0144] In one or more embodiments, the sample is blood, serum or plasma. Preferably, the sample is peripheral blood, serum or plasma.

[0145] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0146] In one or more embodiments, the model in step (2) is a logistic regression model.

[0147] In one or more embodiments, the model is as follows:

[0148]

[0149] therefore,

[0150]

[0151] Among them, ΔCt 1 to ΔCt n are the difference between the quantitative PCR cycle number Ct value of the nth nucleic acid molecule and the reference sequence, and b 0 To b n are the corresponding logistic regression model parameters respectively; σ(z) or P is the score.

[0152] In one or more embodiments, step (3) includes: when the score meets the threshold, the pulmonary nodule is identified as benign or malignant. In one or more embodiments, the pulmonary nodule is identified as malignant if the score is greater than or equal to the threshold, and the pulmonary nodule is identified as benign if the score is less than the threshold.

[0153] The present invention further provides a device, characterized in that the device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the program, the following steps are implemented:

[0154] (1) obtaining expression detection results of one or more or all of the following genes in the sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A,

[0155] (2) obtaining a score by constructing a model using the expression detection results,

[0156] (3) Differentiate between benign and malignant pulmonary nodules based on the scores.

[0157] In one or more embodiments, the expression detection result is the RNA content, expression product content, or RT-PCR result of the gene.

[0158] In one or more embodiments, the expression detection result is the detection result of the nucleic acid molecule or its expression product described in the first aspect of this article, preferably the RNA content, the content of the nucleic acid molecule expression product, or the RT-PCR result of the nucleic acid molecule.

[0159] In one or more embodiments, step (1) comprises testing the sample to obtain the test result.

[0160] In one or more embodiments, the sample is a lung tissue sample or a lung nodule sample.

[0161] In one or more embodiments, the sample is blood, serum or plasma. Preferably, the sample is peripheral blood, serum or plasma.

[0162] In one or more embodiments, the model in step (2) is a logistic regression model.

[0163] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0164] In one or more embodiments, the gene of step (1) is selected from any one of the groups a1-a30 described in the third aspect herein.

[0165] The present invention further provides a computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and when the computer program is executed by a processor, the following steps are implemented:

[0166] (1) obtaining expression detection results of one or more or all of the following genes in the sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A,

[0167] (2) obtaining a score by constructing a model using the expression detection results,

[0168] (3) Differentiate between benign and malignant pulmonary nodules based on the scores.

[0169] In one or more embodiments, the expression detection result is the RNA content, expression product content, or RT-PCR result of the gene.

[0170] In one or more embodiments, the expression detection result is the detection result of the nucleic acid molecule or its expression product described in the first aspect of this article, preferably the RNA content, the content of the nucleic acid molecule expression product, or the RT-PCR result of the nucleic acid molecule.

[0171] In one or more embodiments, step (1) comprises testing the sample to obtain the test result.

[0172] In one or more embodiments, the sample is a lung tissue sample or a lung nodule sample.

[0173] In one or more embodiments, the sample is blood, serum or plasma. Preferably, the sample is peripheral blood, serum or plasma.

[0174] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0175] In one or more embodiments, the model in step (2) is a logistic regression model.

[0176] In one or more embodiments, the gene of step (1) is selected from any one of the groups a1-a30 described in the third aspect herein.

[0177] The present invention also provides a system for distinguishing benign and malignant pulmonary nodules, which is characterized by comprising:

[0178] A collection device for obtaining expression detection results of one or more or all of the following genes in a sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A, preferably the genes are selected from any one of the groups a1-a30 described in the third aspect of this invention,

[0179] A data processing device, used to obtain a score by constructing a model using the detection result,

[0180] The determination device is used to distinguish benign or malignant pulmonary nodules based on the scores.

[0181] In one or more embodiments, the collection device is a device for inputting the test results.

[0182] In one or more embodiments, the collection device is a detection device. The detection device includes a sample processing device and a measurement device.

[0183] In one or more embodiments, the expression detection result is the RNA content, expression product content, or RT-PCR result of the gene.

[0184] In one or more embodiments, the expression detection result is the detection result of the nucleic acid molecule or its expression product described in the first aspect of this article, preferably the RNA content, the content of the nucleic acid molecule expression product, or the RT-PCR result of the nucleic acid molecule.

[0185] In one or more embodiments, step (1) comprises testing the sample to obtain the test result.

[0186] In one or more embodiments, the sample is a lung tissue sample or a lung nodule sample.

[0187] In one or more embodiments, the sample is blood, serum or plasma. Preferably, the sample is peripheral blood, serum or plasma.

[0188] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0189] In one or more embodiments, the model in step (2) is a logistic regression model.

[0190] The present invention also provides a method for screening genes for distinguishing benign and malignant pulmonary nodules, comprising:

[0191] (1) Obtaining gene expression data of malignant pulmonary nodule samples and benign pulmonary nodule samples,

[0192] (2) Select genes expressed in all samples for T-test analysis.

[0193] (3) Compare the gene expression differences between malignant pulmonary nodule samples and benign pulmonary nodule samples, and select genes with a statistical P value less than 0.05 and a gene expression change of more than 1.1 times as candidate genes.

