Methylated biomarker for assisting in identifying MET amplification condition and application of methylated biomarker

Abstract

The invention relates to a methylation biomarker combination for auxiliary identification of MET amplification condition and application thereof, the marker combination comprises multiple combinations including chr14: 102290371, chr8: 17580951, chr4: 901982, chr19: 40928821 and chr7: 104752829, it is found that the methylation biomarker combination can effectively assist in detection of MET amplification condition, the problem of low methylation signal of single DNA can be overcome, the sensitivity and specificity of detection are improved, and the application of the methylation biomarker combination in auxiliary identification of MET amplification condition is realized. Therefore, a more effective auxiliary detection service is provided for formulating a reasonable diagnosis and treatment scheme for the lung cancer patient. Moreover, based on detection of methylation states of the DNA methylation markers in samples, MET amplification conditions and methylation changes in development can be more comprehensively analyzed, and the method can be applied to human malignant tumor diagnosis, curative effect evaluation, relapse monitoring, scientific research and drug resistance research related to MET amplification for auxiliary technical services.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and specifically to methylation biomarkers for assisting in identifying MET amplification and their applications. Background Art

[0002] The mesenchymal-epithelial transition factor gene (MET) encodes the tyrosine kinase receptor for hepatocyte growth factor (HGF). It is a transmembrane tyrosine kinase heterodimeric protein that participates in complex signal transduction processes [Bottaro, D.P., Rubin, J.S., Faletto, D.L., Chan, A.M., Kmiecik, T.E., Vande Woude, G.F., & Aaronson, S.A. (1991). Identification of the hepatocyte growth factor receptor as the c-met proto-oncogene product. Science (New York, N.Y.), 251(4995), 802 - 804. https: / / doi.org / 10.1126 / science.1846706]. The MET / HGF pathway is activated during embryogenesis or organogenesis, enabling cells to acquire the ability to move from the original niche to the surrounding microenvironment for "invasive growth". In adults, it usually remains quiescent, but different stress conditions, such as angiogenesis or hypoxia, can lead to its reactivation. In cancer, the abnormal activation of the MET / HGF pathway is very common in several human malignancies, including non-small cell lung cancer, glioma, gastroesophageal cancer, ovarian cancer, breast cancer, renal cancer, and liver cancer, and is usually driven alone or in combination by three ways: MET amplification, mutation, and fusion. Therefore, interfering with the MET / HGF pathway can be a potential anti-tumor strategy [Landi, L., Minuti, G., D'Incecco, A., Salvini, J., & Cappuzzo, F. (2013). MET overexpression and gene amplification in NSCLC: a clinical perspective. Lung Cancer (Auckland, N.Z.), 4, 15 - 25. https: / / doi.org / 10.2147 / LCTT.S35168].

[0003] MET amplification also leads to the drug resistance of some cancer targeted drugs. First-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), such as gefitinib and erlotinib, are competitive reversible ATP inhibitors that block the activation of EGFR downstream signals by preventing the autophosphorylation of the TK domain. MET amplification provides a bypass pathway in the presence of EGFR inhibitors by activating the ErbB3 phosphorylation pathway independent of EGFR and the downstream PI3K / AKT pathway, thus causing EGFR-TKI resistance. [Wang, Q., Yang, S., Wang, K., & Sun, S. Y. (2019). MET inhibitors for targeted therapy of EGFR TKI-resistant lung cancer. Journal of hematology & oncology, 12(1), 63. https: / / doi.org / 10.1186 / s13045-019-0759-9].

[0004] In clinical applications, there are various ways to determine MET gene amplification by detecting changes in MET copy number. These techniques include, but are not limited to, fluorescence in situ hybridization (FISH), quantitative real-time polymerase chain reaction (qRT-PCR), and next-generation sequencing (NGS). However, different technical routes and even different platforms of the same technical route have different interpretations of the cut-off point for MET amplification, so there is no clear standard definition of MET amplification. [Guo, R., Luo, J., Chang, J., Rekhtman, N., Arcila, M., & Drilon, A. (2020). MET-dependent solid tumours - molecular diagnosis and targeted therapy. Nature reviews. Clinical oncology, 17(9), 569-587. https: / / doi.org / 10.1038 / s41571-020-0377-z]. The above results lead to the situation that MET amplification cannot be accurately quantified in a simple way and more or less depends on technical prior knowledge and experience in operation, so when it is promoted to personnel and teams with different technical levels, it faces the problem that the consistency of results is difficult to guarantee.

[0005] In the human genome, DNA methylation is an important epigenetic modification. Studies have found that it plays an important role in gene expression, genomic stability, and the occurrence and development of tumors, and participates in transcriptional regulation together with histone modification and chromatin configuration remodeling. Among them, abnormal DNA methylation is considered a hallmark of cancer development, causing chromatin instability by inhibiting or inactivating gene transcription [Cheng Y, He C, Wang M, et al. Targeting epigenetic regulators for cancer therapy: mechanisms and advances in clinical trials. Signal Transduct Target Ther. 2019;4:62. Published 2019 Dec 17. doi:10.1038 / s41392-019-0095-0].

[0006] If only looking at the methylation signal changes at a single site, single-site methylation detection has no advantage compared with various conventional methods for detecting MET gene amplification. However, there are numerous methylation sites, and CpG sites with relatively close genomic distances tend to be methylated or demethylated simultaneously, meaning that a cluster of CpG sites can be analyzed as a whole. Through various combinations of horizontal patterns and vertical abundances, methylation subunits with stronger signals can be obtained. Based on this, multiple CpG site clusters can be combined to form a methylation haploid pattern specific to MET gene amplification. This combined method that further improves the accuracy of methylation fingerprints has been verified in the comparative detection of benign and malignant tumor samples [Guo, S., Diep, D., Plongthongkum, N. et al. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet 49, 635–642 (2017).], so this idea can be extended to the method of using methylation signals to detect MET gene amplification.

