TW202413655A - Use of markers in diagnosing breast cancer or predicting breast cancer risk - Google Patents

Abstract

The present invention relates to the use of markers in diagnosing breast cancer or predicting breast cancer risk. The present invention discloses the use of reagents in preparing a kit or microarray for diagnosing breast cancer or predicting breast cancer risk in an individual. The present invention also discloses a kit or microarray for diagnosing breast cancer or predicting breast cancer risk in an individual.

Description

Use of markers in diagnosing breast cancer or predicting breast cancer risk

This application relates to the field of molecular biomedical technology. Specifically, this application relates to the use of markers in diagnosing breast cancer or predicting breast cancer risk.

Breast cancer is the malignant cancer with the highest incidence and mortality rate in women. Among them, 5%-10% of breast cancer patients are caused by genetic mutations, and other cancer patients are mainly induced by environmental factors (Feng Y, Spezia M, Huang S, et al. Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis. Genes Dis. 2018; 5: 77-106.). Relevant statistical studies have shown that in recent years, due to changes in diet structure and lifestyle such as late marriage and late childbearing, the incidence of breast cancer in my country has increased by 30%, and it mainly occurs in women aged 30-60, which seriously threatens women's health (Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016; 66: 115-32.). Early screening of breast cancer is of great significance to the survival of patients. The 5-year survival rate of patients with early breast cancer is 98%, while the 5-year survival rate of patients with late-stage breast cancer drops to 23% (DeSantis CE, Ma J, Gaudet MM, et al. Breast cancer statistics, 2019. CA Cancer J Clin. 2019; 69: 438-51.). The widespread application of early cancer screening technology has significantly reduced cancer mortality. Regular breast cancer screening technology for high-risk women has reduced breast cancer mortality in the United States by 39% between 1990 and 2015 (Byers T, Wender RC, Jemal A, et al. The American Cancer Society challenge goal to reduce US cancer mortality by 50% between 1990 and 2015: Results and reflections. CA Cancer J Clin. 2016; 66: 359-69.). The current clinical strategies for breast cancer diagnosis are: 1. Breast digital examination and physical examination by a doctor; 2. Breast ultrasound and mammography; 3. Fine needle biopsy, etc. Among them, mammography can detect breast tumors and find signs of malignant tumors such as breast microcalcification, and is often used for early screening of high-risk women (Oeffinger KC, Fontham ET, Etzioni R, et al. Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. JAMA. 2015; 314: 1599-614.). The accuracy of mammography is closely related to breast structure. Its overall sensitivity is about 85%, but its sensitivity drops to 47.8%-64.4% in dense breast screening (Kolb TM, Lichy J, Newhouse JH. Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. Radiology. 2002; 225: 165-75.). Asian women generally have dense breast structures, and the accuracy of mammography is low. In addition, mammography screening has radiation hazards and is not suitable for regular testing of young women and pregnant women. In clinical practice, other safe, convenient and accurate examination methods are urgently needed for screening of early breast cancer. Liquid biopsy is a non-invasive detection technology based on blood or other body fluids. It has the advantages of safe and non-invasive sampling, high efficiency and convenience, and has gradually been applied to the detection of various diseases. Studies have found that body fluids contain a large amount of free DNA (cfDNA) released by tissues or cells. The body fluids of cancer patients also contain free tumor DNA (ctDNA) released by cancer tissues into the body fluids. Its genomic characteristics such as mutations, fragment length distribution, terminal motifs, DNA methylation, etc. can all be used as characteristic indicators for early cancer diagnosis (Lo YMD, Han DSC, Jiang P, et al. Epigenetics, fragmentomics, and topology of cell-free DNA in liquid biopsies. Science. 2021; 372.). Among them, DNA methylation has the advantages of low detection limit, high sensitivity, and high specificity. It has been successfully applied to the diagnosis of various cancers. For example, the methylation levels of 6 methylation markers in colorectal cancer plasma were used to construct a machine learning diagnosis model with a specificity of 92% and a sensitivity of 86% (Cai G, Cai M, Feng Z, et al. A Multilocus Blood-Based Assay Targeting Circulating Tumor DNA Methylation Enables Early Detection and Early Relapse Prediction of Colorectal Cancer. Gastroenterology. 2021; 161: 2053-56 e2.); the methylation levels of 10 methylation markers in liver cancer plasma were used to construct a machine learning diagnosis model with a specificity of 90.5% and a sensitivity of 83.3% (Xu RH, Wei W, Krawczyk M, et al. Circulating tumour DNA methylation markers for diagnosis and prognosis of hepatocellular carcinoma. Nat Mater. 2017; 16: 1155-61.), far higher than other screening methods. At present, a large number of studies have revealed that DNA methylation plays an important role in the occurrence and development of breast cancer, and a large number of differentially methylated regions in breast cancer tissues and adjacent tissues have been identified, promoting the study of breast cancer methylation markers (Batra RN, Lifshitz A, Vidakovic AT, et al. DNA methylation landscapes of 1538 breast cancers reveal a replication-linked clock, epigenomic instability and cis-regulation. Nat Commun. 2021; 12: 5406.). However, there is no particularly efficient detection marker for early breast cancer screening based on liquid biopsy. Therefore, it is urgent to develop a method and/or kit that can efficiently read epigenetic information related to breast cancer from extremely limited extracellular free DNA in biological samples, and that can be easily configured and reliably applied in hospital laboratory.

To address the shortcomings of the existing technology, the inventors screened a large number of markers and found that the markers of the present invention can diagnose breast cancer or predict breast cancer risk with high sensitivity, specificity and low cost. Based on the markers of the present invention, breast cancer patients and healthy people can be effectively distinguished. More specifically, the present invention provides 79 cfDNA methylation markers and establishes a diagnostic model for the relationship between the methylation level of methylation markers and breast cancer. The model has the advantages of non-invasive detection, safe and convenient detection, high throughput and high detection accuracy. In one aspect, the present invention relates to the use of a reagent in preparing a kit or microarray for diagnosing breast cancer or predicting breast cancer risk in an individual, characterized in that the reagent is used to detect the methylation level of at least one target region of at least one marker selected from any of the following groups in a sample isolated from the individual: (1) TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1, TNFRSF6B and any combination thereof; (2) SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3, WISP2, and any combination thereof; or (3) WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3, CHRNA4, and any combination thereof, wherein the methylation level of at least one target region of one or more markers is equal to or higher than a threshold value compared to a corresponding threshold value, indicating that the individual has breast cancer or is at risk for breast cancer, and wherein the target region comprises at least one CpG dinucleotide sequence. In some embodiments, the methylation is CpG methylation. In some embodiments, the reagent is selected from the following reagents: i) a substance that hybridizes with at least one target region of the marker or amplifies at least one target region of the marker, such as an oligonucleotide primer or probe; and ii) a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, which distinguishes between methylated and unmethylated dinucleotides, such as methylated and unmethylated CpG dinucleotides, within at least one target region of the marker. In some embodiments, the oligonucleotide primer or probe is complementary or identical to a fragment of at least 9 bases long of at least one target region of the marker. In some embodiments, the marker is TTLL10. In some embodiments, the marker is EPS8L3. In some embodiments, the marker is IRF2BP2. In some embodiments, the marker is FAM150B. In some embodiments, the marker is ID2. In some embodiments, the marker is TERT. In some embodiments, the marker is PITX1. In some embodiments, the marker is KCNMB1. In some embodiments, the marker is BEND6. In some embodiments, the marker is ELN. In some embodiments, the marker is CPXM2. In some embodiments, the marker is TH. In some embodiments, the marker is C1QTNF9. In some embodiments, the marker is CARKD. In some embodiments, the marker is TMEM179. In some embodiments, the marker is SPNS1. In some embodiments, the marker is MYO15B. In some embodiments, the marker is DNM2. In some embodiments, the marker is EPHX3. In some embodiments, the marker is PSG8. In some embodiments, the marker is SLCO4A1. In some embodiments, the marker is TNFRSF6B. In some embodiments, the marker is SKI. In some embodiments, the marker is PRDM16. In some embodiments, the marker is PIAS3. In some embodiments, the marker is SLC10A4. In some embodiments, the marker is CXXC5. In some embodiments, the marker is NR2E1. In some embodiments, the marker is MPC1. In some embodiments, the marker is HOXA13. In some embodiments, the marker is LZTS1. In some embodiments, the marker is CHD7. In some embodiments, the marker is ANKRD20A1. In some embodiments, the marker is CACNA1B. In some embodiments, the marker is ACVRL1. In some embodiments, the marker is CCNA1. In some embodiments, the marker is RNASEH2B. In some embodiments, the marker is SNX20. In some embodiments, the marker is TBCD. In some embodiments, the marker is PIP5K1C. In some embodiments, the marker is ZBTB7A. In some embodiments, the marker is DNASE2. In some embodiments, the marker is TSHZ3. In some embodiments, the marker is WISP2. In some embodiments, the marker is WRAP73. In some embodiments, the marker is C2CD4D. In some embodiments, the marker is CCDC181. In some embodiments, the marker is RNF144A. In some embodiments, the marker is SIX2. In some embodiments, the marker is NRXN1. In some embodiments, the marker is MEIS1. In some embodiments, the marker is LBX2. In some embodiments, the marker is AMT. In some embodiments, the marker is ITIH4. In some embodiments, the marker is TRH. In some embodiments, the marker is SHOX2. In some embodiments, the marker is DGKG. In some embodiments, the marker is RPL9. In some embodiments, the marker is PFN3. In some embodiments, the marker is FOXC1. In some embodiments, the marker is LY86. In some embodiments, the marker is SLC35F1. In some embodiments, the marker is LRRC4. In some embodiments, the marker is PDLIM2. In some embodiments, the marker is PAX2. In some embodiments, the marker is MVK. In some embodiments, the marker is DTX1. In some embodiments, the marker is RBM19. In some embodiments, the marker is GCH1. In some embodiments, the marker is OTX2. In some embodiments, the marker is ZSCAN10. In some embodiments, the marker is AHSP. In some embodiments, the marker is NLRC5. In some embodiments, the marker is ASXL3. In some embodiments, the marker is TCF4. In some embodiments, the marker is PLIN3. In some embodiments, the marker is RASAL3. In some embodiments, the marker is CHRNA4. In some embodiments, the marker is a marker combination selected from the following: i) TTLL10, FAM150B, BEND6, ELN, TMEM179 and MYO15B; or ii) EPS8L3, IRF2BP2, TERT, TH, CARKD, SPNS1 and PSG8. In some embodiments, the marker is a marker combination selected from the following: i) SKI, PRDM16, LZTS1, CCNA1, PIP5K1C and WISP2; or ii) PIAS3, CHD7, CACNA1B, ACVRL1, SNX20, TBCD and ZBTB7A. In some embodiments, the marker is a marker combination selected from: i) ITIH4, FOXC1, PDLIM2, MVK, NLRC5, TCF4 and PLIN3; or ii) RNF144A, SIX2, DGKG, RPL9, LRRC4 and ZSCAN10. In some embodiments, the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids and combinations thereof; preferably, the sample is selected from plasma, serum, whole blood, isolated blood cells and combinations thereof; more preferably, the sample is plasma cfDNA or ctDNA. In some embodiments, the target region is selected from the group consisting of: chr1:1095763-1095986, chr1:110334699-110334899, chr1:234845168-234845486, chr2:469568-469933, chr2:8314701-8314901, chr5:1291139-1291339, chr5:134374 689-134374889, chr5:169805839-169806039, chr6:56716287-56716518, chr7:73407894-73408161, chr10:125650986-125651186, chr11:2226052-2226252, chr13:111277395-111277690, chr1 3:24844736-24844936, chr14:105102434-105102644, chr16:28984534-28984734, chr17:73607909-73608115, chr19:10823485-10823947, chr19:15344061-15344322, chr19:43271257-432714 57, chr20:61304694-61304954, chr20:62330559-62330808, or complementary sequences or treated sequences thereof (e.g., bisulfite-converted corresponding sequences or MSRE-treated corresponding sequences); or treated sequences of the complementary sequences (e.g., bisulfite-converted corresponding sequences or MSRE-treated corresponding sequences); or any combination of the foregoing sequences and/or regions; or The target region is selected from: region chr1:2166118-2166318, chr1:2978722-2978922, chr1:145562922-145563122, chr4:48485417-48485821, chr5:139076623-139076941, chr6:108488634-1084 88917, chr6:166970625-166970825, chr7:27260117-27260462, chr8:20375580-20375780, chr8:61788861-61789200, chr9:68413067-68413267, chr9:140683687-1406839 69, chr12:52311647-52311991, chr13:37005935-37006328, chr13:51417486-51417774, chr16:50715367-50715567, chr17:80745056-80745446, chr19:3688030-3688230, chr19:4059528-4059746, chr19:12978686-12978886, chr19:31842771-31842971, chr20:43331809-43332099 or their complementary sequences or processed sequences; or the processed sequence of the complementary sequence; or any combination of the foregoing sequences and/or regions; or The target region is selected from: region chr1:3567381-3567648, chr1:151811354-151811554, chr1:169396540-169396740, chr2:7148520-7148720, chr2:45232498-45232698, chr2:50574443-50574739, chr2:66666356-66666556, chr2:74731340-74731602, chr3:49459532-49459732, chr3: 52864771-52864971、chr3:52865018-52865236、chr3:129693578-129693796、chr3:157825025-157825225、chr3:185973717-185973917、chr4:39448374-39448574、chr5:176829529-176829796、chr6:1614911-1615144、chr6:6724534-6724734、chr6:118229139-11 8229400, chr7:127744150-127744731, chr8:22438141-22438341, chr10:102497304-102497504, chr12:109996613-109997009, chr12:113515300-113515540, chr12:114162628-114162828, chr14:55243006-55243206, chr14:57264908-57265108, chr16:3139015-3 139246, chr16:31580122-31580353, chr16:57025884-57026193, chr18:31159160-31159360, chr18:53447617-53447817, chr19:4912069-4912269, chr19:15580341-15580719, chr20:62046355-62046589 or their complementary sequences or processed sequences; or the processed sequence of the complementary sequence; or any combination of the foregoing sequences and/or regions. In another aspect, the present invention relates to a kit or microarray for diagnosing breast cancer or predicting the risk of breast cancer in an individual, characterized in that the kit or microarray comprises a reagent for detecting the methylation level of at least one target region of at least one marker selected from any of the following groups in a sample isolated from the individual: (1) TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1, TNFRSF6B, and any combination thereof; (2) SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3, WISP2, and any combination thereof; or (3) WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3, CHRNA4, and any combination thereof, wherein the methylation level of the target region of one or more markers is equal to or higher than the threshold value compared to the corresponding threshold value, indicating that the individual has breast cancer or has a risk of breast cancer, and wherein the target region comprises at least one CpG dinucleotide sequence. In some embodiments, the methylation is CpG methylation. In some embodiments, the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids, and combinations thereof; preferably, the sample is selected from plasma, serum, whole blood, isolated blood cells, and combinations thereof; more preferably, the sample is plasma cfDNA or ctDNA. In some embodiments, the reagent is selected