[0194] Optional (4) sorting the gene expression data according to the correlation between the benign and malignant nature of the sample, and selecting genes with a high correlation with malignant pulmonary nodules as candidate genes,

[0195] (5) Using logistic regression statistical analysis to screen candidate genes,

[0196] (6) screening the candidate genes obtained in step (5) for genes with consistent expression changes by real-time fluorescence quantitative PCR and gene chip methods,

[0197] Optionally (7) construct a model using logistic regression statistical analysis to evaluate the role of the genes obtained in step (5) in distinguishing benign and malignant lung nodules.

[0198] In one or more embodiments, the gene expression data is the content of RNA or the content of nucleic acid molecule expression products.

[0199] In one or more embodiments, the gene for distinguishing benign and malignant pulmonary nodules is selected from one or more or all of the following: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A. In one or more embodiments, the gene for distinguishing benign and malignant pulmonary nodules is selected from any one of the groups a1-a30 described in the third aspect of this invention.

[0200] In one or more embodiments, the correlation in step (4) is expressed as a Pearson correlation coefficient.

[0201] In one or more embodiments, step (5) includes constructing a model for the candidate genes using a logistic regression statistical analysis method, and screening the candidate genes according to the AUC size.

[0202] In one or more embodiments, the model of steps (5) and / or (7) is as follows

[0203]

[0204] Among them, ΔCt 1 to ΔCt n are the differences in the Ct values ​​of the quantitative PCR cycles between n markers and the reference sequence, and b 0 To b n are the corresponding logistic regression model parameters respectively; σ(z) or P is the score.

[0205] In one or more embodiments, the sample is a lung tissue sample or a lung nodule sample.

[0206] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0207] In one or more embodiments, the sample is blood, serum or plasma. Preferably, the sample is peripheral blood, serum or plasma.

[0208] The present invention also provides a method for constructing a model for distinguishing benign and malignant pulmonary nodules, comprising:

[0209] (1) obtaining expression detection results of one or more or all of the following genes in the sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A,

[0210] (2) Calculate the ROC curve and area under the curve for each candidate gene combination, and establish a model using logistic regression statistical analysis method.

[0211] In one or more embodiments, the pulmonary nodule is a pulmonary micronodule. In one or more embodiments, the pulmonary nodule is a pulmonary nodule with a diameter less than 10 mm.

[0212] In one or more embodiments, the detection result is the content of RNA, the content of the expression product of the nucleic acid molecule, or the difference in the quantitative PCR cycle number Ct value between the nucleic acid molecule and the internal reference gene.

[0213] In one or more embodiments, step (1) comprises testing the sample to obtain the test result.

[0214] In one or more embodiments, the model is as follows

[0215]

[0216] Among them, ΔCt 1 to ΔCt n are the differences in the Ct values ​​of the quantitative PCR cycles between n markers and the reference sequence, and b 0 To b n are the corresponding logistic regression model parameters respectively; σ(z) or P is the score.

[0217] In one or more embodiments, the gene of step (1) is selected from any one of the groups a1-a30 described in the third aspect herein.

[0218] The present invention also provides a model for distinguishing benign and malignant pulmonary nodules, which is constructed according to the above method for constructing a model for distinguishing benign and malignant pulmonary nodules. In one or more embodiments, the pulmonary nodules are micropulmonary nodules. In one or more embodiments, the pulmonary nodules are pulmonary nodules with a diameter of less than 10 mm.

[0219] In one or more embodiments, the model is as follows

[0220]

[0221] Among them, ΔCt 1 to ΔCt n are the differences in the Ct values ​​of the quantitative PCR cycles between n markers and the reference sequence, and b 0 To b n are the corresponding logistic regression model parameters respectively; σ(z) or P is the score. BRIEF DESCRIPTION OF THE DRAWINGS

[0222] Figure 1The expression of different genes in benign and malignant samples is shown, with the gene as the horizontal axis and the RT-PCR expression value as the vertical axis.

[0223] Figure 2 : is the ROC curve of the exemplary embodiment, with the false positive rate (1-specificity) as the horizontal axis and the true positive rate (sensitivity) as the vertical axis. The closer the AUC is to 1, the better the diagnostic effect. DETAILED DESCRIPTION

[0224] After many attempts, the inventors finally determined that the expression differences of 14 genes can best show the differences between benign and malignant pulmonary nodules. These 14 genes are: CEACAM4 (SEQ ID NO: 1), S100A9 (SEQ ID NO: 2), FCGR1A (SEQ ID NO: 3), ROGDI (SEQ ID NO: 4), ZNF266 (SEQ ID NO: 5), TNFSF10 (SEQ ID NO: 6), MORF (SEQ ID NO: 7), MCEMP1 (SEQ ID NO: 8), TRAF5 (SEQ ID NO: 9), SLC38A1 (SEQ ID NO: 10), TARBP1 (SEQ ID NO: 11), SMA4 (SEQ ID NO: 12), HNRNPD (SEQ ID NO: 13) and SMC1A (SEQ ID NO: 14). In this article, the sequences shown and the sequence table are regarded as positive chains.

[0225] "Nodules" are a multi-system and multi-organ granulomatous disease of unknown etiology, often invading the lungs, bilateral hilar lymph nodes, eyes, skin and other organs. Pulmonary micronodules or small nodules are pulmonary nodules with a size of less than 10 mm. Pulmonary nodules can be malignant or benign, and malignant pulmonary nodules are also called lung cancer.