[0007] By analyzing the high-throughput methylation sequencing data of tumor tissue samples with different MET gene amplification conditions, after constructing a reasonable biostatistical model, it is possible to effectively predict the MET gene amplification situation in unknown samples. On the one hand, it effectively reduces the experimental and analysis costs and improves the detection sensitivity and specificity; on the other hand, since this method is a model-based quantitative method, it is more accurate and rigorous than traditional methods that rely on technical prior knowledge and operational experience. When promoting it to personnel and teams with different technical levels, the difficulty is reduced. It can be used as a good supplement to traditional detection methods and will play a good role in promoting basic clinical research. Summary of the Invention

[0008] Based on this, the object of the present invention is to propose a methylation biomarker combination for assisting in identifying MET amplification and its application.

[0009] The technical solutions for achieving the above object are as follows.

[0010] In the first aspect of the present invention, a methylation biomarker combination for assisting in identifying MET amplification is provided. The methylation biomarker combination includes the following markers: chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, and the methylation sites are aligned to the corresponding positions in hg19.

[0011] In some embodiments thereof, the methylation biomarker combination includes the following markers: chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, chr17:73360253, chr6:16483998, chr12:120539138, chr5:150003038, chr7:38316654, chr16:89189384, chr7:110506411, chr3:58103186, chr1:160681405, chr6:42071220, chr11:65211710, chr2:177054306, chr2:191875807, and the methylation sites are aligned to the corresponding positions in hg19.

[0012] In some of these embodiments, the methylation biomarker combination includes the following biomarkers: chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, chr17:73360253, chr6:16483998, chr12:120539138, chr5:150003038, chr7:38316654, chr16:89189384, chr7:110506411, chr3:58103186, chr1:160681405, chr6:42071220, chr11:65211710, chr2:177054306, chr2:191875807, chr8:124515503, chr5:145453108, chr15:42750463, chr11:47510561, chr14:68861096, chr7:95050561, chr12:132259591, chr1:155938911, chr8:30284255, chr10:86963122, chr3:49817941, chr16:4745215, chr17:30810854, chr19:13196617, chr4:3180930, chr2:43414162, chr4:152096590, chr22:39918625, chr19:40928737, chr6:138859577, chr12:51319063, and the methylation sites are aligned to the corresponding positions in hg19.

[0013] In some of these embodiments, the methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516, and the methylation sites are aligned to the corresponding positions in hg19.,

[0014] In some of these embodiments, the methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325, The methylation sites are aligned to the corresponding positions in hg19.,

[0015] In some of these embodiments, the methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633. The methylation sites are aligned to the corresponding positions in hg19.,

[0016] In some of these embodiments, the methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633,chr11:92511989, and the methylation sites are aligned to the corresponding positions in hg19.,

[0017] In some of these embodiments, the methylation biomarker combination, in addition to including the biomarkers of the above seven groups, further includes at least one selected from the following: chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633,chr11:92511989,chr14:64926333,chr17:7128558,chr1:151008412,chr20:23344170,chr17:79006167,chr17:2324862,chr11:92572718,chr3:45641120,chr7:30515286,chr12:102216984,chr10:102028800,chr13:76387161,chr1:35449720,chr2:66159331,chr1:27916108,chr1:101705238,chr1:183601018,chr6:41757331,chr17:55186659,chr17:38520654,chr5:124013537,chr1:180127817,chr11:71710885,chr10:43891460,chr21:40194848,chr14:21359737,chr17:25905125,chr8:48595620,chr7:150782539,chr6:36165617,chr22:33257641,chr1:240370408,chr8:41895101,chr15:75941357,chr18:46482440,chr8:17625476,chr2:62533140,chr4:109994040,chr6:38608738,chr1:205630723,chr19:3178760,chr2:28117087,chr5:176920260,chr3:134094538,chr1:33803806,chr11:71710643,chr12:88784938. The methylation sites are aligned to the corresponding positions in hg19.,

[0018] In some of these embodiments, the marker combination is based on the methylation biomarker combination for assisting in identifying MET amplification as described in any one of the above, and further selects any at least one and a total of no more than 100 markers from the first 100 markers in Table 2 to form a combination.

[0019] In some of these embodiments, the methylation biomarker combination is 144 markers in Table 2.

[0020] In a second aspect of the present invention, there is provided the use of the above methylation biomarker combination or its methylation detection reagent in the preparation of a kit for predicting, detecting, or otherwise evaluating the MET amplification situation.

[0021] In a third aspect of the present invention, there is provided a kit for assisting in detecting the MET amplification situation.

[0022] A kit for assisting in detecting the MET amplification situation, which includes a reagent for detecting the methylation difference degree of the above methylation biomarker combination.

[0023] In some of these embodiments, the above kit is prepared by polymerase chain reaction technology, in situ hybridization technology, enzymatic mutation detection technology, chemical cleavage mismatch technology, mass spectrometry analysis technology, gene chip technology, gene sequencing technology, or a combination thereof.

[0024] In some of these embodiments, in the detection technologies used in the above kit, the polymerase chain reaction technology includes but is not limited to RT-PCR, immunological PCR, nested PCR, fluorescence PCR, in situ PCR, membrane-bound PCR, anchored PCR, solid-phase PCR, in situ PCR, asymmetric PCR, long-distance PCR, parachute PCR, gradient PCR, etc.; high-throughput detection technologies include but are not limited to reduced representation bisulfite sequencing, whole-genome bisulfite sequencing, DNA enrichment sequencing, pyrosequencing, bisulfite conversion sequencing; detection technologies based on mass spectrometry such as GC-MS, LC-MS, MALDI-TOFMS, FT-MS, ICP-MS, SIMS; chip detection platforms such as 450K and 850K methylation detection technologies.

[0025] In some of these embodiments, the detection methods used in the above kit include but are not limited to at least one of fluorescence quantitative PCR, methylation-specific PCR, digital PCR, DNA methylation chip, targeted DNA methylation sequencing, whole-genome methylation sequencing, and DNA methylation mass spectrometry.

[0026] One of the purposes of the present invention is also to propose the use of the above kit in the preparation of reagents for predicting, detecting, classifying, treating and monitoring, prognosis, or drug resistance evaluation of malignancies related to MET amplification.

[0027] One object of the present invention is also to provide a method for auxiliary detection of MET amplification for non-diagnostic purposes.