from the following reagents: i) a substance that hybridizes with at least one target region of the marker or amplifies at least one target region of the marker, such as an oligonucleotide primer or probe, preferably, the oligonucleotide primer or probe is complementary or identical to a fragment of at least 9 bases long of at least one target region of the marker; and ii) a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, which distinguishes methylated and unmethylated dinucleotides in at least one target region of the marker, such as methylated and unmethylated CpG dinucleotides. In some embodiments, the marker is TTLL10. In some embodiments, the marker is EPS8L3. In some embodiments, the marker is IRF2BP2. In some embodiments, the marker is FAM150B. In some embodiments, the marker is ID2. In some embodiments, the marker is TERT. In some embodiments, the marker is PITX1. In some embodiments, the marker is KCNMB1. In some embodiments, the marker is BEND6. In some embodiments, the marker is ELN. In some embodiments, the marker is CPXM2. In some embodiments, the marker is TH. In some embodiments, the marker is C1QTNF9. In some embodiments, the marker is CARKD. In some embodiments, the marker is TMEM179. In some embodiments, the marker is SPNS1. In some embodiments, the marker is MYO15B. In some embodiments, the marker is DNM2. In some embodiments, the marker is EPHX3. In some embodiments, the marker is PSG8. In some embodiments, the marker is SLCO4A1. In some embodiments, the marker is TNFRSF6B. In some embodiments, the marker is SKI. In some embodiments, the marker is PRDM16. In some embodiments, the marker is PIAS3. In some embodiments, the marker is SLC10A4. In some embodiments, the marker is CXXC5. In some embodiments, the marker is NR2E1. In some embodiments, the marker is MPC1. In some embodiments, the marker is HOXA13. In some embodiments, the marker is LZTS1. In some embodiments, the marker is CHD7. In some embodiments, the marker is ANKRD20A1. In some embodiments, the marker is CACNA1B. In some embodiments, the marker is ACVRL1. In some embodiments, the marker is CCNA1. In some embodiments, the marker is RNASEH2B. In some embodiments, the marker is SNX20. In some embodiments, the marker is TBCD. In some embodiments, the marker is PIP5K1C. In some embodiments, the marker is ZBTB7A. In some embodiments, the marker is DNASE2. In some embodiments, the marker is TSHZ3. In some embodiments, the marker is WISP2. In some embodiments, the marker is WRAP73. In some embodiments, the marker is C2CD4D. In some embodiments, the marker is CCDC181. In some embodiments, the marker is RNF144A. In some embodiments, the marker is SIX2. In some embodiments, the marker is NRXN1. In some embodiments, the marker is MEIS1. In some embodiments, the marker is LBX2. In some embodiments, the marker is AMT. In some embodiments, the marker is ITIH4. In some embodiments, the marker is TRH. In some embodiments, the marker is SHOX2. In some embodiments, the marker is DGKG. In some embodiments, the marker is RPL9. In some embodiments, the marker is PFN3. In some embodiments, the marker is FOXC1. In some embodiments, the marker is LY86. In some embodiments, the marker is SLC35F1. In some embodiments, the marker is LRRC4. In some embodiments, the marker is PDLIM2. In some embodiments, the marker is PAX2. In some embodiments, the marker is MVK. In some embodiments, the marker is DTX1. In some embodiments, the marker is RBM19. In some embodiments, the marker is GCH1. In some embodiments, the marker is OTX2. In some embodiments, the marker is ZSCAN10. In some embodiments, the marker is AHSP. In some embodiments, the marker is NLRC5. In some embodiments, the marker is ASXL3. In some embodiments, the marker is TCF4. In some embodiments, the marker is PLIN3. In some embodiments, the marker is RASAL3. In some embodiments, the marker is CHRNA4. In some embodiments, the marker is a marker combination selected from the following: i) TTLL10, FAM150B, BEND6, ELN, TMEM179 and MYO15B; or ii) EPS8L3, IRF2BP2, TERT, TH, CARKD, SPNS1 and PSG8. In some embodiments, the marker is a marker combination selected from the following: i) SKI, PRDM16, LZTS1, CCNA1, PIP5K1C and WISP2; or ii) PIAS3, CHD7, CACNA1B, ACVRL1, SNX20, TBCD and ZBTB7A. In some embodiments, the marker is a marker combination selected from the following: i) ITIH4, FOXC1, PDLIM2, MVK, NLRC5, TCF4 and PLIN3; or ii) RNF144A, SIX2, DGKG, RPL9, LRRC4 and ZSCAN10. In some embodiments, the target region is selected from the group consisting of: chr1:1095763-1095986, chr1:110334699-110334899, chr1:234845168-234845486, chr2:469568-469933, chr2:8314701-8314901, chr5:1291139-1291339, chr5:134374 689-134374889, chr5:169805839-169806039, chr6:56716287-56716518, chr7:73407894-73408161, chr10:125650986-125651186, chr11:2226052-2226252, chr13:111277395-111277690, chr1 3:24844736-24844936, chr14:105102434-105102644, chr16:28984534-28984734, chr17:73607909-73608115, chr19:10823485-10823947, chr19:15344061-15344322, chr19:43271257-432714 57, chr20:61304694-61304954, chr20:62330559-62330808, or complementary sequences or treated sequences thereof (e.g., bisulfite-converted corresponding sequences or MSRE-treated corresponding sequences); or treated sequences of the complementary sequences (e.g., bisulfite-converted corresponding sequences or MSRE-treated corresponding sequences); or any combination of the foregoing sequences and/or regions; or The target region is selected from: region chr1:2166118-2166318, chr1:2978722-2978922, chr1:145562922-145563122, chr4:48485417-48485821, chr5:139076623-139076941, chr6:108488634-1084 88917, chr6:166970625-166970825, chr7:27260117-27260462, chr8:20375580-20375780, chr8:61788861-61789200, chr9:68413067-68413267, chr9:140683687-1406839 69, chr12:52311647-52311991, chr13:37005935-37006328, chr13:51417486-51417774, chr16:50715367-50715567, chr17:80745056-80745446, chr19:3688030-3688230, chr19:4059528-4059746, chr19:12978686-12978886, chr19:31842771-31842971, chr20:43331809-43332099 or their complementary sequences or processed sequences; or the processed sequence of the complementary sequence; or any combination of the foregoing sequences and/or regions; or The target region is selected from: region chr1:3567381-3567648, chr1:151811354-151811554, chr1:169396540-169396740, chr2:7148520-7148720, chr2:45232498-45232698, chr2:50574443-50574739, chr2:66666356-66666556, chr2:74731340-74731602, chr3:49459532-49459732, chr3: 52864771-52864971、chr3:52865018-52865236、chr3:129693578-129693796、chr3:157825025-157825225、chr3:185973717-185973917、chr4:39448374-39448574、chr5:176829529-176829796、chr6:1614911-1615144、chr6:6724534-6724734、chr6:118229139-11 8229400, chr7:127744150-127744731, chr8:22438141-22438341, chr10:102497304-102497504, chr12:109996613-109997009, chr12:113515300-113515540, chr12:114162628-114162828, chr14:55243006-55243206, chr14:57264908-57265108, chr16:3139015-3 139246, chr16:31580122-31580353, chr16:57025884-57026193, chr18:31159160-31159360, chr18:53447617-53447817, chr19:4912069-4912269, chr19:15580341-15580719, chr20:62046355-62046589 or their complementary sequences or processed sequences; or the processed sequence of the complementary sequence; or any combination of the foregoing sequences and/or regions. In another aspect, the present invention relates to a method for diagnosing breast cancer or predicting breast cancer risk in an individual, the method comprising the following steps: (a) obtaining a biological sample containing DNA from the individual; and (b) treating the DNA in the biological sample obtained in step (a) with a reagent capable of distinguishing methylated and unmethylated sites, such as CpG sites, in the DNA, thereby obtaining treated DNA; (c) optionally, pre-amplifying at least one target region of at least one target marker in the treated DNA obtained from step (b) with a pre-amplification primer pool, wherein at least one target region of each target marker is pre-amplified to obtain at least one pre-amplification product, and the at least one target marker comprises one or more markers selected from any one of the following groups: (1) TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1, TNFRSF6B, and any combination thereof; (2) SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3, WISP2, and any combination thereof; or (3) WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3, CHRNA4, and any combination thereof, and wherein the target region comprises at least one CpG dinucleotide sequence; (d) detecting a methylation profile of at least one target region of at least one target marker in step (b) or in step (c), wherein the at least one target marker comprises one or more markers selected from any one of the following groups: (1) TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1, TNFRSF6B, and any combination thereof; (2) SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3, WISP2, and any combination thereof; or (3) WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3, CHRNA4, and any combination thereof, and (e) respectively comparing the methylation level and the corresponding threshold of at least one target region of each target marker in step (d), wherein at least one target region of one or more target markers has a methylation level equal to or higher than the threshold relative to its corresponding threshold, indicating that the individual suffers from breast cancer or has a risk of breast cancer. In some embodiments, the reagent is a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, and the bisulfite reagent or the methylation-sensitive restriction enzyme reagent distinguishes between methylated and unmethylated dinucleotides, such as methylated and unmethylated CpG dinucleotides, in at least one target region of the marker. In some embodiments, in step (c), a substance that amplifies at least one target region of the marker, such as an oligonucleotide primer, is used for amplification. In some embodiments, the oligonucleotide primer is complementary or identical to a fragment of at least 9 bases in length of at least one target region of the marker. In some embodiments, the oligonucleotide primer is selected from SEQ ID NO: 23-66. In some embodiments, in step (d), a substance hybridized with at least one target region of the marker, such as a probe, is used for detection. In some embodiments, the probe is complementary or identical to a fragment of at least 9 bases in length of at least one target region of the marker. In some embodiments, the marker is TTLL10. In some embodiments, the marker is EPS8L3. In some embodiments, the marker is IRF2BP2. In some embodiments, the marker is FAM150B. In some embodiments, the marker is ID2. In some embodiments, the marker is TERT. In some embodiments, the marker is PITX1. In some embodiments, the marker is KCNMB1. In some embodiments, the marker is BEND6. In some embodiments, the marker is ELN. In some embodiments, the marker is CPXM2. In some embodiments, the marker is TH. In some embodiments, the marker is C1QTNF9. In some embodiments, the marker is CARKD. In some embodiments, the marker is TMEM179. In some embodiments, the marker is SPNS1. In some embodiments, the marker is MYO15B. In some embodiments, the marker is DNM2. In some embodiments, the marker is EPHX3. In some embodiments, the marker is PSG8. In some embodiments, the marker is SLCO4A1. In some embodiments, the marker is TNFRSF6B. In some embodiments, the marker is SKI. In some embodiments, the marker is PRDM16. In some embodiments, the marker is PIAS3. In some embodiments, the marker is SLC10A4. In some embodiments, the marker is CXXC5. In some embodiments, the marker is NR2E1. In some embodiments, the marker is MPC1. In some embodiments, the marker is HOXA13. In some embodiments, the marker is LZTS1. In some embodiments, the marker is CHD7. In some embodiments, the marker is ANKRD20A1. In some embodiments, the marker is CACNA1B. In some embodiments, the marker is ACVRL1. In some embodiments, the marker is CCNA1. In some embodiments, the marker is RNASEH2B. In some embodiments, the marker is SNX20. In some embodiments, the marker is TBCD. In some embodiments, the marker is PIP5K1C. In some embodiments, the marker is ZBTB7A. In some embodiments, the marker is DNASE2. In some embodiments, the marker is TSHZ3. In some embodiments, the marker is WISP2. In some embodiments, the marker is WRAP73. In some embodiments, the marker is C2CD4D. In some embodiments, the marker is CCDC181. In some embodiments, the marker is RNF144A. In some embodiments, the marker is SIX2. In some embodiments, the marker is NRXN1. In some embodiments, the marker is MEIS1. In some embodiments, the marker is LBX2. In some embodiments, the marker is AMT. In some embodiments, the marker is ITIH4. In some embodiments, the marker is TRH. In some embodiments, the marker is SHOX2. In some embodiments, the marker is DGKG. In some embodiments, the marker is RPL9. In some embodiments, the marker is PFN3. In some embodiments, the marker is FOXC1. In some embodiments, the marker is LY86. In some embodiments, the marker is SLC35F1. In some embodiments, the marker is LRRC4. In some embodiments, the marker is PDLIM2. In some embodiments, the marker is PAX2. In some embodiments, the marker is MVK. In some embodiments, the marker is DTX1. In some embodiments, the marker is RBM19. In some embodiments, the marker is GCH1. In some embodiments, the marker is OTX2. In some embodiments, the marker is ZSCAN10. In some embodiments, the marker is AHSP. In some embodiments, the marker is NLRC5. In some embodiments, the marker is ASXL3. In some embodiments, the marker is TCF4. In some embodiments, the marker is PLIN3. In some embodiments, the marker is RASAL3. In some embodiments, the marker is CHRNA4. In some embodiments, the marker is a marker combination selected from the following: i) TTLL10, FAM150B, BEND6, ELN, TMEM179 and MYO15B; or ii) EPS8L3, IRF2BP2, TERT, TH, CARKD, SPNS1 and PSG8. In some embodiments, the marker is a marker combination selected from the following: i) SKI, PRDM16, LZTS1, CCNA1, PIP5K1C and WISP2; or ii) PIAS3, CHD7, CACNA1B, ACVRL1, SNX20, TBCD and ZBTB7A. In some embodiments, the marker is a combination of markers selected from the following: i) ITIH4, FOXC1, PDLIM2, MVK, NLRC5, TCF4 and PLIN3; or ii) RNF144A, SIX2, DGKG, RPL9, LRRC4 and ZSCAN10. In some embodiments, the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids and combinations thereof; preferably, the sample is selected from plasma, serum, whole blood, isolated blood cells and combinations thereof; more preferably, the sample is plasma cfDNA or ctDNA. In some embodiments, the detection is performed by gene sequencing, PCR (e.g., fluorescent PCR), FISH, immunohistochemistry, ELISA, Western or flow cytometry. In some embodiments, the target region is selected from the group consisting of: chr1:1095763-1095986, chr1:110334699-110334899, chr1:234845168-234845486, chr2:469568-469933, chr2:8314701-8314901, chr5:1291139-1291339, chr5:134374 689-134374889, chr5:169805839-169806039, chr6:56716287-56716518, chr7:73407894-73408161, chr10:125650986-125651186, chr11:2226052-2226252, chr13:111277395-111277690, chr1 3:24844736-24844936, chr14:105102434-105102644, chr16:28984534-28984734, chr17:73607909-73608115, chr19:10823485-10823947, chr19:15344061-15344322, chr19:43271257-432714 57, chr20:61304694-61304954, chr20:62330559-62330808, or complementary sequences or treated sequences thereof (e.g., bisulfite-converted corresponding sequences or MSRE-treated corresponding sequences); or treated sequences of the complementary sequences (e.g., bisulfite-converted corresponding sequences or MSRE-treated corresponding sequences); or any combination of the foregoing sequences and/or regions; or The target region is selected from: region chr1:2166118-2166318, chr1:2978722-2978922, chr1:145562922-145563122, chr4:48485417-48485821, chr5:139076623-139076941, chr6:108488634-1084 88917, chr6:166970625-166970825, chr7:27260117-27260462, chr8:20375580-20375780, chr8:61788861-61789200, chr9:68413067-68413267, chr9:140683687-1406839 69, chr12:52311647-52311991, chr13:37005935-37006328, chr13:51417486-51417774, chr16:50715367-50715567, chr17:80745056-80745446, chr19:3688030-3688230, chr19:4059528-4059746, chr19:12978686-12978886, chr19:31842771-31842971, chr20:43331809-43332099 or their complementary sequences or processed sequences; or the processed sequence of the complementary sequence; or any combination of the foregoing sequences and/or regions; or The target region is selected from: region chr1:3567381-3567648, chr1:151811354-151811554, chr1:169396540-169396740, chr2:7148520-7148720, chr2:45232498-45232698, chr2:50574443-50574739, chr2:66666356-66666556, chr2:74731340-74731602, chr3:49459532-49459732, chr3: 52864771-52864971、chr3:52865018-52865236、chr3:129693578-129693796、chr3:157825025-157825225、chr3:185973717-185973917、chr4:39448374-39448574、chr5:176829529-176829796、chr6:1614911-1615144、chr6:6724534-6724734、chr6:118229139-11 8229400, chr7:127744150-127744731, chr8:22438141-22438341, chr10:102497304-102497504, chr12:109996613-109997009, chr12:113515300-113515540, chr12:114162628-114162828, chr14:55243006-55243206, chr14:57264908-57265108, chr16:3139015-3 139246, chr16:31580122-31580353, chr16:57025884-57026193, chr18:31159160-31159360, chr18:53447617-53447817, chr19:4912069-4912269, chr19:15580341-15580719, chr20:62046355-62046589 or their complementary sequences or processed sequences; or the processed sequence of the complementary sequence; or any combination of the foregoing sequences and/or regions.