[0226] The "marker" described herein can be a nucleic acid molecule or its expression product. The nucleic acid molecule can be a nucleic acid molecule isolated from an animal, or can be an artificially synthesized nucleic acid molecule having an animal genome fragment sequence.

[0227] Herein, methods for detecting gene expression levels are well known in the art, such as PCR, DNA sequencing, RNA sequencing, DNA hybridization, RNA hybridization, Southern, Western, immunoprecipitation, ELISA, restriction endonuclease analysis, high-resolution melting curve method, mass spectrometry, preferably RT real-time quantitative PCR, RNA sequencing or chip-based methods.

[0228] Therefore, the present invention relates to reagents for detecting gene expression levels. The reagents used in the above-mentioned methods for detecting gene expression levels are well known in the art. Exemplarily, the reagents for detecting gene expression levels may include one or more of the following: buffer, polymerase, dNTP, primer, probe, restriction endonuclease, fluorescent dye, fluorescent quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard, control. In the detection method involving DNA amplification, the detection reagent includes a primer. The reagent for detecting gene expression levels can also be a polypeptide probe such as an antibody. The polypeptide probe specifically binds to the expression product of the nucleic acid molecule described herein, for example, the polypeptide probe is an antibody that specifically binds to the expression product of the nucleic acid molecule described in the first aspect of the present invention. The reagent for detecting gene expression levels can also be a nucleic acid probe. Generally, the 5' end of the sequence of the nucleic acid probe is labeled with a fluorescent reporter group, and the 3' end is labeled with a quenching group. The nucleic acid probe described herein can hybridize with the genomic sequence, coding sequence, RNA sequence or cDNA sequence of the gene described herein under stringent conditions. The stringent conditions for nucleic acid hybridization are known to those skilled in the art. Preferably, the conditions are such that sequences at least about 65%, 70%, 75%, 85%, 90%, 95%, 98% or 99% homologous to each other generally remain hybridized to each other. A non-limiting example of stringent hybridization conditions is hybridization at 65°C in a high salt buffer containing 6xSSC, 50 mM Tris-HCl (pH 7.5), 1 mM EDTA, 0.02% PVP, 0.02% Ficolll, 0.02% BSA and 500 mg / ml denatured salmon sperm DNA, and optionally washed once or twice at 50°C in 0.2xSSC, 0.01% BSA.

[0229] In an exemplary embodiment, the present invention uses RT-qPCR to detect gene expression. The steps and required reagents of conventional RT-qPCR are known in the art. Generally, the steps of RT-qPCR include: reverse transcription, denaturation, annealing, and extension. The reagents used in RT-qPCR include: buffer, reverse transcriptase, primers, probes, dNTPs, DNA polymerase, and controls.

[0230] Herein, the sample is from a mammal, preferably a human. The sample may be from any organ (e.g., lung), tissue (e.g., epithelial tissue, connective tissue, muscle tissue, and neural tissue), cell (e.g., lung nodule cell), or body fluid (e.g., blood, plasma, serum, tissue fluid, urine). In an exemplary embodiment, the sample is peripheral blood.

[0231] The present invention also relates to a kit for identifying pulmonary nodules, comprising the reagents described herein, in particular the reagents described in the second aspect of this invention. The kit may also comprise the nucleic acid molecules described herein, in particular the nucleic acid molecules described in the first aspect as internal standards or positive controls. In addition to the reagents and nucleic acid molecules, the kit may also comprise other reagents required for detecting expression. Exemplarily, the kit may comprise one or more of the following: tools and / or reagents for collecting samples from a subject, tools and / or reagents suitable for long-term storage of samples and / or controls, tools and / or reagents for pre-treating samples and / or controls.

[0232] The "primer" described herein refers to a nucleic acid molecule with a specific nucleotide sequence that guides synthesis at the start of nucleotide polymerization. Primers are usually two artificially synthesized oligonucleotide sequences, one primer is complementary to a DNA template strand at one end of the target region, and the other primer is complementary to another DNA template strand at the other end of the target region, and its function is to serve as the starting point of nucleotide polymerization. Primers designed artificially in vitro are widely used in polymerase chain reaction (PCR), qPCR, sequencing, and probe synthesis. Typically, primers are designed to amplify products with a length of 50 to 150 bp, 60-140, 70-130, 80-120 bp. The primers contained in the reagents herein can be primers for genome sequencing, such as whole genome sequencing primers or sequencing primers for a certain region of the genome, or PCR primers for amplifying genes or their specific DNA regions or fragments. The art knows the design method of whole genome sequencing primers or PCR primers for a certain region or a certain site in the region.