[0028] The technical solution for achieving the above object is as follows:

[0029] A method for auxiliary detection of MET amplification for non-diagnostic purposes, comprising the following steps:

[0030] Extract genomic DNA of a biological sample to be tested;

[0031] Perform bisulfite conversion on the DNA;

[0032] Detect the methylation difference degree of the above methylation biomarker or its combination.

[0033] In some embodiments, the above method includes but is not limited to the following techniques: methylation-specific PCR, bisulfite PCR sequencing, real-time quantitative methylation-specific PCR, etc.; high-throughput detection techniques include reduced representation bisulfite sequencing, whole genome bisulfite sequencing, DNA enrichment sequencing, pyrosequencing, bisulfite conversion sequencing, etc.; detection techniques based on detection platforms such as mass spectrometry; detection techniques based on chip detection platforms, such as 450K and 850K methylation detection techniques.

[0034] In some embodiments, the above biological sample is tissue, peripheral blood (plasma), saliva, pleural effusion, ascites, amniotic fluid, bone marrow or cultured animal cells. Further preferably, the biological sample is a tissue sample.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The inventors of the present invention found that by comprehensively considering the differential analysis results of the self-owned data training set (27 sample cases) and TCGA data, and combining methods such as machine learning modeling, a 144-marker combination was screened out. Also, a 5-marker combination (including regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, etc.), an 18-marker combination (including regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, chr17:73360253, chr6:16483998, chr12:120539138, chr5:150003038, chr7:38316654, chr16:89189384, chr7:110506411, chr3:58103186, chr1:160681405, chr6:42071220, chr11:65211710, chr2:177054306, chr2:191875807, etc.), a 39-marker combination, an 89-marker combination, a 93-marker combination, a 96-marker combination, and a 97-marker combination, which were obtained by appropriately reducing the number of features based on the 144 alternative features without significantly reducing the performance. By using methods such as differential analysis and random forest to establish a prediction model in a given tissue sample, it was found that it can effectively assist in detecting MET amplification, and at the same time overcome the problem of low single DNA methylation signal, and improve the sensitivity and specificity of detection, thus providing more effective auxiliary detection services for formulating reasonable diagnosis and treatment plans for lung cancer patients. Moreover, by detecting the methylation status of these DNA methylation markers in the sample, it is also possible to more comprehensively analyze MET amplification and methylation changes during development, and apply it to human malignant tumors, including non-small cell lung cancer, glioma, gastroesophageal cancer, ovarian cancer, breast cancer, renal cancer, liver cancer for auxiliary diagnosis, efficacy evaluation and recurrence monitoring, scientific research, and drug resistance analysis and drug research of some cancer targeted drugs, providing more accurate and sensitive technical services for clinical practice. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1For the 144 features screened by the differential analysis considering both the self-owned data training set (27 samples) and TCGA data in the embodiment, according to the feature importance given when building a random forest model based on the self-owned data training set (27 samples), when selecting the top 1 - top 144 features and building a random forest model again on the self-owned data training set (27 samples), the changing trend of the AUC of the self-owned data test set (15 samples).

[0038] Figure 2 In the embodiment, a heatmap drawn using the 144-marker methylation biomarkers screened on the two batches of combined self-owned data (corresponding to the training set (27 samples) and the test set (15 samples) respectively). Each column represents a sample, and each row represents a methylation biomarker.

[0039] Figure 3 In the embodiment, a heatmap drawn using the 18-marker methylation biomarkers screened on the two batches of combined self-owned data (corresponding to the training set (27 samples) and the test set (15 samples) respectively). Each column represents a sample, and each row represents a methylation biomarker.

[0040] Figure 4 In the embodiment, a heatmap drawn using the 5-marker methylation biomarkers screened on the two batches of combined self-owned data (corresponding to the training set (27 samples) and the test set (15 samples) respectively). Each column represents a sample, and each row represents a methylation biomarker.

[0041] Figure 5 In the embodiment, after building a random forest model using the 144-marker methylation biomarkers in the self-owned data training set (27 samples), the ROC graph corresponding to the prediction results on the self-owned data test set (15 samples).

[0042] Figure 6 In the embodiment, after building a random forest model using the 18-marker methylation biomarkers in the self-owned data training set (27 samples), the ROC graph corresponding to the prediction results on the self-owned data test set (15 samples).

[0043] Figure 7 In the embodiment, after building a random forest model using the 5-marker methylation biomarkers in the self-owned data training set (27 samples), the ROC graph corresponding to the prediction results on the self-owned data test set (15 samples).

[0044] Figure 8ROC curve corresponding to the prediction results on the in-house data test set (15 samples) after establishing a random forest model using the 39-marker methylation biomarkers selected for the in-house data training set (27 samples) in the examples.

[0045] Figure 9 ROC curve corresponding to the prediction results on the in-house data test set (15 samples) after establishing a random forest model using the 89-marker methylation biomarkers selected for the in-house data training set (27 samples) in the examples.

[0046] Figure 10 ROC curve corresponding to the prediction results on the in-house data test set (15 samples) after establishing a random forest model using the 93-marker methylation biomarkers selected for the in-house data training set (27 samples) in the examples.

[0047] Figure 11 ROC curve corresponding to the prediction results on the in-house data test set (15 samples) after establishing a random forest model using the 96-marker methylation biomarkers selected for the in-house data training set (27 samples) in the examples.

[0048] Figure 12 ROC curve corresponding to the prediction results on the in-house data test set (15 samples) after establishing a random forest model using the 97-marker methylation biomarkers selected for the in-house data training set (27 samples) in the examples. Detailed implementation

[0049] In the following examples, the experimental methods without specific conditions mentioned are generally carried out under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or according to the conditions recommended by the manufacturer. All common chemical reagents used in the examples are commercially available products.

[0050] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used in the present invention includes any and all combinations of one or more of the related listed items.

[0051] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the understanding of the disclosure of the present invention more thorough and comprehensive.

[0052] The present invention will be further described in detail below in conjunction with specific embodiments.