Several aspects of the present invention are described below with reference to example applications for illustration. It should be understood that many specific details, relationships, and methods are set forth to provide a full understanding of the present invention. However, a person of ordinary skill in the relevant art will readily recognize that the present invention may be practiced without one or more of the specific details or may be practiced with other methods. The present invention relates to the relationship between the methylation level of a newly discovered marker and breast cancer. The markers described herein provide methods for diagnosing breast cancer or assessing breast cancer risk in an individual. Therefore, one embodiment of the present invention represents an improvement in a marker that is suitable for diagnosing breast cancer or assessing breast cancer risk. In another embodiment, the newly discovered markers of the present invention can be used in combination with one or more other breast cancer markers known in the art (e.g., CEA, CA 15-3, CA 125, Ki-67, HER-2, ER, PR, etc.) and/or conventional examination methods such as breast finger examination and doctor's examination, breast ultrasound and mammography, fine needle aspiration tissue biopsy, etc., for example, to diagnose breast cancer or assess breast cancer risk in an individual or to prepare kits and/or microarrays for this purpose. The term "sample" means a material known or suspected to express or contain the markers described herein. The sample may be derived from a biological source ("biological sample"), such as a tissue (e.g., a biopsy sample), an extract or a cell culture including cells (e.g., tumor cells), a cell lysate, and a biological or physiological fluid, such as whole blood, plasma, serum, saliva, cerebrospinal fluid, sweat, urine, milk, peritoneal fluid, etc. The sample obtained from the source or after pretreatment to improve the characteristics of the sample (e.g., preparing plasma from blood, etc.) can be used directly. In certain aspects of the present invention, the sample is a human physiological fluid, such as human plasma. In certain aspects of the present invention, the sample is a biopsy sample such as a tumor tissue or cell obtained by tissue examination. Samples that can be analyzed according to the present invention include polynucleotides of clinical origin. As will be understood by those skilled in the art, the target polynucleotide may include DNA or RNA, in particular DNA, in particular free DNA such as extracellular free DNA. In certain specific aspects of the invention, the sample is plasma cfDNA or ctDNA. The target polynucleotide or a substance hybridized or amplified with the target polynucleotide (such as an oligonucleotide primer or probe) may be detectably labeled on one or more nucleotides using methods known in the art. The detectable label may be, but is not limited to, a luminescent label, a fluorescent label, a bioluminescent label, a chemiluminescent label, a radioactive label, and a colorimetric label. As used herein, the term "marker" refers to a target nucleic acid, gene region or methylation site whose methylation level or the score of a computational model based on the methylation level (e.g., the AUC of a ROC curve when a machine learning model such as a logical regression model is used) indicates a diagnosis of breast cancer or a high risk of breast cancer. A gene should be considered to include all its transcriptional variants and all its promoters and regulatory elements. As understood by those skilled in the art, certain genes are known to exhibit allelic variation or single nucleotide polymorphisms ("SNPs") between individuals. SNPs include insertions and deletions of simple repeat sequences (e.g., dinucleotide and trinucleotide repeats) of varying lengths. Therefore, this application should be understood to extend to all forms of markers/genes produced by any other mutation, polymorphism, or allelic gene variation. In addition, it should be understood that the term "marker" should include both the positive-sense and anti-sense sequences of the marker or gene. The term "marker" used herein is broadly interpreted to include both 1) the original marker found in a biological sample or genomic DNA (in a specific methylation state) and 2) its treated sequence (e.g., the corresponding region after bisulfite conversion or the corresponding region after MSRE treatment). The corresponding region after bisulfite conversion is different from the target marker in the genomic sequence in that one or more unmethylated cytosine residues are converted to uracil bases, thymine bases, or other bases that are different from cytosine in hybridization behavior. The corresponding region treated with MSRE differs from the target marker in the genomic sequence in that the sequence is cut at one or more MSRE cutting sites. In the present invention, "methylation state" refers to the presence, absence and/or amount of one or more methylated nucleotide bases in a nucleic acid molecule. For example, a nucleic acid molecule containing methylated cytosine is considered to be methylated, and the methylation state of the nucleic acid molecule is methylated. A nucleic acid molecule that does not contain any methylated modified cytosine is considered to be unmethylated, and the methylation state of the nucleic acid molecule is unmethylated. In some embodiments, if a nucleic acid is not methylated at a specific locus (e.g., a locus of a specific single CpG dinucleotide) or a specific combination of loci, the nucleic acid can be characterized as "unmethylated", even if it is methylated at other loci of the same gene or molecule. Therefore, the methylation state describes the state of methylation of a nucleic acid (e.g., a genomic sequence). Additionally, methylation status refers to characteristics associated with methylation of a nucleic acid segment at a specific genomic locus. Such characteristics include, but are not limited to, whether any cytosine (C) residues within the DNA sequence are methylated, the location of one or more methylated C residues, the frequency or percentage of methylated C throughout any specific region of the nucleic acid, and allelic differences in methylation due to, for example, differences in allelic origins. "Methylation status" refers to the relative concentration, absolute concentration, or pattern of methylated or unmethylated C throughout any specific region of a nucleic acid in a biological sample. For example, if one or more cytosine (C) residues within a nucleic acid sequence are methylated, it may be referred to as "hypermethylated" or having "increased methylation", whereas if one or more cytosine (C) residues within a DNA sequence are unmethylated, it may be referred to as "demethylated" or having "reduced methylation". Similarly, if one or more cytosine (C) residues within a nucleic acid sequence are methylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.), the sequence is considered to be hypermethylated or having increased methylation compared to the other nucleic acid sequence. Alternatively, if one or more cytosine (C) residues within a DNA sequence are unmethylated compared to another nucleic acid sequence (e.g., from a different region or from a different individual, etc.), the sequence is considered to be demethylated or having reduced methylation compared to the other nucleic acid sequence. In the present invention, the methylation level represents the proportion of one or more sites in the methylation state. The methylation level of a region (or a group of sites) is the average of the methylation levels of all sites in the region (or all sites in the group). Therefore, an increase or decrease in the methylation level of a region does not mean that the methylation levels of all methylated sites in the region are increased or decreased. The art knows the process of converting the results obtained by the method of detecting DNA methylation (such as simplified methylation sequencing, fluorescent quantitative PCR) into methylation levels. The "methylation level" described herein includes the relationship between the methylation status of CpGs of any number and any position in the sequence involved. The relationship can be the addition or subtraction of a methylation state parameter (e.g., 0 or 1) or the calculation result of a mathematical algorithm (e.g., mean, percentage, fraction, ratio, degree, or calculation using a mathematical model), including but not limited to methylation level metric, methylation haplotype ratio, methylation haplotype load, or when using a machine learning model such as a logical regression model, the AUC of the ROC curve. The genes used as markers in the present invention are expected to include naturally occurring variants of the genes, their complementary sequences, all of their promoters and regulatory elements (e.g., nucleic acid sequences within 5 kb (e.g., 4 kb, 3 kb, 2 kb, or 1 kb) upstream of the gene annotation start site and within 5 kb downstream of the gene annotation end site) and fragments of the genes or variants, especially fragments detectable in molecular biology. In the present invention, the terms "molecularly biologically detectable fragment", "target region" and "target gene region" can be used interchangeably. The molecularly biologically detectable fragment preferably contains at least 16, 17, 18, 19, 20, 22, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 150, 200, 250, 300 or more consecutive nucleotides of the marker. In some embodiments, the consecutive nucleotides contain at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15 or more CpG dinucleotide sequences. In some embodiments, the target gene region is preferably rich in CpG dinucleotides. In the present invention, the term "target region" or "target gene region" refers to any molecular biologically detectable fragment within the nucleic acid region consisting of the marker gene itself, 5 kb upstream of its gene annotation start site (e.g., 4 kb, 3 kb, 2 kb or 1 kb) and 5 kb downstream of its gene annotation end site (e.g., 4 kb, 3 kb, 2 kb or 1 kb), or its complementary sequence or treated sequence (e.g., the corresponding sequence after bisulfite conversion or the corresponding sequence after MSRE treatment), or the treated sequence of the complementary sequence (e.g., the corresponding sequence after bisulfite conversion or the corresponding sequence after MSRE treatment). For example, the target gene region of the target markers in Table 1A, Table 1B and Table 1C below includes its Hg19 coordinate and any molecular biologically detectable fragment within 5 kb (e.g., 4 kb, 3 kb, 2 kb or 1 kb) upstream and downstream of the coordinate, its complementary sequence or treated sequence (e.g., the corresponding sequence after bisulfite conversion or the corresponding sequence after MSRE treatment), and the treated sequence of the complementary sequence (e.g., the corresponding sequence after bisulfite conversion or the corresponding sequence after MSRE treatment). More preferably, the target gene region of the target marker in Table 1A, Table 1B and Table 1C below includes its Hg19 coordinate and any molecular biologically detectable fragment within 5 kb (e.g., 4 kb, 3 kb, 2 kb or 1 kb) upstream of the coordinate, its complementary sequence or treated sequence (e.g., the corresponding sequence after bisulfite conversion or the corresponding sequence after MSRE treatment), and the treated sequence of the complementary sequence (e.g., the corresponding sequence after bisulfite conversion or the corresponding sequence after MSRE treatment). In some embodiments, it is preferred to use and detect the target markers selected from Table 1A, Table 1B and Table 1C below and their target gene regions or any combination thereof: The TTLL10 described in this article is a protein-coding gene, also known as Tubulin Tyrosine Ligase Like 10, etc., and the function of the encoded protein is related to the activity of protein glycine ligase. The EPS8L3 described in this article is a protein-coding gene, also known as EPS8 Like 3, etc., and the encoded protein is related to epidermal growth factor receptor pathway substrate 8. The IRF2BP2 described in this article is a protein-coding gene, also known as Interferon Regulatory Factor 2 Binding Protein 2, etc., and the encoded protein binds to the IRF2 protein to form a transcriptional inhibition complex. The FAM150B described in this article is a protein-coding gene, also known as ALKAL2, ALK And LTK Ligand 2, etc., and the encoded protein is a ligand for lysine kinase ALK and LTK receptor. The ID2 described in this article is a protein-coding gene, also known as Inhibitor Of DNA Binding 2, etc., and the function of the encoded protein is related to transcriptional regulation. TERT described in this article is a protein-coding gene, also known as Telomerase Reverse Transcriptase, etc. The encoded protein is involved in the apoptosis of synovial fibroblasts and the WNT signaling pathway, etc. PITX1 described in this article is a protein-coding gene, also known as Paired Like Homeodomain 1, etc., and its function is to activate gene expression by transcriptional regulatory factors. KCNMB1 described in this article is a protein-coding gene, also known as Potassium Calcium-Activated Channel Subfamily M Regulatory Beta Subunit 1, and the encoded protein is related to the activity of potassium and calcium ion channels. BEND6 described in this article is a protein-coding gene, also known as BEN Domain Containing 6, and the encoded protein is involved in the Notch signaling pathway. ELN described in this article is a protein-coding gene, also known as Elastin, and the encoded protein is involved in the formation of the extracellular matrix. CPXM2 described in this article is a protein-coding gene, also known as Carboxypeptidase X, M14 Family Member 2, and the encoded protein is related to metal carboxypeptidase activity. TH described in this article is a protein-coding gene, also known as Tyrosine Hydroxylase, and the encoded protein function is tyrosine hydroxylase. C1QTNF9 described in this article is a protein-coding gene, also known as C1q And TNF Related 9, and the encoded protein activates AMPK, AKT, and p44/42 MAPK signaling pathways. CARKD described in this article is a protein-coding gene, also known as NAXD, NAD(P)HX Dehydratase, and the encoded protein is involved in the metabolism of water-soluble vitamins and cofactors and the niacin metabolism pathway. TMEM179 described in this article is a protein-coding gene, also known as Transmembrane Protein 179. SPNS1 described in this article is a protein-coding gene, also known as Sphingolipid Transporter 1 (Putative), and the function of the encoded protein is related to transporter activity. MYO15B described in this article is a protein-coding gene, also known as Myosin XVB. DNM2 described in this article is a protein-coding gene, also known as Dynamin 2, and the encoded protein is one of the subclasses of GTP-binding proteins. EPHX3 described in this article is a protein-coding gene, also known as Epoxide Hydrolase 3, and the encoded protein catalyzes the hydrolysis of epoxy fatty acids. PSG8 described in this article is a protein-coding gene, also known as Pregnancy Specific Beta-1-Glycoprotein 8, and the encoded protein is involved in the vascular wall cell surface interaction signaling pathway and the platelet calcium ion elevated response signaling pathway. SLCO4A1 described in this article is a protein-coding gene, also known as Solute Carrier Organic Anion Transporter Family Member 4A1, and the encoded protein is related to transport activity. TNFRSF6B described herein is a protein coding gene, also known as TNF Receptor Superfamily Member 6b, and the encoded protein belongs to the tumor necrosis factor receptor family. In some embodiments, it is preferred to use and detect two or more (e.g., 3, 4, 5, or 6) combinations of the target markers and their target gene regions in Table 1A. In some embodiments, it is preferred to use and detect the following combinations of the target markers and their target gene regions in Table 1A: i) TTLL10, FAM150B, BEND6, ELN, TMEM179, and MYO15B; or ii) EPS8L3, IRF2BP2, TERT, TH, CARKD, SPNS1, and PSG8. In some embodiments, the target marker CARKD and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker TTLL10 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker EPS8L3 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker IRF2BP2 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker FAM150B and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker ID2 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker TERT and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker PITX1 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker KCNMB1 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker BEND6 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker ELN and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker CPXM2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker TH and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker C1QTNF9 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker TMEM179 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker SPNS1 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker MYO15B and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, DNM2, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker DNM2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, EPHX3, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker EPHX3 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, PSG8, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker PSG8 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, SLCO4A1 and TNFRSF6B. In some embodiments, the target marker SLCO4A1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8 and TNFRSF6B. In some embodiments, the target marker TNFRSF6B and its target gene region are preferably used and detected, and optionally, target markers selected from the following and their target gene regions or any combination thereof are additionally used and detected: TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8 and SLCO4A1. The SKI described herein is a protein coding gene, also known as SKI Proto-Oncogene, and the function of the encoded protein is a TGF-beta signaling pathway inhibitor. The PRDM16 described herein is a protein coding gene, also known as PR/SET Domain 16, and the encoded protein can regulate gene transcription. PIAS3 described in this article is a protein-coding gene, also known as Protein Inhibitor Of Activated STAT 3, and the encoded protein inhibits the activation of the STAT3 signaling pathway. SLC10A4 described in this article is a protein-coding gene, also known as Solute Carrier Family 10 Member 4, and the encoded protein is a transport protein that participates in the transport of bile acid, etc. CXXC5 described in this article is a protein-coding gene, also known as CXXC Finger Protein 5, and the encoded protein binds to a specific DNA motif and participates in signal transduction of multiple signaling pathways such as NF-kappa-B, MAPK, and WNT. NR2E1 described in this article is a protein-coding gene, also known as Nuclear Receptor Subfamily 2 Group E Member 1, and the encoded protein participates in the formation of nuclear receptors. MPC1 described in this article is a protein-coding gene, also known as Mitochondrial Pyruvate Carrier 1, and the encoded protein participates in mitochondrial pyruvate transport. HOXA13 described in this article is a protein-coding gene, also known as Homeobox A13, and the protein it encodes is a transcription factor that participates in transcriptional regulation. LZTS1 described in this article is a protein-coding gene, also known as Leucine Zipper Tumor Suppressor 1, and the protein it encodes participates in regulating the cell cycle. CHD7 described in this article is a protein-coding gene, also known as Chromodomain Helicase DNA Binding Protein 7, and the gene body involved is annotated as chromatin binding and helicase activity. ANKRD20A1 described in this article is a protein-coding gene, also known as Ankyrin Repeat Domain 20 Family Member A1. CACNA1B described in this article is a protein-coding gene, also known as Calcium Voltage-Gated Channel Subunit Alpha1 B, and the protein it encodes participates in the formation of calcium ion channels. ACVRL1 described in this article is a protein-coding gene, also known as Activin A Receptor Like Type 1, and the protein it encodes is a TFG-beta family ligand receptor that participates in regulating vascular development. CCNA1 described in this article is a protein-coding gene, also known as Cyclin A1, and the encoded protein is involved in regulating the cell cycle. RNASEH2B described in this article is a protein-coding gene, also known as Ribonuclease H2 Subunit B, and the encoded protein is a nuclease subunit. SNX20 described in this article is a protein-coding gene, also known as Sorting Nexin 20, and the encoded protein is involved in cell vesicle transport. TBCD described in this article is a protein-coding gene, also known as Tubulin Folding Cofactor D, and the encoded protein is involved in tubulin folding. PIP5K1C described in this article is a protein-coding gene, also known as Phosphatidylinositol-4-Phosphate 5-Kinase Type 1 Gamma, and the encoded protein function is a phosphokinase. ZBTB7A described in this article is a protein-coding gene, also known as Zinc Finger And BTB Domain Containing 7A, and the encoded protein is a transcription factor that inhibits transcription initiation. DNASE2 described herein is a protein coding gene, also known as Deoxyribonuclease 2, Lysosomal, and the coding protein belongs to the DNA endonuclease family. TSHZ3 described herein is a protein coding gene, also known as Teashirt Zinc Finger Homeobox 3, and the coding protein is involved in transcriptional regulation. WISP2 described herein is a protein coding gene, also known as CCN5 or Cellular Communication Network Factor 5, and the coding protein belongs to the WISP protein family. In some embodiments, it is preferred to use and detect a combination of two or more (e.g., 3, 4, 5, or 6) of the target markers and their target gene regions in Table 1B. In some embodiments, the following combinations of target markers and their target gene regions in Table 1B are preferably used and detected: i) SKI, PRDM16, LZTS1, CCNA1, PIP5K1C and WISP2; or ii) PIAS3, CHD7, CACNA1B, ACVRL1, SNX20, TBCD and ZBTB7A. In some embodiments, the target marker SKI and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker PRDM16 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker PIAS3 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker SLC10A4 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker CXXC5 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker NR2E1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker MPC1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker HOXA13 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker LZTS1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker CHD7 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker ANKRD20A1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker CACNA1B and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker ACVRL1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker CCNA1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker RNASEH2B and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker SNX20 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker TBCD and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, PIP5K1C, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker PIP5K1C and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, ZBTB7A, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker ZBTB7A and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, DNASE2, TSHZ3 and WISP2. In some embodiments, the target marker DNASE2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, TSHZ3 and WISP2. In some embodiments, the target marker TSHZ3 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, and WISP2. In some embodiments, the target marker WISP2 and its target gene region are preferably used and detected, and optionally, target markers selected from the following and their target gene regions or any combination thereof are additionally used and detected: SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2 and TSHZ3. WRAP73 described herein is a protein coding gene, also known as WD Repeat Containing, Antisense To TP73, and the encoded protein belongs to a member of the WD repeat protein family. C2CD4D described herein is a protein coding gene, also known as C2 Calcium Dependent Domain Containing 4D. CCDC181 described herein is a protein-coding gene, also known as Coiled-Coil Domain Containing 181. RNF144A described herein is a protein-coding gene, also known as Ring Finger Protein 144A, and the encoded protein belongs to the zinc finger protein family. SIX2 described herein is a protein-coding gene, also known as SIX Homeobox 2, and the encoded protein is a transcription factor. NRXN1 described herein is a protein-coding gene, also known as Neurexin 1, and the encoded protein is a cell surface protein. MEIS1 described herein is a protein-coding gene, also known as Meis Homeobox 1, and the encoded protein is a transcriptional regulator of PAX6. LBX2 described herein is a protein-coding gene, also known as Ladybird Homeobox 2, and the encoded protein is a transcriptional regulatory factor. AMT described herein is a protein-coding gene, also known as Aminomethyltransferase, and the encoded protein is involved in glycine degradation. ITIH4 described in this article is a protein-coding gene, also known as Inter-Alpha-Trypsin Inhibitor Heavy Chain 4, and the encoded protein is a secretory protein. TRH described in this article is a protein-coding gene, also known as Thyrotropin Releasing Hormone, and the encoded protein belongs to the thyrotropin-releasing hormone family. SHOX2 described in this article is a protein-coding gene, also known as Short Stature Homeobox 2, and the encoded protein is a transcription factor. DGKG described in this article is a protein-coding gene, also known as Diacylglycerol Kinase Gamma, and the encoded protein belongs to the diacylglycerol kinase family. RPL9 described in this article is a protein-coding gene, also known as Ribosomal Protein L9, and the encoded protein participates in the formation of ribosomal subunits. PFN3 described in this article is a protein-coding gene, also known as Profilin 3, and the encoded protein belongs to the actin family. FOXC1 described in this article is a protein-coding gene, also known as Forkhead Box C1, and the encoded protein is a transcription factor. LY86 described in this article is a protein-coding gene, also known as Lymphocyte Antigen 86, and the encoded protein is involved in the immune response signaling pathway. SLC35F1 described in this article is a protein-coding gene, also known as Solute Carrier Family 35 Member F1, and the encoded protein is a member of the ion transport family. LRRC4 described in this article is a protein-coding gene, also known as Leucine Rich Repeat Containing 4, and the encoded protein is a synaptic adhesion protein. PDLIM2 described in this article is a protein-coding gene, also known as PDZ And LIM Domain 2, and the encoded protein function is related to cell adhesion. PAX2 described in this article is a protein-coding gene, also known as Paired Box 2, and the encoded protein is a transcription factor. MVK described in this article is a protein-coding gene, also known as Mevalonate Kinase, and the encoded protein is mevalonate kinase. DTX1 described in this article is a protein-coding gene, also known as Deltex E3 Ubiquitin Ligase 1, and the encoded protein is a ubiquitin ligase. RBM19 described in this article is a protein-coding gene, also known as RNA Binding Motif Protein 19. GCH1 described in this article is a protein-coding gene, also known as GTP Cyclohydrolase 1, and the encoded protein belongs to the GTP cyclohydrolase family. OTX2 described in this article is a protein-coding gene, also known as Orthodenticle Homeobox 2, and the encoded protein is a transcription factor. ZSCAN10 described in this article is a protein-coding gene, also known as Zinc Finger And SCAN Domain Containing 10, and the encoded protein is a transcription factor. AHSP described in this article is a protein-coding gene, also known as Alpha Hemoglobin Stabilizing Protein. NLRC5 described in this article is a protein-coding gene, also known as NLR Family CARD Domain Containing 5, and the encoded protein is involved in regulating the immune system. ASXL3 described in this article is a protein-coding gene, also known as ASXL Transcriptional Regulator 3, and the encoded protein is involved in regulating gene transcription. TCF4 described herein is a protein coding gene, also known as Transcription Factor 4, and the coding protein is a transcription factor. PLIN3 described herein is a protein coding gene, also known as Perilipin 3. RASAL3 described herein is a protein coding gene, also known as RAS Protein Activator Like 3, and the coding protein is a member of the RasGAP family. CHRNA4 described herein is a protein coding gene, also known as Cholinergic Receptor Nicotinic Alpha 4 Subunit, and the coding protein is a nicotinic acetylcholine receptor subunit. In some embodiments, it is preferred to use and detect a combination of two or more (e.g., 3, 4, 5, or 6) of the target markers and their target gene regions in Table 1C. In some embodiments, the following combinations of target markers and their target gene regions in Table 1C are preferably used and detected: i) ITIH4, FOXC1, PDLIM2, MVK, NLRC5, TCF4 and PLIN3; or ii) RNF144A, SIX2, DGKG, RPL9, LRRC4 and ZSCAN10. In some embodiments, the target marker WRAP73 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker C2CD4D and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker CCDC181 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker RNF144A and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker SIX2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker NRXN1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker MEIS1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker LBX2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker AMT and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker ITIH4 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker TRH and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker SHOX2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker DGKG and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker RPL9 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker PFN3 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker FOXC1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker LY86 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker SLC35F1 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker LRRC4 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker PDLIM2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker PAX2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker MVK and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker DTX1 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker RBM19 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker GCH1 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker OTX2 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker ZSCAN10 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker AHSP and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, NLRC5, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker NLRC5 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, ASXL3, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker ASXL3 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, TCF4, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker TCF4 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, PLIN3, RASAL3 and CHRNA4. In some embodiments, the target marker PLIN3 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, RASAL3 and CHRNA4. In some embodiments, the target marker RASAL3 and its target gene region are preferably used and detected, and optionally a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3 and CHRNA4. In some embodiments, the target marker CHRNA4 and its target gene region are preferably used and detected, and optionally, a target marker selected from the following and its target gene region or any combination thereof is additionally used and detected: WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3 and RASAL3. In some embodiments, the use of the target markers of the present invention and their target gene regions or combinations thereof can achieve a sensitivity of at least 25%, such as at least 30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 81%, at least 82% or at least 83% with a specificity of greater than 80%, such as greater than 85% or greater than 90%. The terms "subject", "patient" and "individual" are used interchangeably herein and refer to warm-blooded animals, such as mammals. The term includes but is not limited to livestock, rodents (such as rats and mice), primates and