[0233] The term "variant" or "mutant" herein refers to a polynucleotide that changes the nucleic acid sequence by inserting, deleting or replacing one or more nucleotides compared to a reference sequence while retaining its ability to hybridize with other nucleic acids. The mutant described in any embodiment herein includes a nucleotide sequence that has at least 70%, preferably at least 80%, preferably at least 85%, preferably at least 90%, preferably at least 95%, preferably at least 97% sequence identity with the reference sequence and retains the biological activity of the reference sequence. The sequence identity between two aligned sequences can be calculated using, for example, NCBI's BLASTn. Mutants also include nucleotide sequences that have one or more mutations (insertions, deletions or substitutions) in the reference sequence and nucleotide sequence while still retaining the biological activity of the reference sequence. The multiple mutations generally refer to within 1-10, such as 1-8, 1-5 or 1-3. The substitution may be a substitution between a purine nucleotide and a pyrimidine nucleotide, or between a purine nucleotide or between a pyrimidine nucleotide. The substitution is preferably a conservative substitution. For example, in the art, conservative substitutions with nucleotides with similar or similar properties generally do not change the stability and function of the polynucleotide. Conservative substitutions include, for example, the exchange of (A and G) between purine nucleotides and the exchange of (T or U and C) between pyrimidine nucleotides. Therefore, replacing one or several sites with residues from the same residue in the polynucleotide of the present invention will not substantially affect its activity.

[0234] In certain specific embodiments, the markers of the present invention are screened by the following method: 1) peripheral blood samples are collected from patients with malignant micronodules and benign micronodules. All patients with micronodules were confirmed to have nodules less than or equal to 10 mm in size by CT scan, and 2-3 ml of peripheral blood was collected from each sample; 2) Gene chip was used to obtain the peripheral blood gene expression spectrum data of the samples, and the expression spectrum data was normalized; 3) Genes with moderate expression (signal values ​​between 100-10000) in all samples were selected for T-test analysis to compare the differences in peripheral blood gene expression between micronodule lung cancer and benign nodules, and genes with a statistical P value less than 0.05 and a gene expression fold change of more than 1.1 times were selected as candidate genes for subsequent analysis; 4) the correlation between the above candidate genes and micronodule lung cancer was analyzed, and the genes were ranked according to the correlation coefficient with micronodule lung cancer, and a group of gene cohorts with high correlation with micronodule lung cancer were selected (gene cohort I); in addition, the remaining The remaining genes were analyzed for correlation with the genes in cohort I, and another group of gene cohorts (gene cohort II) with high correlation with gene cohort I was selected; then the genes in gene cohort I and gene cohort II were paired in pairs to form a series of candidate gene combinations; 5) The logistic regression statistical analysis method was used to evaluate the discrimination effect of each candidate gene combination on micronodular lung cancer, and the receiver operating characteristic curve (ROC curve) and area under the curve value AUC of each candidate gene combination were calculated to screen out a series of gene combinations with good discrimination ability for micronodular lung cancer; 6) The screened series of gene combinations were verified by real-time fluorescence quantitative PCR method, and the genes with consistent expression changes in quantitative PCR and gene expression profile chip detection were retained as peripheral blood characteristic genes of micronodular lung cancer. The significance of the 14 lung cancer characteristic genes was shown in Figure 1 8) Use logistic regression statistical analysis to evaluate the diagnostic effect of any combination of the above 14 genes on micronodular lung cancer, calculate the ROC AUC of the gene combination, and establish the micronodular lung cancer discrimination model shown below:

[0235]

[0236] Among them, σ(z) or P is the risk value of developing micronodular lung cancer (malignant pulmonary nodules), i.e., the score; ΔCt 1 to ΔCt n are the differences in the Ct values ​​of the quantitative PCR cycles between n markers and the reference sequence, and b 0 To b n are the corresponding logistic regression model parameters respectively; z is the log likelihood ratio of logistic regression.

[0237] In one or more embodiments, when compared with the control sample, the expression level of the subject sample increases or decreases. The expression of the measured gene is mathematically analyzed to obtain a score. For the sample detected, when the score is greater than the threshold, the result is determined to be positive, i.e., a malignant nodule, otherwise it is negative, i.e., a benign nodule. The method of conventional mathematical analysis and the process of determining the threshold are known in the art, and an exemplary method is a mathematical model, such as a logistic regression model, a support vector machine model, and a random forest model. For example, for differentially expressed markers, a logistic regression model, a support vector machine model, or a random forest model is constructed for two groups of samples, and the accuracy, sensitivity and specificity of the test results and the area under the predictive characteristic curve (ROC) (AUC) of the model statistics are used to statistically test the test set sample prediction score.

[0238] Those skilled in the art are aware of conventional methods for constructing logistic regression models. An exemplary model is as follows:

[0239]

[0240] In addition, the present invention also discloses a computer-readable storage medium storing a computer program, and the computer program stored on the storage medium executes the method for distinguishing benign and malignant pulmonary nodules described herein after running. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be directly embodied in hardware, in a software module executed by a processor, or in a combination of the two. If implemented as a computer program product in software, each function can be stored on a computer-readable medium or transmitted by it as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium can be integrated into the processor. The processor and the storage medium can reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in a user terminal.

[0241] Example

[0242] The present invention is further described in detail below in conjunction with the accompanying drawings and specific examples. In the following examples, the experimental methods without specifying specific conditions are usually carried out according to the methods described in conventional conditions.