[0053] Embodiment 1

[0054] This embodiment discloses a 144-marker combination obtained by comprehensively considering the differential analysis results of the self-owned data training set (27 sample cases) and TCGA data, and combining methods such as machine learning modeling. The combination includes regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, chr17:73360253, chr6:16483998, chr12:120539138, chr5:150003038, chr7:38316654, chr16:89189384, chr7:110506411, chr3:58103186, chr1:160681405, chr6:42071220, chr11:65211710, chr2:177054306, chr2:191875807, chr8:124515503, chr5:145453108, chr15:42750463, chr11:47510561, chr14:68861096, chr7:95050561, chr12:132259591, chr1:155938911, chr8:30284255, chr10:86963122, chr3:49817941, chr16:4745215, chr17:30810854, chr19:13196617, chr4:3180930, chr2:43414162, chr4:152096590, chr22:39918625, chr19:40928737, chr6:138859577, chr12:51319063, chr8:144650595, chr9:1056641, chr1:2321365, chr2:192208956, chr5:33982209, chr1:223921358, chr7:38309985, chr16:14326544, chr1:206671182, chr4:85878806, chr11:64631357, chr6:11429245, chr17:41900588, chr7:41747323, chr17:57449016, chr9:134609066, chr3:38010392, chr1:112263095, chr1:168212211, chr19:46214108, chr9:117012651, chr11:125985045, chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633,chr11:92511989,chr14:64926333,chr17:7128558,chr1:151008412,chr20:23344170,chr17:79006167,chr17:2324862,chr11:92572718,chr3:45641120,chr7:30515286,chr12:102216984,chr10:102028800,chr13:76387161,chr1:35449720,chr2:66159331,chr1:27916108,chr1:101705238,chr1:183601018,chr6:41757331,chr17:55186659,chr17:38520654,chr5:124013537,chr1:180127817,chr11:71710885,chr10:43891460,chr21:40194848,chr14:21359737,chr17:25905125,chr8:48595620,chr7:150782539,chr6:36165617,chr22:33257641,chr1:240370408,chr8:41895101,chr15:75941357,chr18:46482440,chr8:17625476,chr2:62533140,chr4:109994040,chr6:38608738,chr1:205630723,chr19:3178760,chr2:28117087,chr5:176920260,chr3:134094538,chr1:33803806,chr11:71710643,chr12:88784938, etc.), as well as 5-marker combinations with a properly reduced number of features based on these 144 alternative features and no significant performance degradation (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829, etc.), 18-marker combinations (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807, etc.), 39-marker combinations (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063, etc.), 89-marker combination (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516 etc.), 93-marker combination (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325, etc.), 96-marker combination (including regions chr14:102290371, chr8:17580951, chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633 etc.) and the 97 - marker combination (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956, chr5:33982209, chr1:223921358, chr7:38309985, chr16:14326544, chr1:206671182, chr4:85878806, chr11:64631357, chr6:11429245, chr17:41900588, chr7:41747323, chr17:57449016, chr9:134609066, chr3:38010392, chr1:112263095, chr1:168212211, chr19:46214108, chr9:117012651, chr11:125985045, chr19:45261005, chr7:116377467, chr2:106792427, chr17:61520113, chr2:11518494, chr11:73692156, chr19:38755753, chr17:56019314, chr3:141118615, chr17:43367610, chr21:46948366, chr16:85010598, chr11:118781763, chr3:64136334, chr9:38061779, chr10:80834947, chr16:3115287, chr1:21947438, chr1:9210824, chr16:69962827, chr6:158097037, chr17:15336969, chr14:105784091, chr14:56800154, chr17:80851007, chr14:102647731, chr3:15085146, chr7:70599516, chr10:103574627, chr8:30248467, chr1:10743217, chr17:60994325, chr17:16976476, chr1:12513497, chr17:74019633, chr11:92511989, etc.) can all be used in the detection method for assisting in detecting the MET amplification situation, and specifically include the following steps:

[0055] I. Sample Information

[0056] 1. The first cohort of self-owned lung cancer tissue samples consisted of 27 cases, which were used as the training set for differential analysis and model construction to screen for specific methylation markers that could identify MET amplification status. It included 12 cases of MET amplification samples and 15 negative samples; 17 cases were less than or equal to 60 years old and 10 cases were older than 60 years old; 17 cases were male and 10 cases were female; according to the stage, there was 1 case in stage I, 3 cases in stage II, 4 cases in stage III, 13 cases in stage IV, and 6 cases without stage information; according to the sampling source, there were 18 cases from the primary focus, 8 cases from metastatic foci, and 1 case from other sources.

[0057] 2. The second cohort of self-owned lung cancer tissue samples consisted of 15 cases, which were used as the test set for model validation to identify specific methylation markers that could identify MET amplification status. It included 7 cases of MET amplification samples and 8 negative samples; 12 cases were less than or equal to 60 years old and 3 cases were older than 60 years old; 8 cases were male and 7 cases were female; according to the stage, there were 2 cases in stage I, 0 cases in stage II, 3 cases in stage III, 8 cases in stage IV, and 2 cases without stage information; according to the sampling source, there were 11 cases from the primary focus, 4 cases from metastatic foci, and 0 cases from other sources.

[0058] 3. The tissue samples of the above patients were subjected to high-throughput targeted methylation sequencing, including but not limited to separating tissue genomic DNA, bisulfite conversion, and steps such as AnchorIRIS TM library construction and targeted capture methylation sequencing using the Twist Human Methylome panel.

[0059] 4. The data for the third batch of auxiliary verification was sourced from the third-party cancer public database TCGA (The Cancer Genome Atlas Program, https: / / portal.gdc.cancer.gov / ) and served as an auxiliary dataset for the training set (27 samples). The selected samples were divided into 261 cases of lung adenocarcinoma (LUAD) and 193 cases of lung squamous cell carcinoma (LUSC). Among the lung adenocarcinoma samples, there were 77 amplification samples and 184 negative samples; among the lung squamous cell carcinoma samples, there were 48 amplification samples and 145 negative samples.

[0060] II. Library construction process and method

[0061] 1. Sample DNA extraction and methylation library construction

[0062] 1.1. Extraction of sample DNA.