humans. Preferably, the term refers to humans. The term "methylation assay" refers to any assay that determines the methylation state of one or more dinucleotide (e.g., CpG) sequences within a DNA sequence. In this article, the term "threshold" should be understood according to the general understanding of those skilled in the art, and represents any useful reference for reflecting the level of DNA methylation. In some embodiments, the threshold is represented by a positive reference interval, wherein within the positive reference interval, it is indicated that the individual suffers from breast cancer or has a risk of breast cancer; for example, compared with the corresponding positive reference interval, the methylation level of one or more markers within the positive reference interval indicates that the individual suffers from breast cancer or has a risk of breast cancer. The threshold or positive reference interval can be obtained from a known database or personal study. In the present invention, the threshold or positive reference interval refers to the level from a positive control (i.e., an individual suffering from breast cancer). The threshold or positive reference interval can be obtained from the patient's own blood reference sample; the expression of marker genes of individuals with breast cancer; or breast cancer cells of individuals with breast cancer who are predetermined to have breast cancer. In some embodiments, when a machine learning model (including but not limited to a logical regression model) is used to determine breast cancer, the AUC value of the ROC curve is used to set the positive reference interval, for example, the AUC value of each marker greater than 90% specificity (the proportion of samples without breast cancer detected as positive is less than 10%) is used to set the positive reference interval. In some embodiments in which the 22 markers of group (1) above are used to construct a logical regression model, the AUC threshold is set to, for example, equal to or greater than 0.362. In some embodiments of constructing a logical regression model using TTLL10, FAM150B, BEND6, ELN, TMEM179, and MYO15B markers, the AUC threshold is set to, for example, equal to or greater than 0.609. In some embodiments of constructing a logical regression model using EPS8L3, IRF2BP2, TERT, TH, CARKD, SPNS1, and PSG8 markers, the AUC threshold is set to, for example, equal to or greater than 0.604. In some embodiments of constructing a logical regression model using a single CARKD marker, the AUC threshold is set to, for example, equal to or greater than 0.532. In some embodiments of constructing a logical regression model using a single TTLL10 marker, the AUC threshold is set to, for example, equal to or greater than 0.533. In some embodiments where a single EPS8L3 marker is used to construct a logistic regression model, the AUC threshold is set to, for example, equal to or greater than 0.533. In some embodiments where a single IRF2BP2 marker is used to construct a logistic regression model, the AUC threshold is set to, for example, equal to or greater than 0.533. In some embodiments where a single FAM150B marker is used to construct a logistic regression model, the AUC threshold is set to, for example, equal to or greater than 0.532. In some embodiments where a single ID2 marker is used to construct a logistic regression model, the AUC threshold is set to, for example, equal to or greater than 0.532. In some embodiments where a single TERT marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single PITX1 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single KCNMB1 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single BEND6 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single ELN marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single CPXM2 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.531. In some embodiments where a single TH marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single C1QTNF9 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single TMEM179 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single SPNS1 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single MYO15B marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single DNM2 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single EPHX3 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single PSG8 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single SLCO4A1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.537. In some embodiments where a single TNFRSF6B marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.531. In some embodiments, when a machine learning model (including but not limited to a logical regression model) is used to determine breast cancer, the AUC value of the ROC curve is used to set a positive reference interval, for example, the AUC value of each marker greater than 90% specificity (the proportion of samples without breast cancer detected as positive is less than 10%) is used to set a positive reference interval. In some embodiments in which the 22 markers of the above group (2) are used to construct a logical regression model, the AUC threshold is set to, for example, equal to or greater than 0.440. In some embodiments in which the SKI, PRDM16, LZTS1, CCNA1, PIP5K1C, and WISP2 markers are used to construct a logical regression model, the AUC threshold is set to, for example, equal to or greater than 0.541. In some embodiments where the PIAS3, CHD7, CACNA1B, ACVRL1, SNX20, TBCD, and ZBTB7A markers are used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.513. In some embodiments where the SKI marker alone is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where the PRDM16 marker alone is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where the PIAS3 marker alone is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single SLC10A4 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single CXXC5 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single NR2E1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single MPC1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.540. In some embodiments where a single HOXA13 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single LZTS1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single CHD7 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single ANKRD20A1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.530. In some embodiments where a single CACNA1B marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single ACVRL1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.530. In some embodiments where a single CCNA1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single RNASEH2B marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single SNX20 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.548. In some embodiments where a single TBCD marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single PIP5K1C marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single ZBTB7A marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.529. In some embodiments where a single DNASE2 marker is used to construct a logical regression model, the AUC threshold is set to, for example, be equal to or greater than 0.533. In some embodiments where a single TSHZ3 marker is used to construct a logical regression model, the AUC threshold is set to, for example, be equal to or greater than 0.532. In some embodiments where a single WISP2 marker is used to construct a logical regression model, the AUC threshold is set to, for example, be equal to or greater than 0.532. In some embodiments, when a machine learning model (including but not limited to a logical regression model) is used to determine breast cancer, the AUC value of the ROC curve is used to set a positive reference interval, for example, the AUC value of each marker greater than 90% specificity (the proportion of samples without breast cancer detected as positive is less than 10%) is used to set a positive reference interval. In some embodiments in which the 35 markers of the above group (3) are used to construct a logical regression model, the AUC threshold is set to, for example, equal to or greater than 0.505. In some embodiments in which the ITIH4, FOXC1, PDLIM2, MVK, NLRC5, TCF4, and PLIN3 markers are used to construct a logical regression model, the AUC threshold is set to, for example, equal to or greater than 0.445. In some embodiments of constructing a logical regression model using RNF144A, SIX2, DGKG, RPL9, LRRC4, and ZSCAN10 markers, the AUC threshold is set to, for example, equal to or greater than 0.447. In some embodiments of constructing a logical regression model using a single WRAP73 marker, the AUC threshold is set to, for example, equal to or greater than 0.533. In some embodiments of constructing a logical regression model using a single C2CD4D marker, the AUC threshold is set to, for example, equal to or greater than 0.533. In some embodiments of constructing a logical regression model using a single CCDC181 marker, the AUC threshold is set to, for example, equal to or greater than 0.533. In some embodiments where a single RNF144A marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single SIX2 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single NRXN1 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a single MEIS1 marker is used to construct a logical regression model, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a logistic regression model is constructed using a single LBX2 marker, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a logistic regression model is constructed using a single AMT marker, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a logistic regression model is constructed using a single ITIH4 marker, the AUC threshold is set, for example, to be equal to or greater than 0.529. In some embodiments where a logistic regression model is constructed using a single ITIH4 marker, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a logistic regression model is constructed using a single TRH marker, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single SHOX2 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single DGKG marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.534. In some embodiments where a single RPL9 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.531. In some embodiments where a single PFN3 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.535. In some embodiments where a single FOXC1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single LY86 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single SLC35F1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single LRRC4 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a logical regression model is constructed using a single PDLIM2 marker, the AUC threshold is set to, for example, be equal to or greater than 0.533. In some embodiments where a logical regression model is constructed using a single PAX2 marker, the AUC threshold is set to, for example, be equal to or greater than 0.533. In some embodiments where a logical regression model is constructed using a single MVK marker, the AUC threshold is set to, for example, be equal to or greater than 0.533. In some embodiments where a logical regression model is constructed using a single DTX1 marker, the AUC threshold is set to, for example, be equal to or greater than 0.535. In some embodiments where a logical regression model is constructed using a single RBM19 marker, the AUC threshold is set to, for example, be equal to or greater than 0.533. In some embodiments where a single GCH1 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single OTX2 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single ZSCAN10 marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a single AHSP marker is used to construct a logistic regression model, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a logistic regression model is constructed using a single NLRC5 marker, the AUC threshold is set, for example, to be equal to or greater than 0.531. In some embodiments where a logistic regression model is constructed using a single ASXL3 marker, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments where a logistic regression model is constructed using a single TCF4 marker, the AUC threshold is set, for example, to be equal to or greater than 0.532. In some embodiments where a logistic regression model is constructed using a single PLIN3 marker, the AUC threshold is set, for example, to be equal to or greater than 0.531. In some embodiments where a logistic regression model is constructed using a single RASAL3 marker, the AUC threshold is set, for example, to be equal to or greater than 0.533. In some embodiments of constructing a logical regression model using a single CHRNA4 marker, the AUC threshold is set to, for example, equal to or greater than 0.532. The term "oligonucleotide" refers to a polymer form of nucleotides of any length, which is a ribonucleotide or a deoxyribonucleotide. The term includes double-stranded and single-stranded DNA and RNA, such as modified and unmodified forms of methylated or capped polynucleotides. The terms "polynucleotide" and "oligonucleotide" are used interchangeably herein. Oligonucleotides may but need not include other coding or non-coding sequences, or they may but need not be connected to other molecules and/or carriers or supporting materials. Oligonucleotides used in the methods or kits of the present invention may have any length suitable for a specific method. In some applications, the term refers to an antisense nucleic acid molecule (e.g., an mRNA or DNA chain in the opposite direction of a sense polynucleotide encoding a marker of the present invention). Oligonucleotides used in the present invention include complementary nucleic acid sequences and nucleic acids substantially identical to these sequences, and also include sequences that differ from nucleic acid sequences due to genetic code degeneracy. Oligonucleotides that can be used in the present invention also include nucleic acids that hybridize with oligonucleotide cancer marker nucleic acid sequences under stringent conditions, preferably highly stringent conditions. Nucleotide hybridization assays are well known in the art. Hybridization assay procedures and conditions will vary depending on the application and are selected based on known general binding methods, see, for example, J. Sambrook et al., Molecular Cloning: A Laboratory Manual (3rd Edition. Science Press, 2002); and Young and Davis, P.N.A.S, 80: 1194 (1983). Methods and apparatus for performing repeated and controlled hybridization reactions have been described in U.S. Patent Nos. 5,871,928, 5,874,219, 6,045,996, 6,386,749, and 6,391,623, each of which is incorporated herein by reference. As used herein, "primer" generally refers to a linear oligonucleotide that is complementary to and anneals to a target sequence. The lower limit of primer length is determined by hybridization ability, because very short primers (e.g., less than 5 nucleotides) do not form thermodynamically stable duplexes under most hybridization conditions. Primer lengths generally vary from 8-50 nucleotides. In certain embodiments, primers are between about 15-25 nucleotides. Naturally occurring nucleotides (especially guanine, adenine, cytosine and thymine, hereinafter referred to as "G", "A", "C" and "T"), as well as nucleotide analogs, can be used in the primers of the present invention. "Amplification product" as used herein refers to an amplified nucleic acid produced from a nucleic acid template by nucleic acid amplification. The term "nucleotide analog" as used herein refers to a compound that is structurally similar to a naturally occurring nucleotide. Nucleotide analogs may have altered phosphate backbones, sugar moieties, nucleobases or combinations thereof. Nucleotide analogs with altered nucleobases typically impart different base pairing and base stacking properties, among others. Nucleotide analogs with altered phosphate-sugar backbones (e.g., peptide nucleic acids (PNA), locked nucleic acids (LNA)) typically alter chain properties, such as secondary structure formation, among others. Examples of primers and probes used in the present invention are shown in Table 2A, Table 2B and Table 2C, and the target gene regions they target are shown in Table 1A, Table 1B and Table 1C. The nucleotide sequences of the primers and probes of the present invention also include modified forms thereof, as long as the amplification or detection effect of the primers is not significantly affected. The modification may be, for example, adding one or more nucleotide residues in the nucleotide sequence or at both ends, deleting one or more nucleotide residues in the nucleotide sequence, or replacing one or more nucleotide residues in the sequence with other nucleotide residues, such as replacing A with T, replacing C with