[0243] Example 1: Screening of peripheral blood characteristic gene markers for lung cancer

[0244] The screening of lung cancer characteristic genes includes the following steps:

[0245] 1) Peripheral blood samples were collected from 48 patients with malignant micronodules diagnosed with lung cancer by surgery and pathological examination and 38 patients with benign micronodules (benign lung lesions). All patients with micronodules had nodules less than or equal to 10 mm in size confirmed by CT examination, and 2-3 ml of peripheral blood was collected from each sample.

[0246] 2) Total RNA from the above samples was extracted using the PAXgene Blood RNA Kit, and the fragment integrity (RIN) of the RNA samples was detected using the Agilent Bioanalyzer 2100 bioanalyzer, and the purity of the RNA samples was detected using the Nano1000 micro-volume UV spectrophotometer. All RNA samples must meet the following quality control conditions: RNA yield greater than 2 micrograms, 28S / 18S peak ratio greater than 1, RIN value greater than 7, and absorbance ratio at 260nm / 280nm greater than 1.8.

[0247] 3) The above peripheral blood total RNA samples were detected using Affymetrix Gene Profiling Array U133Plus2 chip (human whole gene expression profile chip) to obtain the peripheral blood gene expression profile data of the samples, and then the peripheral blood gene expression profile data were normalized using the MAS5 method in Affymetrix Expression Console software to eliminate the systematic errors that may occur during the expression profile chip detection process and obtain peripheral blood gene expression profile data that can be uniformly compared.

[0248] 4) Gene expression signals that were too high or too low in the peripheral blood gene expression profile were eliminated, and genes with moderate expression (signal values ​​between 100 and 10,000) in all samples were selected for T-test analysis to compare the differences in peripheral blood gene expression between micronodule lung cancer and benign nodules. Genes with a statistical P value less than 0.05 and a gene expression fold change of more than 1.1 times were selected as candidate genes for subsequent analysis.

[0249] 5) Analyze the correlation between the above candidate genes and micronodule lung cancer, sort the genes according to the correlation coefficient with micronodule lung cancer, and select a group of gene cohorts (gene cohort I) that are highly correlated with micronodule lung cancer; in addition, analyze the correlation between the remaining genes and the genes in cohort I, and select another group of gene cohorts (gene cohort II) that are highly correlated with gene cohort I. Then pair the genes in gene cohort I and gene cohort II to form a series of candidate gene combinations.

[0250] 6) Logistic regression statistical analysis was used to evaluate the ability of each candidate gene combination to discriminate small nodules of lung cancer. The receiver operator characteristic curve (ROC curve) and area under the curve (AUC) of each candidate gene combination were calculated to screen out a series of gene combinations with good discrimination ability for small nodules of lung cancer.

[0251] 7) The screened gene combinations were verified by real-time fluorescence quantitative PCR, and the genes with consistent expression changes in quantitative PCR and gene expression profile chip detection were retained as peripheral blood characteristic genes of micronodular lung cancer. The 14 lung cancer characteristic genes CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A were mainly screened (their gene sequences are shown in SEQ ID NO.1 to SEQ ID NO.14). The significance of the 14 lung cancer characteristic genes is shown in Figure 1. Figure 1 .

[0252] 8) Logistic regression statistical analysis was used to evaluate the diagnostic effect of any combination of 7 genes from the above 14 genes on micronodular lung cancer, the ROC AUC of the gene combination was calculated, and the micronodular lung cancer discrimination model shown below was established:

[0253]

[0254] Among them, σ(z) or P is the risk value of developing micronodular lung cancer (malignant pulmonary nodules); b 0 To b 7 are the corresponding logistic regression model parameters; ΔCt 1 to ΔCt 7 are the differences in the Ct values ​​of the quantitative PCR cycles of the seven micronodule lung cancer gene markers and the internal reference gene; z is the log likelihood ratio of logistic regression.

[0255] Example 2: Detection of micronodular lung cancer using the screened lung cancer characteristic gene markers

[0256] This example uses a 7-gene combination: HNRNPD, TNFSF10, SLC38A1, MCEMP1, ZFN266, TRAF5, MORF for diagnosis.

[0257] Method steps:

[0258] 1) Collection of peripheral blood samples to be tested: Use BD PAXgene RNA blood collection tubes from QIAGEN to collect peripheral blood samples from patients;

[0259] 2) Extraction and purification of total RNA from peripheral blood samples: Use QIAGEN's PAXgene BloodRNA Kit to extract and purify total RNA from peripheral blood, and use Agilent BioAnalyzer 2100 micro-electrophoresis analyzer to identify the integrity and yield of the extracted total RNA fragments. Use Nano1000 micro-ultraviolet spectrophotometer to detect the purity of RNA samples;

[0260] 3) Reverse transcription reaction: Use the High-Capacity cDNA Reverse Transcription kit from Life Technology Company, use total RNA as a template, and use Oligo (dT) as a reverse transcription primer to perform reverse transcription reaction to synthesize cDNA.