[0063] For lung cancer tissue samples, the DNA extraction steps were carried out according to the operation instructions of the DNA FFPE Tissue KIT of QIAGEN.

[0064] 1.2, Transformation

[0065] The extracted sample DNA (100 ng) was subjected to bisulfite conversion, which deaminated and converted unmethylated cytosine in the DNA into uracil, while methylated cytosine remained unchanged, resulting in bisulfite-converted DNA. The specific operation of the conversion was carried out according to the instructions of the Zymo Research EZ DNA Methylation-Lightning Kit.

[0066] 1.3, End repair

[0067] Add the above 17 μl of the converted sample to the following reagents for reaction:

[0068]

[0069] Place it in a PCR instrument and carry out the reaction according to the following program:

[0070] 37℃ 30 min 95℃ 5 min Thermal cover 105℃

[0071] When the second step (95 °C) of the PCR reaction reached 5 min, immediately take out the sample from the PCR instrument, directly insert it into ice, and place it for more than 2 min before proceeding to the next step.

[0072] 1.4 Ligation I

[0073] Prepare the following reaction solution:

[0074] Component Single dosage (μl) Product of previous reaction 20 <![CDATA[H2O]]> 4 MLB1 buffer 8 MLR1 reagent 2 MLR5 reagent 2 MLE1 enzyme 2 MLE5 enzyme 2 Reaction mixture volume 40

[0075] Place it in a PCR instrument and carry out the reaction according to the following program:

[0076] 37℃ 30 min 95℃ 5 min 10℃ Hold Thermal cover 105℃

[0077] 1.5 Amplification I

[0078] Prepare the following reaction solution

[0079] Component Single dosage (μl) Product of previous reaction 40 <![CDATA[H2O]]> 35 MAB2 buffer 20 MAR1 reagent 2 MAR2 reagent 2 MAE3 enzyme 1 Reaction mixture volume 40

[0080] Place it in a PCR instrument and carry out the reaction according to the following program:

[0081]

[0082] 1.6 Purification I:

[0083] Add 166 μl of 1:6 diluted Agencourt AMPure Beads (previously equilibrated at room temperature for half an hour) to purify the product after the amplification I reaction, and elute it with 21 μl of EB. The specific purification steps are as follows:

[0084] Take the product of the previous step and centrifuge it. Add 166 μl of Agencourt AMPure Beads diluted 1:6 to each sample, and pipette to mix well. Incubate at room temperature for 5 min. Centrifuge and place on a magnetic stand for 5 min. Aspirate the supernatant. Add 500 μl of 80% EtOH, let stand for 30 s, aspirate the ethanol, repeat once, then centrifuge, place the PCR tube on the magnetic stand, aspirate the remaining ethanol, open the lid and dry the magnetic beads for 2 - 3 min, taking care not to over-dry. Add 21 μl of EB for elution, pipette to mix well, and let stand at room temperature for 3 min. Centrifuge, place the PCR tube on the magnetic stand, and let stand for 3 min. Aspirate 20 μl of the supernatant into a new PCR tube.

[0085] 1.7 Denaturation of the purified product

[0086] Place the product purified in the previous step in a PCR instrument and perform the reaction according to the following program:

[0087] Temperature Time Number of cycles 95℃ 5 min 1

[0088] When the PCR reaction (at 95 °C) reaches 5 min, immediately remove the sample from the PCR instrument, directly insert it into ice, and let it stand for more than 2 min before proceeding to the next step.

[0089] 1.7 Ligation II

[0090] Prepare the following reaction solution:

[0091] Component Volume (μl) Volume of previous reaction 20 <![CDATA[H2O]]> 4 MSB1 buffer 8 MSR1 reagent 2 MSR5 reagent 2 MSE1 enzyme 2 MSE5 enzyme 2 Total volume 40

[0092] Place it in a PCR instrument and perform the reaction according to the following program

[0093] Temperature Time Number of cycles 37℃ 30 min 1 95℃ 5 min 1 10℃ Hold 1

[0094] 1.8 Indexing PCR (amplified product library construction):

[0095] Prepare the following reaction solution:

[0096]

[0097]

[0098] Place it in a PCR instrument and perform the reaction according to the following program

[0099]

[0100] 1.9 Purification II

[0101] Add 71 μl of Agencourt AM Pure Beads (equilibrate at room temperature for half an hour in advance) to purify the product after the Indexing PCR reaction, and elute with 41 μl of EB. The specific purification steps are as follows:

[0102] Take the product of the previous step and centrifuge. Add 71 μl of undiluted Agencourt AM Pure Beads to each sample, and pipette to mix well. Incubate at room temperature for 5 min. Centrifuge and place on the magnetic stand for 5 min. Aspirate the supernatant. Add 200 μl of 80% EtOH, let stand for 30 s, aspirate the ethanol, repeat the step once, then centrifuge, place the PCR tube on the magnetic stand, and aspirate the remaining ethanol. Open the lid and dry the magnetic beads for 2 - 3 min, taking care not to over-dry. Add 55 μl of EB for elution, pipette to mix well, and let stand at room temperature for 3 min. Centrifuge and place the PCR tube on the magnetic stand for 3 min. Aspirate 54 μl of the supernatant into a new PCR tube. Qubit quantification: Take 1 μl and use the Qubit dsDNA HS Assay Kit to quantify the library.

[0103] 2. Obtain the final library for sequencing of the specific region by performing oligonucleotide probe capture enrichment on the library - constructed samples. The hybridization capture kit is xGen Lockdown Reagents from IDT, and the operation is carried out according to the instructions.

[0104] 3. Sequence the samples after hybridization capture using the sequencer from Illumina to obtain the sequencing results.

[0105] 4. Analysis of the data after sequencing:

[0106] Perform routine bioinformatics analysis and processing on the raw data after sequencing from the sequencer. First, filter out the low - quality reads (low QC, short length, too many Ns, etc.) through fastp, then remove the adapters, common sequences, and PolyA / T at both ends of the reads to obtain the ideal insert fragment sequences (target intervals). After aligning these reads to the corresponding positions in hg19 using bismark, deduplicate the reads according to UMI to obtain the real reads data (bam file) captured by the probes for each sample. Statistically analyze the bam file to obtain the methylation data for subsequent re - analysis of the data.