G, etc. It is clear to those skilled in the art that the modified form of the primer is also covered within the scope of protection of the present invention, especially within the scope of the patent application. In one embodiment, the modified form of the nucleotide sequence of the primer is a chemically enhanced primer as disclosed in CN103270174A. Each nucleotide in the primer of the present invention can be synthesized by chemical methods using, for example, a universal DNA synthesizer (e.g., Model 394 manufactured by Applied Biosystems). Oligonucleotides may also be synthesized by any other method known in the art. DNA extracted from a sample is used as a template, and a PCR primer is used to amplify the target marker to obtain an amplification product. Amplification reactions include, but are not limited to, polymerase chain reaction (PCR), ligase chain reaction (LCP), self-sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), chain displacement amplification (SDA), multiple displacement amplification (MDA) and circular amplification (RCA), which are disclosed in the following references (incorporated herein by reference): Mullis et al., U.S. Patent Nos. 4,683,195; 4,965,188; 4,683,202; 4,800,159 (PCR); Gelfand et al., U.S. Patent No. 5,210,015 (referred to as "Taqman" or "Taq" [Registered Trademark] Real-time PCR with probe); Wittwer et al., U.S. Patent No. 6,174,670; Kacian et al., U.S. Patent No. 5,399,491 ("NASBA"); Lizardi, U.S. Patent No. 5,854,033; Aono et al., Japanese Patent Publication No. JP 4-262799 (Rolling Circle Amplification); etc. The target marker is preferably amplified using PCR. The PCR method itself is well known in the art. The term "PCR" includes derivative forms of the reaction, including but not limited to reverse transcription PCR, real-time PCR, embedded mode PCR, multiplex PCR and fluorescent quantitative PCR. It is preferred to use fluorescent quantitative PCR to quantitatively amplify the target nucleotide. In the presence of primers, template DNA and thermostable DNA polymerase, a primer hybridized with the sense strand (reverse primer) and a primer hybridized with the antisense strand (forward primer) are used to perform PCR by repeating the cycle of denaturation, annealing and extension steps for about 30 to 60 times (e.g., 50 times). In one embodiment, PCR is fluorescent quantitative PCR. In one embodiment, PCR uses the primers shown in Table 2. It can be understood by those skilled in the art that other PCR methods and primers can also be used as long as the target fragment can be amplified. In the PCR of the present invention, various conventional thermostable DNA polymerases can be used for amplification, including but not limited to FastStart Taq DNA polymerase (Roche), Ex Taq (registered trademark, Takara), Z-Taq, AccuPrime Taq DNA polymerase and HotStarTaq Plus DNA polymerase. The method of selecting suitable PCR reaction conditions based on the primer Tm value is well known in the art, and ordinary technicians in the art can select the best conditions based on primer length, GC content, target specificity and sensitivity, the properties of the polymerase used, etc. For example, the following conditions can be used for fluorescent quantitative PCR reaction: 95°C for 5 minutes; 95°C for 15 seconds, 56°C for 40 seconds, and 50 cycles. The reaction system is 25 μL. Reagents that can be used to detect the methylation level of the target marker of the present invention are well known in the art. Such reagents suitable for the present invention, such as bisulfite reagents or methylation-sensitive restriction enzymes, can be purchased commercially or routinely prepared by methods well known to technicians in the field. The term "bisulfite reagent" refers to bisulfite used to distinguish between methylated and unmethylated CpG dinucleotide sequences. The term "methylation-sensitive restriction enzyme" should be understood as an enzyme that selectively digests nucleic acids based on the methylation status of its recognition site. For restriction enzymes that specifically cleave when the recognition site is unmethylated or hemimethylated, when the recognition site is methylated, cleavage does not occur, or cleavage occurs with significantly reduced efficiency. For restriction enzymes that specifically cleave when the recognition site is methylated, when the recognition site is unmethylated, cleavage does not occur, or cleavage occurs with significantly reduced efficiency. The following methylation-sensitive restriction enzymes are preferred, whose recognition sequences contain CG dinucleotides (e.g., cgcg or cccggg). In some embodiments, it is further preferred that the restriction enzyme does not cut when the cytosine in the dinucleotide is methylated at the C5 carbon atom. The kit of the present invention can be prepared by conventional methods in the art. The kit may contain materials or reagents used to implement the method of the present invention (including reagents for detecting each target marker). The kit may include storage reaction reagents (such as primers, dNTPs, enzymes, etc. in suitable containers) and/or support materials (such as buffers, instructions for performing the detection, etc.). For example, the kit may include one or more containers (such as boxes) containing corresponding reaction reagents and/or support materials. Such contents can be delivered to a predetermined recipient together or separately. As an example, the kit may contain reagents, buffers, and instructions for use for detecting each target marker. The kit may also contain polymerases and dTNPs, etc. The kit may also contain internal standards, positive and negative controls, etc. for quality control. The kit may also contain reagents for preparing nucleic acids, such as DNA, from samples. The above examples should not be construed as limiting the kits and their contents applicable to the present invention. A microarray refers to a solid support having a flat surface, which has an array of nucleic acids, each member of the array comprising an identical copy of an oligonucleotide or polynucleotide fixed to a spatially defined region or site, which does not overlap with the regions or sites of other members in the array; that is, the region or site is spatially discrete. In addition, a spatially defined hybrid site can be "addressable" because its position and the identity of its immobilized oligonucleotide are known or predetermined (e.g., known or predetermined before its use). Typically, the oligonucleotide or polynucleotide is a single strand and is covalently linked to a solid support, usually at the 5'-end or the 3'-end. The density of nucleic acids containing non-overlapping regions in the microarray is usually greater than 100/cm ², preferably greater than 1000/cm ². Microarray technology is disclosed in, for example, the following references: Microarrays: A Practical Approach (IRL Press, Oxford, 2000), edited by Schena; Southern, Current Opin. Chem. Biol., 2:404-410, 1998, all of which are incorporated herein by reference. The present invention discloses the use of markers in diagnosing breast cancer and predicting its risk. A person skilled in the art can refer to the contents of this article and appropriately improve the process parameters to achieve the purpose. It is particularly important to point out that all similar substitutions and modifications are obvious to a person skilled in the art, and they are all considered to be included in the present invention. The uses described in this invention have been described through preferred embodiments. Relevant personnel can obviously modify or appropriately change and combine the uses described in this article without departing from the content, spirit and scope of this invention to realize and apply the technology of this invention. EmbodimentIn order to more clearly understand the content of the present invention, it will be described in detail with reference to the attached drawings and embodiments. Embodiment 1: Methylation targeted sequencing to screen for breast cancer methylation markers in plasmaThe inventor collected a total of 132 female samples, including 70 female breast cancer patients and 62 healthy women. All participants signed informed consent. These samples were divided into training sets and test sets according to a certain ratio. The training set was used to build the following machine learning model, and the test set was used to test the performance of the model. The sample information is shown in Table 3 below. This application obtains the methylation sequencing data of sample plasma cfDNA by the method of Methyl-Titan (China Patent No. CN201910515830) and screens out the methylation markers therein. The specific technical scheme is as follows: 1. Extraction of plasma cfDNA samples A streck blood collection tube is used to collect 2ml whole blood samples from volunteers. The sample information of the volunteers included in the group is shown in Table 3. The plasma is centrifuged and separated in time (within 3 days). After being transferred to the laboratory, the QIAGEN QIAamp Circulating Nucleic Acid Kit is used to extract cfDNA according to the instructions. 2. Sequencing and data preprocessing a) The library is sequenced with 150bp double-end using the Illumina Nextseq 500 sequencer, and the sequencing amount is not less than 5M. b)  Pear (v0.6.0) software merged the double-end sequencing data of the same fragment of 150bp sequencing off the sequencer into one sequence, with the shortest overlap length of 20 bp and the shortest length after merging of 30bp. c)  Trim_galore v 0.6.0 and cutadapt v1.8.1 software were used to remove the junction of the merged sequencing data. The junction sequence was "AGATCGGAAGAGCAC" and the bases with sequencing quality values less than 20 at both ends were removed. 3. Sequencing data alignment The reference genome data used in this article comes from the UCSC database (UCSC: HG19, http://hgdownload.soe.ucsc.edu/goldenPath/ hg19/ bigZips/hg19.fa.gz). a)  Use Bismark software to convert HG19 genome sequences from cytosine to thymine (CT) and adenine to guanine (GA), and use Bowtie2 software to build indexes for the converted genomes. b)  Perform CT and GA conversion on the preprocessed data in the same way. c)  Use Bowtie2 software to align the converted sequences to the converted HG19 reference genome, with a minimum seed sequence length of 20 and no mismatches in the seed sequence. 4. Calculation of AMF and MHF for each sample Based on the above comparison results, obtain the methylation status corresponding to each CpG site in each target methylation interval. a)  Calculate the average methylation rate AMF value of the target methylation interval. The calculation formula for AMF is as follows: Where M is the total number of CpG sites in the target methylation interval, i is the CpG site in the interval, and N _C,iis the number of reads sequenced as C at the CpG site (i.e., the number of methylation reads), N _T,iis the number of reads with T sequenced at the CpG site (i.e., the number of unmethylated sequenced reads). b) Calculate the MHF value of the target methylation interval methylation haplotype rate. A target methylation interval may have multiple methylation haplotypes. This value needs to be calculated for each methylation haplotype in the target region. The calculation formula of MHF is as follows: Where l represents the target methylation interval, h represents the target methylation haplotype, N _lIndicates the number of reads located in the target methylation region, N _l,hIndicates the number of reads containing the target methylation haplotype 5. Feature matrix construction a) Merge the AMF and MHF values of each target methylation interval of each sample in the training set and the test set into the training set and the test set feature matrix, and treat the target methylation interval with less than 100 reads as missing values. b) Remove the target methylation interval with a missing value ratio higher than 10%. c) Use the KNN algorithm to train the transformer for the training set matrix, and use the transformer to interpolate missing data for the training set and the test set feature matrix. 6. Search for breast cancer methylation markers based on training set samples (see Figure 1) a) In the training set, construct a logical regression model for each feature to distinguish breast cancer from healthy people, calculate the average AUC of 3-fold cross-validation, and sort from high to low b) Add the remaining features to the feature set in turn, and reconstruct the logical regression model c) If the average AUC of the 5-fold cross-validation of the logical regression model increases, retain the feature, otherwise remove it d) After traversing all features, obtain the optimal marker combination, use the optimal combination to build a model, and finally use the test set samples to verify the effect of the model. 7. A total of 79 breast cancer methylation markers were screened in the above process. The above process screened out 79 breast cancer methylation markers (22 in group (1), 22 in group (2), and 35 in group (3), among which a single methylation marker or a combination of multiple methylation markers can be used as methylation markers for breast cancer identification. No. (1) GroupThe methylation marker-associated gene refers to the gene corresponding to the nearest TSS within 100Kb of the methylation marker. The specific associated genes and methylation levels are shown in Table 4A. The methylation levels of the 22 methylation markers in the training set and test set breast cancer samples and healthy human samples are shown in Figure 2A and Table 4A. The methylation marker-associated gene refers to the gene corresponding to the nearest TSS within 100Kb of the methylation marker. The methylation marker genomic position refers to the methylation marker in the UCSC (https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19) HG19 genomic position. For the training set and test set samples of healthy people and breast cancer patients, the methylation level of each sample methylation marker was calculated respectively, and the median of the category was calculated as the methylation level of the category. The statistical significance of the difference in methylation between healthy people and breast cancer patients in the training set and test set was calculated using 'Wilcox.test'. If the Wilcox.P value is <0.05, it is considered that the methylation marker has a significant methylation difference between healthy people and breast cancer patients. Among the 22 methylation markers, 19 methylation markers have significant methylation differences in the training set samples, and 14 have significant methylation differences in the test set samples. These results show that the 22 methylation markers we screened can also better distinguish healthy people and breast cancer patients in terms of sample methylation levels. We use Seq ID NO:14 to show the methylation level of this methylation marker in the training set and test set of breast cancer and healthy people in detail, as shown in Table 4A below. This methylation marker has extremely significant methylation differences in the training set and test set of breast cancer and healthy people, with the training set Wilcox.P value being 1.2E-11 and the test set Wilcox.P value being 1.0E-08. No. (2) GroupThe methylation marker-associated gene refers to the gene corresponding to the nearest TSS within 100Kb of the methylation marker. The specific associated genes and methylation levels are shown in Table 4     B. The methylation levels of the 22 methylation markers in the training set and test set breast cancer samples and healthy human samples are shown in Figure 1 and Table 2B. The methylation marker-associated gene refers to the gene corresponding to the nearest TSS within 100Kb of the methylation marker. The methylation marker genomic position refers to the methylation marker in the UCSC (https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19) HG19 genomic position. For the training set and test set samples of healthy people and breast cancer patients, the methylation level of each sample methylation marker was calculated respectively, and the median of the category was calculated as the methylation level of the category. The statistical significance of the difference in methylation between healthy people and breast cancer patients in the training set and test set was calculated using 'Wilcox.test'. If the Wilcox.P value is <0.05, it is considered that the methylation marker has a significant methylation difference between healthy people and breast cancer patients. Among the 22 methylation markers, 19 methylation markers have significant methylation differences in the training set samples, and 13 have significant methylation differences in the test set samples. These results show that the 22 methylation markers we screened can also better