[0261] 4) Fluorescence quantitative RT-PCR detection: This embodiment exemplarily uses a 7-gene panel combination (HNRNPD, TNFSF10, SLC38A1, MCEMP1, ZFN266, TRAF5, MORF, a total of 7 gene combinations) and the relevant sequence of the internal reference gene GAPDH, designs corresponding specific primers and / or probes or uses SYBRGreen dye that can non-specifically bind to the PCR amplification fragment, uses the cDNA obtained by reverse transcription as an amplification template, performs real-time fluorescence quantitative PCR reaction, and uses the GAPDH gene as an internal reference gene (the amplification primer sequence of the internal reference gene GAPDH is shown in SEQ ID NO.57 to SEQ ID NO.58, and the probe sequence is shown in SEQ ID NO.59) to obtain the relative mRNA content of the 6 gene markers in the peripheral blood sample. The following Table 1 lists the fluorescence quantitative PCR reaction system. The following Table 2 lists the primers and probe sequences designed for lung cancer characteristic genes.

[0262] Table 1 Fluorescence quantitative PCR reaction system

[0263] Reagents concentration volume Signature gene primers 800nM 2μL Characteristic gene fluorescent probe 200nM 0.5μL Primers for internal reference gene GAPDH 800nM 2μL Internal reference gene GAPDH fluorescent probe 200nM 0.5μL 2×PCR MasterMix 12.5μL cDNA template 2.67 ng / μL 7.5μL total 25μL

[0264] Table 2 Specific primers and probe sequences of lung cancer characteristic genes

[0265]

[0266]

[0267]

[0268] 5) Diagnosis of the results of the samples to be tested: Based on the relative mRNA levels of the seven gene markers HNRNPD, TNFSF10, SLC38A1, MCEMP1, ZNF266, TRAF5, and MORF in peripheral blood samples detected by real-time fluorescence quantitative PCR, the corresponding micronodule lung cancer discrimination model is used to calculate the logistic regression risk value (P) of the sample. The test result with a risk value (P) exceeding the threshold is judged as positive, that is, lung cancer; the test result with a risk value (P) below the threshold is judged as negative, that is, not lung cancer.

[0269] 2. Results

[0270] A total of 114 cases of malignant micronodule lung cancer (Lung Cancer) and 82 cases of benign micronodules (Benign) were collected for peripheral blood samples. Fluorescence quantitative PCR was used to detect the relative expression of 7 gene markers of micronodule lung cancer and the internal reference gene GAPDH in the peripheral blood. The logistic regression log likelihood ratio X value of each sample was calculated. X value ≥ 0 was considered a positive test result, and vice versa as a negative test result. The test results were compared with the pathological test results, and it was concluded that the lung cancer characteristic gene markers of the present invention can better distinguish malignant pulmonary nodules (micronodule lung cancer) and benign micronodules, and the sensitivity and specificity of micronodule lung cancer detection are both over 75%. The specific test results are shown in Figure 2 and Table 3.

[0271] Table 3

[0272]

[0273] Based on 196 sample data, the model constructed with 7 gene markers achieved a sensitivity, specificity, and accuracy of more than 75% in the training set, with an AUC of 0.88; its sensitivity, specificity, and accuracy in the test set also achieved more than 75%, with an AUC of 0.7.

[0274] Example 3: Detection of micronodular lung cancer using multiple combinations of screened lung cancer characteristic gene markers

[0275] The same method as described in Example 2 was used to diagnose using various gene combinations, and the results are shown in Tables 4 and 5. Table 4 shows that various gene marker combinations can be constructed based on 14 gene markers. Table 5 shows the sensitivity, specificity, accuracy and AUC of these combinations in the test set and training set. Except for Groups 1 and 2, the sensitivity and specificity of each group in the test set were greater than 60%.

[0276]

[0277]

[0278]

[0279]

[0280] The above-mentioned embodiments only express the implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for ordinary technicians in this field, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be based on the attached claims.

Claims

1. An isolated nucleic acid molecule, which is a pulmonary nodule marker, wherein the nucleic acid molecule is 13-15000 bp in length, and the nucleic acid molecule has a DNA sequence selected from one, any multiple or all of the following, or a variant thereof having at least 70%, at least 80%, at least 90%, at least 95%, at least 97%, at least 99% identity, or a corresponding RNA sequence of the DNA sequence or variant: (1) a coding sequence of CEACAM4 or a fragment thereof matching SEQ ID NO: 43, or a complementary sequence thereof, (2) a coding sequence of S100A9 or a fragment thereof matching SEQ ID NO: 44, or a complementary sequence thereof, (3) a coding sequence of FCGR1A or a fragment thereof matching SEQ ID NO: 45, or a complementary sequence thereof, (4) a coding sequence of ROGDI or a fragment thereof matching SEQ ID NO: 46, or a complementary sequence thereof, (5) a coding sequence of ZNF266 or a fragment thereof matching SEQ ID NO: 47, or a complementary sequence thereof, (6) a coding sequence of TNFSF10 or a fragment thereof matching SEQ ID NO: NO:48, or their complementary sequences, (7) the coding sequence of MORF or a fragment thereof matching SEQ ID NO:49, or their complementary sequences, (8) the coding sequence of MCEMP1 or a fragment thereof matching SEQ ID NO:50, or their complementary sequences, (9) the coding sequence of TRAF5 or a fragment thereof matching SEQ ID NO:51, or their complementary sequences, (10) the coding sequence of SLC38A1 or a fragment thereof matching SEQ ID NO:52, or their complementary sequences, (11) the coding sequence of TARBP1 or a fragment thereof matching SEQ ID NO:53, or their complementary sequences, (12) the coding sequence of SMA4 or a fragment thereof matching SEQ ID NO:54, or their complementary sequences, (13) the coding sequence of HNRNPD or a fragment thereof matching SEQ ID NO:55, or their complementary sequences, and (14) the coding sequence of SMC1A or a fragment thereof matching SEQ ID NO:56, or their complementary sequences.