[0107] 5. The original sequencing data was subjected to relevant cleaning and processing analysis [Liang, W., et al., Non-invasive diagnosis of early-stage lung cancer using high-throughput targeted DNA methylation sequencing of circulating tumor DNA (ctDNA). 2019.9(7): p. 2056.], and the percentage of methylated cytosine (β value) in each region was determined based on the read counts.

[0108] 6. Statistical analysis methods:

[0109] The R statistical language (v3.5.1, Bell Laboratories, Murray Hill, New Jersey, USA) and the Python programming language (v3.7.9) were used for all relevant statistical tests and modeling analyses. The DSS (Dispersion Shrinkage for Sequencing data) module corresponding algorithm in the R statistical language (for the self-organized sample cohort using the sequencing technology solution), and the minfi (Analyze Illumina Infinium DNA methylation arrays) module corresponding algorithm (for the TCGA tissue sample cohort using the chip technology solution) were used to detect differentially methylated biomarkers in their respective sample sets. The Random Forest (RF) prediction model was implemented using the RandomForestClassifier method in the scikit-learn module of the Python programming language, and the ROC curve was plotted using the pROC module in the R statistical language.

[0110] 7. A preliminary range screening of methylation biomarkers was performed on 261 lung adenocarcinoma (LUAD) tissue samples and 193 lung squamous cell carcinoma (LUSC) tissue samples in the TCGA dataset.

[0111] The total number of effective methylation biomarkers in the lung adenocarcinoma (LUAD) tissue samples participating in the differential analysis statistical test based on the minfi method was 394,266, and the total number of effective methylation biomarkers in the lung squamous cell carcinoma (LUSC) tissue samples participating in the differential analysis statistical test based on the minfi method was 394,361. The intersection of the two was 394,266 (the latter total completely included the former, with only a very small proportion of several additional ones).

[0112] Using the minfi method, 81,556 methylation biomarkers were screened from lung adenocarcinoma (LUAD) samples, which were significantly different between MET-amplified samples and negative samples (fdr < 0.05); 61,415 methylation biomarkers were screened from lung squamous cell carcinoma (LUSC) samples, which were significantly different between MET-amplified samples and negative samples (fdr < 0.05). Taking the intersection of the two, 22,053 methylation biomarkers were obtained, which were used as the results screened from the overall TCGA lung cancer data.

[0113] Subsequently, using the first cohort of 27 self-owned lung cancer tissue samples as the training set (see the previous description for details), differential analysis was first performed. The total number of effective methylation biomarkers participating in the differential analysis statistical test based on the DSS method was 811,019, of which 16,346 were significantly different between MET-amplified samples and negative samples (fdr < 0.05). Considering that there may be their own systematic fluctuations in different technical platforms and different sample sets, in order to reduce the influence of the above fluctuations and improve the discrimination signal-to-noise ratio of methylation biomarkers, the 22,053 methylation biomarkers screened by the minfi differential analysis method based on the TCGA dataset were intersected with the 16,346 methylation biomarkers screened by the DSS differential analysis method from the self-owned data training set (27 samples), and finally 144 representative methylation biomarkers were obtained as the starting point for subsequent analysis.

[0114] Based on the first cohort of 27 self-owned lung cancer tissue samples as the training set, combined with the 144 representative methylation biomarkers screened in the above steps, a random forest (RF) method was used for modeling analysis. First, all 144 features were used to build the model, and their feature importance rankings were obtained.

[0115] Then, the feature with the highest importance ranking in the model was selected, and the number of features was gradually increased one by one in this order until the top 144 features were included. The above-selected feature subsets were used to build a random forest model on the tissue samples again, and then these models were used to predict the second cohort of 15 self-owned lung cancer tissue samples as the test set, and the receiver operator characteristic curve (ROC) was drawn according to the prediction results and the area under the curve (AUC) was calculated to evaluate the performance of these models. The results are as Figure 1As shown, it shows the changing trend of the AUC of the above test set. There is an obvious inflection point near the first 5 features, and the corresponding AUC value on the test set is 0.955; the performance peak in the test set is obtained near the first 18 features, and the corresponding AUC value on the test set is 0.964; using all 144 features screened in the previous step, the corresponding AUC value on the test set is 0.946. The above results indicate that using all the preliminarily screened features can already obtain an acceptable classification effect (which can be called the "most comprehensive marker"). Further, when the first 18 features are taken, the random forest model can obtain the optimal classification effect in the test samples (which can be called the "marker for optimizing performance"). However, in actual application scenarios, it may be restricted by reasons such as cost and time. Then, when the first 5 features are taken, the classification effect of the random forest model in the test samples has a relatively high cost performance (which can be called the "marker for optimizing cost performance").

[0116] Considering a certain redundancy requirement and combining a certain cost performance requirement, when the total number of combined features is limited to less than 100, the combinations composed of the first 39, first 89, first 93, first 96, and first 97 features can be included together. The AUC of these combinations in the 15-case test set also reaches the highest level (0.964). Table 1 is a summary of all the selected combination situations.

[0117] Table 1 shows the results of using the random forest method to build a model in the first batch of 27 self-owned lung cancer tissue sample cohorts as the training set based on the above feature selection set and detecting in the second batch of 15 self-owned lung cancer tissue sample cohorts as the test set.

[0118] Feature grouping AUC of 15 cases in test set Number of features Combination naming Top 5 features 0.955 5 5-marker combination Top 18 features 0.964 18 18-marker combination Top 39 features 0.964 39 39-marker combination Top 89 features 0.964 89 89-marker combination Top 93 features 0.964 93 93-marker combination Top 96 features 0.964 96 96-marker combination Top 97 features 0.964 97 97-marker combination All preliminarily screened features 0.946 144 144-marker combination

[0119] Figures 2 - 4 Shows the signal distribution of the 144-marker combination, 18-marker combination, and 5-marker combination in the above-selected feature combinations in the training set (27 samples) and test set (15 samples), which is presented using a heatmap. Each column represents a sample, each row represents a marker, and the middle area shows the distribution of the corresponding marker for the corresponding sample. The narrow strip at the top shows the clustering of different grouped samples (MET amplification samples (positive) and MET negative samples (negative)). From Figures 2 - 4 it can be seen that the selected marker combinations can make similar samples have similar signal distributions of markers, so they tend to cluster together; Figures 5 - 12 Shows the ROC curve shape of the above-selected feature combinations in the test set (15 samples). The area under the curve is the AUC value. Combining the curve properties (the more the curve is distributed towards the upper left corner, the better the model performance) andFigures 5 - 12 From the actual situation, it can be seen that the model constructed using the selected biomarker combination has good classification effects; Table 2 below provides information such as the location information of the 144 selected methylated biomarkers, and the methylation sites are all aligned to the corresponding positions of hg19.