distinguish healthy people and breast cancer patients in terms of sample methylation levels. We use Seq ID NO:75 to show the methylation level of this methylation marker in the training set and test set of breast cancer and healthy people in detail, as shown in Table 4B below. This methylation marker has extremely significant methylation differences in the training set and test set of breast cancer and healthy people, with the training set Wilcox.P value of 9.6E-12 and the test set Wilcox.P value of 2.5E-03. No. (3) GroupThe methylation marker-associated gene refers to the gene corresponding to the nearest TSS within 100Kb of the methylation marker. The specific associated genes and methylation levels are shown in Table 4    C. The methylation levels of the 35 methylation markers in the training set and test set breast cancer samples and healthy human samples of Group (3) are shown in Figure 1 and Table 4C. The methylation marker genomic position refers to the methylation marker position in the UCSC (https://genome.ucsc.edu/cgi-bin/hgTracks?db=hg19) HG19 genome. The methylation marker-associated gene refers to the gene whose TSS is within 100Kb of the methylation marker and is the closest to it. For the samples of healthy people and breast cancer patients in the training set and the test set, the methylation level of each sample methylation marker was calculated respectively, and the median of the category was calculated as the methylation level of the category. The statistical significance of the difference in methylation between healthy people and breast cancer patients in the training set and the test set was calculated using 'Wilcox.test'. If the P value is <0.05, it is considered that the methylation marker has a significant methylation difference between healthy people and breast cancer patients. Among the 35 methylation markers, 28 methylation markers have significant methylation differences in the training set samples, and 18 have significant methylation differences in the test set samples. These results show that the methylation markers we screened can also better distinguish healthy people and breast cancer patients in terms of sample methylation levels. We use Seq ID NO:152 as an example to show the methylation level of this methylation marker in the training set and test set of breast cancer and healthy people in detail, as shown in Table 4C below. This methylation marker has extremely significant methylation differences between breast cancer and healthy people in both the training set and the test set. The P value of the training set ‘Wilcox.test’ is 6.0E-7, and the P value of the test set ‘Wilcox.test’ is 7.8E-04. Embodiment 2 ：Machine learning diagnostic model for all methylation markers AllModelThis embodiment uses 79 methylation markers to construct a logical regression machine learning model to identify plasma samples of healthy people and breast cancer patients. No. (1) GroupThe methylation levels of 22 methylation markers in the training set samples in Example 1 were used for model training, and then the effect of the model was tested using the samples in the test set. The specific steps are as follows: 1. Use the logical regression model in the sklearn (V1.0.1) package in python (V3.9.7): AllModel = LogisticRegression() 2. Use the samples in the training set for training: AllModel.fit (Traindata, TrainPheno), where TrainData is the data of the training set, TrainPheno is the trait of the training set samples (breast cancer is 1, healthy people are 0), and determine the relevant threshold of the model based on the samples in the training set. 3. Test the samples in the test set: TestPred = AllModel.predict_proba(TestData)[:, 1], where TestData is the test set data, TestPred is the model prediction score, and the prediction score is used to judge whether the sample is breast cancer based on the above threshold. The distribution of model prediction scores in the training set and test set is shown in Figure 4A. It can be seen from the figure that the model scores of breast cancer and healthy people samples are significantly different. The ROC curve is shown in Figure 5A. In the training set, the AUC of the breast cancer and healthy people discrimination model is 0.992, and the AUC of the test set is 0.935. According to the training set data, the threshold is set to 0.362. If it is greater than this value, it is breast cancer, otherwise it is a healthy person. Under this threshold, the test set accuracy is 0.825, specificity is 0.737, and sensitivity is 0.905. See Table 5A for specific data. This model can better distinguish breast cancer plasma samples from healthy people's plasma samples and can be used for early screening of breast cancer. No. (2) GroupThe methylation levels of 22 methylation markers in the training set samples in Example 1 were used for model training, and then the effect of the model was tested using the samples in the test set. The specific steps are as follows: 1. Use the logical regression model in the sklearn (V1.0.1) package in python (V3.9.7): AllModel = LogisticRegression() 2. Use the samples in the training set for training: AllModel.fit (Traindata, TrainPheno), where TrainData is the data of the training set, TrainPheno is the trait of the training set samples (breast cancer is 1, healthy people are 0), and determine the relevant threshold of the model based on the samples in the training set. 3. Test the samples in the test set: TestPred = AllModel.predict_proba(TestData)[:, 1], where TestData is the test set data, TestPred is the model prediction score, and the prediction score is used to judge whether the sample is breast cancer based on the above threshold. The distribution of model prediction scores in the training set and test set is shown in Figure 4B. It can be seen from the figure that the model scores of breast cancer and healthy people samples are significantly different. The ROC curve is shown in Figure 5B. In the training set, the AUC of the breast cancer and healthy people distinction model is 0.995, and the AUC of the test set is 0.962. According to the training set data, the threshold is set to 0.440. If it is greater than this value, it is breast cancer, otherwise it is a healthy person. Under this threshold, the test set accuracy is 0.900, the specificity is 0.842, and the sensitivity is 0.952. See Table 5B for specific data. This model can better distinguish breast cancer plasma samples from healthy people's plasma samples and can be used for early screening of breast cancer. No. (3) GroupThe methylation levels of 35 methylation markers in the training set samples in Example 1 were used for model training, and then the effect of the model was tested using the samples in the test set. The specific steps are as follows: 4. Use the logical regression model in the sklearn (V1.0.1) package in python (V3.9.7): AllModel = LogisticRegression() 5. Use the samples in the training set for training: AllModel.fit (Traindata, TrainPheno), where TrainData is the data of the training set, TrainPheno is the trait of the training set samples (breast cancer is 1, healthy people are 0), and determine the relevant threshold of the model based on the samples in the training set. 6. Test the samples in the test set: TestPred = AllModel.predict_proba(TestData)[:, 1], where TestData is the test set data, TestPred is the model prediction score, and the prediction score is used to judge whether the sample is breast cancer based on the above threshold. The distribution of model prediction scores in the training set and test set is shown in Figure 4C. It can be seen from the figure that the model scores of breast cancer and healthy people samples are significantly different. The ROC curve is shown in Figure 5C. In the training set, the AUC of the breast cancer and healthy people distinction model is 0.975, and the AUC of the test set is 0.932. According to the training set data, the threshold is set to 0.505. A value greater than this value is breast cancer, otherwise it is a healthy person. At this threshold, the test set accuracy is 0.875, the specificity is 0.789, and the sensitivity is 0.952, see Table 5C. This model can effectively distinguish breast cancer plasma samples from healthy people's plasma samples and can be used for early screening of breast cancer. Embodiment 3 : Random methylation marker combination 1 Machine Learning Diagnostic Models Sub1 No. (1) GroupIn order to verify the effect of the random methylation marker combination, this embodiment selected 6 methylation markers, Seq ID NO:1, Seq ID NO:4, Seq ID NO:9, Seq ID NO:10, Seq ID NO:15, Seq ID NO:17, from all 22 methylation markers to construct a new machine learning model Sub1. The method of constructing the machine learning model is the same as that of Example 2, but only 6 methylation markers in the random methylation marker combination 1 are selected. The model scores of the model in the training set and the test set are shown in Figure 6A, and the ROC curve of the model is shown in Figure 7A. It can be seen that in the training set and test set, the scores of breast cancer samples are significantly different from those of healthy people. The AUC of the training set is 0.944, and the AUC of the test set is 0.912. When the threshold is set to 0.609, the accuracy of the test set is 0.750, the specificity is 0.895, and the sensitivity is 0.619. The specific data are shown in Table 5A, which shows the good performance of the combined model. No. (2) GroupIn order to verify the effect of the random methylation marker combination, this embodiment selected 6 methylation markers, Seq ID NO:67, Seq ID NO:68, Seq ID NO:75, Seq ID NO:80, Seq ID NO:84, Seq ID NO:88, from all 22 methylation markers to construct a new machine learning model Sub1. The method of constructing the machine learning model is the same as that of Example 2, but only 6 methylation markers in the random methylation marker combination 1 are selected. The model scores of the model in the training set and the test set are shown in Figure 6B, and the ROC curve of the model is shown in Figure 7B. It can be seen that in the training set and test set of this model, the scores of breast cancer samples are significantly different from those of healthy people. The AUC of the training set of this model is 0.930, and the AUC of the test set is 0.867. When the threshold is set to 0.541, the accuracy of the test set is 0.775, the specificity is 0.842, and the sensitivity is 0.714. The specific data are shown in Table 5B, which shows the good performance of this combined model. No. (3) GroupIn order to verify the effect of the random methylation marker combination, this embodiment selected 7 methylation markers, including Seq ID NO:142, Seq ID NO:149, Seq ID NO:153, Seq ID NO:155, Seq ID NO:162, Seq ID NO:164, and Seq ID NO:165, from all 35 methylation markers to construct a new machine learning diagnosis model Sub1. The method for constructing the machine learning model is the same as that of Example 2, but only 7 methylation markers in the random methylation marker combination 1 are selected. The model scores of the model in the training set and the test set are shown in Figure 6C, and the ROC curve of the model is shown in Figure 7C. It can be seen that in the training set and test set of this model, the scores of breast cancer samples are significantly different from those of healthy people. The AUC of the training set of this model is 0.884, and the AUC of the test set is 0.847. When the threshold is set to 0.445, the accuracy of the test set is 0.775, the specificity is 0.737, and the sensitivity is 0.810, as shown in Table 5C, which shows the good performance of this model. Embodiment 4 : Random methylation marker combination 2 Machine Learning Diagnostic Models Sub2 No. (1) GroupThis embodiment uses another set of random methylation marker combinations: Seq ID NO:2, Seq ID NO:3, Seq ID NO:6, Seq ID NO:12, Seq ID NO:14, Seq ID NO:16, Seq ID NO:20, a total of 7 methylation markers to construct the machine learning model Sub2. The model construction method is also consistent with Example 2. The model scores of the model in the training set and the test set are shown in Figure 8A, and the ROC curve is shown in Figure 9A. It can be seen from the figure that in the training set and the test set, the breast cancer sample scores of the model are significantly higher than the healthy person scores. The model training set AUC is 0.935, and the test set AUC is 0.852. When the threshold is set to 0.604, the test set accuracy is 0.700, the specificity is 0.789, and the sensitivity is 0.619. The specific data are shown in Table 5A. It can also distinguish breast cancer from normal people. No. (2) GroupThis embodiment uses another set of random methylation marker combinations: Seq ID NO:69, Seq ID NO:76, Seq ID NO:78, Seq ID NO:79, Seq ID NO:82, Seq ID NO:83, Seq ID NO:85, a total of 7 methylation markers to construct the machine learning model Sub2. The model construction method is also consistent with Example 2. The model scores of the model in the training set and the test set are shown in Figure 8B, and the ROC curve is shown in Figure 9B. It can be seen from the figure that in the training set and the test set, the breast cancer sample scores of the model are significantly higher than the healthy person scores. The model training set AUC is 0.910, and the test set AUC is 0.875. When the threshold is set to 0.513, the test set accuracy is 0.850, the specificity is 0.789, and the sensitivity is 0.905. The specific data are shown in Table 5B. It can also better distinguish breast cancer from normal people. No. (3) GroupThis embodiment uses another set of random methylation marker combinations: Seq ID NO:136, Seq ID NO:137, Seq ID NO:146, Seq ID NO:147, Seq ID NO:152, Seq ID NO:160, a total of 6 methylation markers to construct the machine learning model Sub2. The model construction method is also consistent with Example 2. The model scores of the model in the training set and the test set are shown in Figure 8C, and the ROC curve is shown in Figure 9C. It can be seen from the figure that in the training set and the test set, the breast cancer sample scores of the model are significantly higher than the healthy person scores. The model training set AUC is 0.849, and the test set AUC is 0.865. When the threshold is set to 0.447, the test set accuracy is 0.800, the specificity is 0.632, and the sensitivity is 0.952, as shown in Table 5C. It can also better distinguish breast cancer from normal people. Embodiment 5 Effect of a single landmark No. (1) GroupThe inventors of this application found that the methylation level of a single methylation marker among the 22 methylation markers also has a good classification effect. The effect of a single methylation marker in distinguishing breast cancer from healthy people is shown in Table 6A. Taking Seq ID NO:14 as an example, if the methylation marker is used alone to construct a machine learning model, the model training set AUC is 0.880, and the test set AUC is 0.962. When the threshold is set to 0.532, the test set accuracy is 0.875, the specificity is 0.789, and the sensitivity is 0.952, and the classification effect is obvious. No. (2) GroupThe inventors of this application found that the methylation level of a single methylation marker among the 22 methylation markers also has a good classification effect. The effect of a single methylation marker in distinguishing breast cancer from healthy people is shown in Table 6B. Taking Seq ID NO:75 as an example, if the methylation marker is used alone to construct a machine learning model, the model training set AUC is 0.882, and the test set AUC is 0.774. When the threshold is set to 0.534, the test set accuracy is 0.725, the specificity is 0.684, and the sensitivity is 0.762, and the classification effect is obvious. No. (3) GroupThe inventors of this application found that among the 35 methylation markers, the methylation level of a single methylation marker also has a good classification effect. The effect of a single methylation marker in distinguishing breast cancer from healthy people is shown in Table 6C. Taking Seq ID NO:152 as an example, if the methylation marker is used alone to construct a machine learning model, the model training set AUC is 0.792, and the test set AUC is 0.802. When the threshold is set to 0.533, the test set accuracy is 0.800, the specificity is 0.684, and the sensitivity is 0.905, and the classification effect is obvious. This application screened out 79 methylation markers of breast cancer. The machine learning diagnosis model constructed based on the methylation levels of these methylation markers can better distinguish breast cancer from healthy people, which is of great significance for early screening of breast cancer. The above is only the preferred implementation of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the scope of protection of the present invention.