2. The nucleic acid molecule according to claim 1, wherein The nucleic acid molecule has a DNA sequence selected from any of the following groups, or a variant thereof having at least 70%, at least 80%, at least 90%, at least 95%, at least 97%, at least 99% identity, or their corresponding RNA sequences: a1: (3), (7), (8), (12) and (13), a2: (1), (7), (8), (10) and (11), a3: (1), (5), (7), (9) and (11), a4: (3), (6), (7), (8), (11) and (12), a5: (1), (3), (6), (8), (11) and (13), a6: (6), (9), (10), (11), (12) and (14), a7: (5), (6), (7), (8), (9), (10) and (13), a8: (4), (8), (9), (10), (11), (13) and (14), a9: (1), (3), (5), (7), (10), (12) and (13), a10: (2), (4), (7), (9), (10), (12) and (13), a11: (5), (6), (7), (8), (9), (10) and (13), a12: (3), (7), (8), (9), (11), (12), (13) and (14), a13: (1), (4), (7), (8), (9), (10), (12) and (13), a14: (6), (7), (8), (9), (10), (11), (12) and (14), a15: (1), (3), (4), (7), (9), (10), (12), (13) and (14), a16: (1), (4), (5), (8), (9), (10), (12), (13) and (14), a17: (2), (3), (8), (9), (10), (11), (12), (13) and (14), a18: (3), (4), (6), (7), (8), (10), (11), (12), (13) and (14), a19：(1)、(2)、(3)、(4)、(5)、(9)、(10)、(11)、 (13) and (14), a20: (1), (2), (4), (5), (6), (8), (9), (10), (11) and (14), a21: (1), (2), (4), (5), (6), (7), (8), (11), (12), (13) and (14), a22：(1)、(2)、(3)、(6)、(8)、(9)、(10)、(11)、 (12), (13) and (14), a23: (1), (2), (3), (4), (5), (6), (7), (8), (12), (13) and (14), a24: (3), (4), (5), (6), (7), (8), (9), (10), (11), (12), (13) and (14), a25: (1), (2), (3), (4), (6), (7), (8), (9), (10), (11), (13) and (14), a26: (1), (3), (4), (5), (6), (7), (8), (9), (10), (11), (12) and (13), a27: (1), (2), (3), (4), (5), (7), (8), (9), (10), (11), (12), (13) and (14), a28: (1), (2), (3), (4), (5), (6), (7), (8), (9), (10), (12), (13) and (14), a29: (1), (2), (3), (4), (5), (6), (7), (8), (9), (10), (11), (13) and (14), or a30: (1), (2), (3), (4), (5), (6), (7), (8), (9), (10), (11), (12), (13) and (14).

3. A reagent for detecting (1) the transcription level, translation level, or content of one or more of the nucleic acid molecules of claim 1; or (2) The content or activity of the expression product of (1), Preferably, the reagent is selected from one or more of the following methods: PCR, DNA sequencing, RNA sequencing, DNA hybridization, RNA hybridization, Southern, Northern, Western, immunoprecipitation, ELISA, restriction endonuclease analysis, high resolution melting curve method, mass spectrometry, preferably RT-qPCR, RNA sequencing or chip-based methods, More preferably, the reagent is selected from one or more of the following: buffer, reverse transcriptase, polymerase, dNTP, primer, probe, restriction endonuclease, fluorescent dye, fluorescence quencher, fluorescent reporter, exonuclease, alkaline phosphatase, internal standard, control.

4. A kit comprising the reagent according to claim 2 and optionally the nucleic acid molecule according to claim 1, preferably, the kit further comprises primers and / or probes for detecting a reference sequence.