[0120] Table 2 List of characteristics of 144 methylated biomarkers for random forest modeling and evaluation (positions aligned to the hg19 version of the human genome, arranged from high to low according to the feature importance obtained from the modeling)

[0121]

[0122]

[0123]

[0124]

[0125] Among them, intronic is intron, intergenic is intergenic sequence, exonic is exon, upstream is upstream fragment, and downstream is downstream fragment. According to the serial numbers, the alternative features of serial numbers 1 - 5 are the selected 5-marker combination, the alternative features of serial numbers 1 - 18 are the selected 18-marker combination, the alternative features of serial numbers 1 - 39 are the selected 39-marker combination, the alternative features of serial numbers 1 - 89 are the selected 89-marker combination, the alternative features of serial numbers 1 - 93 are the selected 93-marker combination, the alternative features of serial numbers 1 - 96 are the selected 96-marker combination, the alternative features of serial numbers 1 - 97 are the selected 97-marker combination, and the alternative features of serial numbers 1 - 144 are the selected 144-marker combination.

[0126] The above results indicate that by comprehensively considering the self-owned data training set (27 samples) and the differential analysis results of TCGA data, and combining methods such as machine learning modeling, the 144-marker combination was screened out (including regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, chr17:73360253, chr6:16483998, chr12:120539138, chr5:150003038, chr7:38316654, chr16:89189384, chr7:110506411, chr3:58103186, chr1:160681405, chr6:42071220, chr11:65211710, chr2:177054306, chr2:191875807, chr8:124515503, chr5:145453108, chr15:42750463, chr11:47510561, chr14:68861096, chr7:95050561, chr12:132259591, chr1:155938911, chr8:30284255, chr10:86963122, chr3:49817941, chr16:4745215, chr17:30810854, chr19:13196617, chr4:3180930, chr2:43414162, chr4:152096590, chr22:39918625, chr19:40928737, chr6:138859577, chr12:51319063, chr8:144650595, chr9:1056641, chr1:2321365, chr2:192208956, chr5:33982209, chr1:223921358, chr7:38309985, chr16:14326544, chr1:206671182, chr4:85878806, chr11:64631357, chr6:11429245, chr17:41900588, chr7:41747323, chr17:57449016, chr9:134609066, chr3:38010392, chr1:112263095, chr1:168212211, chr19:46214108, chr9:117012651, chr11:125985045, chr19:45261005, chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633,chr11:92511989,chr14:64926333,chr17:7128558,chr1:151008412,chr20:23344170,chr17:79006167,chr17:2324862,chr11:92572718,chr3:45641120,chr7:30515286,chr12:102216984,chr10:102028800,chr13:76387161,chr1:35449720,chr2:66159331,chr1:27916108,chr1:101705238,chr1:183601018,chr6:41757331,chr17:55186659,chr17:38520654,chr5:124013537,chr1:180127817,chr11:71710885,chr10:43891460,chr21:40194848,chr14:21359737,chr17:25905125,chr8:48595620,chr7:150782539,chr6:36165617,chr22:33257641,chr1:240370408,chr8:41895101,chr15:75941357, chr18:46482440, chr8:17625476, chr2:62533140, chr4:109994040, chr6:38608738, chr1:205630723, chr19:3178760, chr2:28117087, chr5:176920260, chr3:134094538, chr1:33803806, chr11:71710643, chr12:88784938, etc.), as well as 5-marker combinations with appropriately reduced number of features based on these 144 alternative features without significant performance degradation (including regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, etc.), 18-marker combinations (including regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, chr17:73360253, chr6:16483998, chr12:120539138, chr5:150003038, chr7:38316654, chr16:89189384, chr7:110506411, chr3:58103186, chr1:160681405, chr6:42071220, chr11:65211710, chr2:177054306, chr2:191875807, etc.), 39-marker combinations (including regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821, chr7:104752829, chr17:73360253, chr6:16483998, chr12:120539138, chr5:150003038, chr7:38316654, chr16:89189384, chr7:110506411, chr3:58103186, chr1:160681405, chr6:42071220, chr11:65211710, chr2:177054306, chr2:191875807, chr8:124515503, chr5:145453108, chr15:42750463, chr11:47510561, chr14:68861096, chr7:95050561, chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063, etc.), 89-marker combination (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516 etc.), 93 - marker combination (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325 etc.), 96-marker combinations (including regions chr14:102290371, chr8:17580951, chr4:901982, chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633 etc.) and 97-marker combination (including regions chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633,chr11:92511989, etc.) as alternative combinations. These combinations have effective discrimination ability in tissue samples with different MET amplification conditions and can be used to assist in detecting MET amplification conditions.,

[0127] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A methylation biomarker combination for assisting in identifying the MET amplification situation, characterized in that, The methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829, and the methylation sites are aligned to the corresponding positions in hg19.

2. The methylation biomarker combination for assisting in identifying the MET amplification situation according to claim 1, wherein The methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807, and the methylation sites are aligned to the corresponding positions in hg19.

3. The methylation biomarker combination for assisting in identifying MET amplification according to claims 1-2, characterized in that, The methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063. The methylation sites are aligned to the corresponding positions in hg19.