5. Use of one or more or all of the following genes or their DNA or RNA sequences in animal genomes in the preparation of a reagent or kit for distinguishing benign or malignant pulmonary nodules: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A, Preferably, the reagent detects (1) the transcription level, translation level, content or methylation of the gene in the sample, or (2) the content or activity of the expression product of (1); more preferably, the reagent is a primer or a probe, Preferably, the pulmonary nodules are pulmonary micronodules. Preferably, the gene is selected from any one of the following groups: a1: FCGR1A, MCEMP1, MORF, SMA4 and HNRNPD, a2: CEACAM4, MCEMP1, MORF, SLC38A1 and TARBP1, a3: CEACAM4, TRAF5, MORF, ZNF266 and TARBP1, a4: FCGR1A, MCEMP1, MORF, TARBP1, TNFSF10 and SMA4, a5: CEACAM4, FCGR1A, MCEMP1, TARBP1, TNFSF10 and HNRNPD, a6: SLC38A1, TARBP1, TNFSF10, TRAF5, SMA4 and SMC1A, a7: HNRNPD, TNFSF10, SLC38A1, MCEMP1, ZNF266, TRAF5 and MORF, a8: MCEMP1, ROGDI, SLC38A1, TARBP1, TRAF5, HNRNPD and SMC1A, a9: CEACAM4, FCGR1A, MORF, SLC38A1, ZNF266, SMA4 and HNRNPD, a10: MORF, ROGDI, S100A9, SLC38A1, TRAF5, SMA4 and HNRNPD, a14: TRAF5, MCEMP1, MORF, SLC38A1, TNFSF10, ZNF266 and HNRNPD, a12: FCGR1A, MCEMP1, MORF, TARBP1, TRAF5, SMA4, HNRNPD and SMC1A, a13: CEACAM4, MCEMP1, MORF, ROGDI, SLC38A1, TRAF5, SMA4, and HNRNPD, a14: MCEMP1, MORF, SLC38A1, TARBP1, TNFSF10, TRAF5, SMA4 and SMC1A, a15: CEACAM4, FCGR1A, MORF, ROGDI, SLC38A1, TRAF5, SMA4, HNRNPD and SMC1A, a16: CEACAM4, MCEMP1, ROGDI, SLC38A1, TRAF5, ZNF266, SMA4, HNRNPD, and SMC1A, a17: FCGR1A, A, C, EMP1, S100A9, SLC38A1, TARBP1, TRAF5, SMA4, HNRNPD, and SMC1A, a18: FCGR1A, MCEMP1, MORF, ROGDI, SLC38A1, TARBP1, TNFSF10, SMA4, HNRNPD and SMC1A, a19: CEACAM4, FCGR1A, ROGDI, S100A9, SLC38A1, TARBP1, TRAF5, ZNF266, HNRNPD, and SMC1A, a20: CEACAM4, MCEMP1, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, and SMC1A, a21: CEACAM4, MCEMP1, MORF, ROGDI, S100A9, TARBP1, TNFSF10, ZNF266, SMA4, HNRNPD and SMC1A, a22: CEACAM4, FCGR1A, MCEMP1, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, SMA4, HNRNPD, and SMC1A, a23: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, TNFSF10, ZNF266, SMA4, HNRNPD and SMC1A, a24: FCGR1A, MCEMP1, MORF, ROGDI, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, SMA4, HNRNPD and SMC1A, a25: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, HNRNPD and SMC1A, a26: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, SMA4 and HNRNPD, a27: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TRAF5, ZNF266, SMA4, HNRNPD and SMC1A, a28: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TNFSF10, TRAF5, ZNF266, SMA4, HNRNPD and SMC1A, a29: CEACAM4, FCGR1A, CEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, HNRNPD, and SMC1A or a30: CEACAM4, FCGR1A, MCEMP1, MORF, ROGDI, S100A9, SLC38A1, TARBP1, TNFSF10, TRAF5, ZNF266, SMA4, HNRNPD and SMC1A.

6. Use of the reagent according to claim 3 and optionally the nucleic acid molecule according to claim 1 or 2 in the preparation of a kit for distinguishing benign and malignant pulmonary nodules; preferably, The pulmonary nodules are pulmonary micronodules; and / or The kit is a real-time quantitative PCR kit, an RNA sequencing kit or a kit containing a gene chip.

7. A device, characterized in that: The device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the program: (1) obtaining expression detection results of one or more or all of the following genes in the sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A, (2) obtaining a score by constructing a model using the expression detection results, (3) Differentiate between benign and malignant pulmonary nodules based on the scores. Preferably, the pulmonary nodules are pulmonary micronodules.

8. A system for distinguishing benign and malignant pulmonary nodules, characterized in that: include: A collection device is used to obtain expression detection results of one or more or all of the following genes in a sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A, A data processing device, used to obtain a score by constructing a model using the detection result, The judgment device is used to distinguish benign and malignant pulmonary nodules according to the score. Preferably, the pulmonary nodules are pulmonary micronodules.

9. A method for screening genes for distinguishing benign and malignant pulmonary nodules, comprising: (1) Obtaining gene expression data of malignant pulmonary nodule samples and benign pulmonary nodule samples, (2) Select genes expressed in all samples for T-test analysis. (3) Compare the gene expression differences between malignant pulmonary nodule samples and benign pulmonary nodule samples, and select genes with a statistical P value less than 0.05 and a gene expression change of more than 1.1 times as candidate genes. Optional (4) Sort by correlation between gene expression data and benign or malignant characteristics of samples, select genes with high correlation with malignant pulmonary nodules as gene group I; select genes with high correlation with gene group I from the remaining genes as gene group II; then pair the genes in gene group I and gene group II in pairs to form a candidate gene combination, (5) Using logistic regression statistical analysis to screen candidate genes, Preferably, the pulmonary nodules are pulmonary micronodules, and the genes for distinguishing benign and malignant pulmonary nodules are selected from one or more or all of the following: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A.

10. A method for constructing a model for distinguishing benign and malignant pulmonary nodules, comprising: (1) obtaining expression detection results of one or more or all of the following genes in the sample: CEACAM4, S100A9, FCGR1A, ROGDI, ZNF266, TNFSF10, MORF, MCEMP1, TRAF5, SLC38A1, TARBP1, SMA4, HNRNPD and SMC1A, (2) Calculate the ROC curve and area under the curve for each candidate gene combination, and establish a model using logistic regression statistical analysis. Preferably, the pulmonary nodules are pulmonary micronodules.

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