4. The methylation biomarker combination for assisting in identifying MET amplification according to claims 1-3, characterized in that, The methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516, the methylation sites are aligned to the corresponding positions in hg19., 5. The methylation biomarker combination for assisting in identifying the MET amplification status according to claims 1-4, characterized in that, The methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325, The methylation sites are aligned to the corresponding positions in hg19., 6. The methylation biomarker combination for assisting in identifying the MET amplification status according to claims 1-5, characterized in that, The methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633, The methylation sites are aligned to the corresponding positions in hg19., 7. The methylation biomarker combination for assisting in identifying MET amplification according to claims 1-6, characterized in that, The methylation biomarker combination includes the following biomarkers: chr14:102290371,chr8:17580951,chr4:901982,chr19:40928821,chr7:104752829,chr17:73360253,chr6:16483998,chr12:120539138,chr5:150003038,chr7:38316654,chr16:89189384,chr7:110506411,chr3:58103186,chr1:160681405,chr6:42071220,chr11:65211710,chr2:177054306,chr2:191875807,chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633,chr11:92511989, the methylation sites are aligned to the corresponding positions in hg19., 8. The methylation biomarker combination for assisting in identifying MET amplification according to claims 1-7, characterized in that, The methylation biomarker combination further includes at least one selected from the following: chr8:124515503,chr5:145453108,chr15:42750463,chr11:47510561,chr14:68861096,chr7:95050561,chr12:132259591,chr1:155938911,chr8:30284255,chr10:86963122,chr3:49817941,chr16:4745215,chr17:30810854,chr19:13196617,chr4:3180930,chr2:43414162,chr4:152096590,chr22:39918625,chr19:40928737,chr6:138859577,chr12:51319063,chr8:144650595,chr9:1056641,chr1:2321365,chr2:192208956,chr5:33982209,chr1:223921358,chr7:38309985,chr16:14326544,chr1:206671182,chr4:85878806,chr11:64631357,chr6:11429245,chr17:41900588,chr7:41747323,chr17:57449016,chr9:134609066,chr3:38010392,chr1:112263095,chr1:168212211,chr19:46214108,chr9:117012651,chr11:125985045,chr19:45261005,chr7:116377467,chr2:106792427,chr17:61520113,chr2:11518494,chr11:73692156,chr19:38755753,chr17:56019314,chr3:141118615,chr17:43367610,chr21:46948366,chr16:85010598,chr11:118781763,chr3:64136334,chr9:38061779,chr10:80834947,chr16:3115287,chr1:21947438,chr1:9210824,chr16:69962827,chr6:158097037,chr17:15336969,chr14:105784091,chr14:56800154,chr17:80851007,chr14:102647731,chr3:15085146,chr7:70599516,chr10:103574627,chr8:30248467,chr1:10743217,chr17:60994325,chr17:16976476,chr1:12513497,chr17:74019633,chr11:92511989,chr14:64926333,chr17:7128558,chr1:151008412,chr20:23344170,chr17:79006167,chr17:2324862,chr11:92572718,chr3:45641120,chr7:30515286,chr12:102216984,chr10:102028800,chr13:76387161,chr1:35449720,chr2:66159331,chr1:27916108,chr1:101705238,chr1:183601018,chr6:41757331,chr17:55186659,chr17:38520654,chr5:124013537,chr1:180127817,chr11:71710885,chr10:43891460,chr21:40194848,chr14:21359737,chr17:25905125,chr8:48595620,chr7:150782539,chr6:36165617,chr22:33257641,chr1:240370408,chr8:41895101,chr15:75941357,chr18:46482440,chr8:17625476,chr2:62533140,chr4:109994040,chr6:38608738,chr1:205630723,chr19:3178760,chr2:28117087,chr5:176920260,chr3:134094538,chr1:33803806,chr11:71710643,chr12:88784938, the methylation sites are aligned to the corresponding positions in hg19., 9. The methylation biomarker combination for assisting in identifying MET amplification according to claim 8, characterized in that, The biomarker combination is based on the methylation biomarker combination for assisting in identifying the MET amplification status described in any one of claims 1-7, and is a combination composed of any at least one and a total of no more than 100 biomarkers selected from the first 100 biomarkers in Table 2.

10. The methylation biomarker combination for assisting in identifying MET amplification according to claims 1-9, characterized in that, The methylation biomarker combination is the 144 biomarkers in Table 2.

11. Use of the methylation biomarker combination according to any one of claims 1-10 or a reagent for detecting the degree of methylation difference thereof in the preparation of a kit for predicting, detecting, or otherwise evaluating the MET amplification status.

12. An auxiliary detection kit for MET amplification condition, characterized in that, It includes a reagent for detecting the degree of methylation difference of the methylation biomarker combination according to any one of claims 1-10.

13. The MET amplification situation-assisted detection kit according to claim 12, characterized in that, The kit is prepared by using polymerase chain reaction technology, in situ hybridization technology, enzymatic mutation detection technology, chemical cleavage mismatch technology, mass spectrometry analysis technology, gene chip technology, gene sequencing technology, or a combination thereof.

14. Use of the kit according to claim 12 or 13 in the preparation of a reagent for predicting, detecting, classifying, monitoring treatment, prognosis or evaluating drug resistance of malignancies associated with MET amplification.

15. An auxiliary detection method for MET amplification for non-diagnostic purposes, characterized in that, The auxiliary detection method comprises the following steps: extracting genomic DNA of a biological sample to be detected; performing bisulfite conversion on the DNA; detecting the methylation difference degree of the methylation biomarker combination according to any one of claims 1-10.

16. The auxiliary detection method according to claim 15, characterized in that, The detection of the methylation difference degree of the methylation biomarker combination according to any one of claims 1-10 includes using methylation-specific PCR, bisulfite PCR sequencing, real-time quantitative methylation-specific PCR or high-throughput detection techniques, detection techniques based on detection platforms such as mass spectrometry; detection techniques based on chip detection platforms; preferably, the high-throughput detection techniques include reduced-representation bisulfite sequencing, whole-genome bisulfite sequencing, DNA enrichment sequencing, pyrosequencing, bisulfite conversion sequencing.

17. The auxiliary detection method according to claim 15, wherein The biological sample is tissue, peripheral blood, saliva, pleural effusion, ascites, amniotic fluid, bone marrow or cultured animal cells, preferably, the biological sample is tissue.

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