[Figure 1] shows the breast cancer marker screening process. [Figure 2A] shows the methylation levels of the selected 22 markers in the training set and the test set; [Figure 2B] shows the methylation levels of the selected 22 markers in the training set and the test set; [Figure 2C] shows the methylation levels of the selected 35 markers in the training set and the test set. [Figure 3A] shows the methylation level of Seq ID NO:14 in the training set and the test set; [Figure 3B] shows the methylation level of Seq ID NO:75 in the training set and the test set; [Figure 3C] shows the methylation level of Seq ID NO:152 in the training set and the test set [Figure 4A, Figure 4B] and [Figure 4C] show the distribution of AllModel model prediction scores. [Figure 5A, Figure 5B] and [Figure 5C] show the ROC curves of the AllModel model in the training set and the test set. [Figure 6A, Figure 6B] and [Figure 6C] show the prediction score distribution of the Sub1 model. [Figure 7A, Figure 7B] and [Figure 7C] show the ROC curves of the Sub1 model in the training set and the test set. [Figure 8A, Figure 8B] and [Figure 8C] show the prediction score distribution of the Sub2 model. [Figure 9A, Figure 9B] and [Figure 9C] show the ROC curves of the Sub2 model in the training set and the test set.

TW202413655A_112129464_SEQL.xml

Claims (10)

A reagent for use in preparing a kit or microarray for diagnosing breast cancer or predicting breast cancer risk in an individual, characterized in that the reagent is used to detect the methylation level of at least one target region of at least one marker selected from any of the following groups in a sample isolated from the individual: (1) TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1, TNFRSF6B and any combination thereof; (2)SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3, WISP2 and any combination thereof; or (3) WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3, CHRNA4, and any combination thereof, wherein the methylation level of at least one target region of one or more markers is equal to or higher than the threshold value compared to the corresponding threshold value, indicating that the individual has breast cancer or is at risk for breast cancer, and wherein the target region comprises at least one CpG dinucleotide sequence. The use according to claim 1, wherein the methylation is CpG methylation. The use according to claim 1, wherein the reagent is selected from the following reagents: i) a substance that hybridizes with or amplifies at least one target region of the marker, such as an oligonucleotide primer or a probe; and ii) a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, wherein the bisulfite reagent or the methylation-sensitive restriction enzyme reagent distinguishes between methylated and unmethylated dinucleotides, such as methylated and unmethylated CpG dinucleotides, within at least one target region of the marker. The use according to claim 3, wherein the oligonucleotide primer or probe is complementary to or identical to a fragment of at least 9 bases in length of at least one target region of the marker. The use according to any one of claims 1-4, wherein, the marker is CARKD; or a marker combination selected from the following: i) TTLL10, FAM150B, BEND6, ELN, TMEM179 and MYO15B; or ii) EPS8L3, IRF2BP2, TERT, TH, CARKD, SPNS1 and PSG8; or the marker is LZTS1; or a marker combination selected from the following: i) SKI, PRDM16, LZTS1, CCNA1, PIP5K1C and WISP2; or ii) PIAS3, CHD7, CACNA1B, ACVRL1, SNX20, TBCD and ZBTB7A; or the marker is LRRC4; or a marker combination selected from the following: i) ITIH4, FOXC1, PDLIM2, MVK, NLRC5, TCF4 and PLIN3; or ii) RNF144A, SIX2, DGKG, RPL9, LRRC4 and ZSCAN10. The use according to any one of claims 1-4, wherein the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids and combinations thereof; preferably, the sample is selected from plasma, serum, whole blood, separated blood cells and combinations thereof; more preferably, the sample is plasma cfDNA or ctDNA; and/or The target region is selected from: region chr1:1095763-1095986, chr1:110334699-110334899, chr1:234845168-234845486, chr2:469568-469933, chr2:8314701-8314901, chr5:1291139-1291339, chr 5:134374689-134374889, chr5:169805839-169806039, chr6:56716287-56716518, chr7:73407894-73408161, chr10:125650986-125651186, chr11:2226052-2226252, chr13 :111277395-111277690, chr13:24844736-24844936, chr14:105102434-105102644, chr16:28984534-28984734, chr17:73607909-73608115, chr19:10823485-10823947, chr 19:15344061-15344322, chr19:43271257-43271457, chr20:61304694-61304954, chr20:62330559-62330808 or their complementary sequences or processed sequences; or the processed sequences of the complementary sequences; or any combination of the aforementioned sequences and/or regions; or The target region is selected from: region chr1:2166118-2166318, chr1:2978722-2978922, chr1:145562922-145563122, chr4:48485417-48485821, chr5:139076623-139076941, chr6:108488634-1084 88917, chr6:166970625-166970825, chr7:27260117-27260462, chr8:20375580-20375780, chr8:61788861-61789200, chr9:68413067-68413267, chr9:140683687-14068396 9, chr12:52311647-52311991, chr13:37005935-37006328, chr13:51417486-51417774, chr16:50715367-50715567, chr17:80745056-80745446, chr19:3688030-3688230, c hr19:4059528-4059746, chr19:12978686-12978886, chr19:31842771-31842971, chr20:43331809-43332099 or their complementary sequences or processed sequences; or the processed sequences of the complementary sequences; or any combination of the aforementioned sequences and/or regions; or The target region is selected from: region chr1:3567381-3567648, chr1:151811354-151811554, chr1:169396540-169396740, chr2:7148520-7148720, chr2:45232498-45232698, chr2:50574443-50574739, chr2:66666356-66666556, chr2:74731340-74731602, chr3:49459532-49459732, chr3: 52864771-52864971、chr3:52865018-52865236、chr3:129693578-129693796、chr3:157825025-157825225、chr3:185973717-185973917、chr4:39448374-39448574、chr5:176829529-176829796、chr6:1614911-1615144、chr6:6724534-6724734、chr6:118229139-118 229400, chr7:127744150-127744731, chr8:22438141-22438341, chr10:102497304-102497504, chr12:109996613-109997009, chr12:113515300-113515540, chr12:114162628-114162828, chr14:55243006-55243206, chr14:57264908-57265108, chr16:3139015-31 39246, chr16:31580122-31580353, chr16:57025884-57026193, chr18:31159160-31159360, chr18:53447617-53447817, chr19:4912069-4912269, chr19:15580341-15580719, chr20:62046355-62046589 or their complementary sequences or processed sequences; or the processed sequences of the complementary sequences; or any combination of the aforementioned sequences and/or regions. A kit or microarray for diagnosing breast cancer or predicting breast cancer risk in an individual, wherein the kit or microarray comprises a reagent for detecting the methylation level of at least one target region of at least one marker selected from any of the following groups in a sample isolated from the individual: (1) TTLL10, EPS8L3, IRF2BP2, FAM150B, ID2, TERT, PITX1, KCNMB1, BEND6, ELN, CPXM2, TH, C1QTNF9, CARKD, TMEM179, SPNS1, MYO15B, DNM2, EPHX3, PSG8, SLCO4A1, TNFRSF6B and any combination thereof; (2)SKI, PRDM16, PIAS3, SLC10A4, CXXC5, NR2E1, MPC1, HOXA13, LZTS1, CHD7, ANKRD20A1, CACNA1B, ACVRL1, CCNA1, RNASEH2B, SNX20, TBCD, PIP5K1C, ZBTB7A, DNASE2, TSHZ3, WISP2 and any combination thereof; or (3) WRAP73, C2CD4D, CCDC181, RNF144A, SIX2, NRXN1, MEIS1, LBX2, AMT, ITIH4, TRH, SHOX2, DGKG, RPL9, PFN3, FOXC1, LY86, SLC35F1, LRRC4, PDLIM2, PAX2, MVK, DTX1, RBM19, GCH1, OTX2, ZSCAN10, AHSP, NLRC5, ASXL3, TCF4, PLIN3, RASAL3, CHRNA4, and any combination thereof, wherein the methylation level of at least one target region of one or more markers is equal to or higher than the threshold value compared to the corresponding threshold value, indicating that the individual has breast cancer or is at risk for breast cancer, and wherein the target region comprises at least one CpG dinucleotide sequence. The kit or microarray according to claim 7, wherein the sample is selected from cell lines, histological sections, tissue biopsies, paraffin-embedded tissues, body fluids and combinations thereof; preferably, the sample is selected from plasma, serum, whole blood, isolated blood cells and combinations thereof; more preferably, the sample is plasma cfDNA or ctDNA; and/or The target region is selected from: region chr1:1095763-1095986, chr1:110334699-110334899, chr1:234845168-234845486, chr2:469568-469933, chr2:8314701-8314901, chr5:1291139-1291339, chr 5:134374689-134374889, chr5:169805839-169806039, chr6:56716287-56716518, chr7:73407894-73408161, chr10:125650986-125651186, chr11:2226052-2226252, chr13 :111277395-111277690, chr13:24844736-24844936, chr14:105102434-105102644, chr16:28984534-28984734, chr17:73607909-73608115, chr19:10823485-10823947, chr 19:15344061-15344322, chr19:43271257-43271457, chr20:61304694-61304954, chr20:62330559-62330808 or their complementary sequences or processed sequences; or the processed sequences of the complementary sequences; or any combination of the aforementioned sequences and/or regions; or The target region is selected from: region chr1:2166118-2166318, chr1:2978722-2978922, chr1:145562922-145563122, chr4:48485417-48485821, chr5:139076623-139076941, chr6:108488634-1084 88917, chr6:166970625-166970825, chr7:27260117-27260462, chr8:20375580-20375780, chr8:61788861-61789200, chr9:68413067-68413267, chr9:140683687-14068396 9, chr12:52311647-52311991, chr13:37005935-37006328, chr13:51417486-51417774, chr16:50715367-50715567, chr17:80745056-80745446, chr19:3688030-3688230, c hr19:4059528-4059746, chr19:12978686-12978886, chr19:31842771-31842971, chr20:43331809-43332099 or their complementary sequences or processed sequences; or the processed sequences of the complementary sequences; or any combination of the aforementioned sequences and/or regions; or The target region is selected from: region chr1:3567381-3567648, chr1:151811354-151811554, chr1:169396540-169396740, chr2:7148520-7148720, chr2:45232498-45232698, chr2:50574443-50574739, chr2:66666356-66666556, chr2:74731340-74731602, chr3:49459532-49459732, chr3: 52864771-52864971、chr3:52865018-52865236、chr3:129693578-129693796、chr3:157825025-157825225、chr3:185973717-185973917、chr4:39448374-39448574、chr5:176829529-176829796、chr6:1614911-1615144、chr6:6724534-6724734、chr6:118229139-118 229400, chr7:127744150-127744731, chr8:22438141-22438341, chr10:102497304-102497504, chr12:109996613-109997009, chr12:113515300-113515540, chr12:114162628-114162828, chr14:55243006-55243206, chr14:57264908-57265108, chr16:3139015-31 39246, chr16:31580122-31580353, chr16:57025884-57026193, chr18:31159160-31159360, chr18:53447617-53447817, chr19:4912069-4912269, chr19:15580341-15580719, chr20:62046355-62046589 or their complementary sequences or processed sequences; or the processed sequences of the complementary sequences; or any combination of the aforementioned sequences and/or regions. A kit or microarray according to claim 7 or 8, wherein the reagent is selected from the following reagents: i) a substance that hybridizes with or amplifies at least one target region of the marker, such as an oligonucleotide primer or probe, preferably, the oligonucleotide primer or probe is complementary to or identical to a fragment of at least 9 bases in length of at least one target region of the marker; and ii) a bisulfite reagent or a methylation-sensitive restriction enzyme reagent, the bisulfite reagent or the methylation-sensitive restriction enzyme reagent distinguishing between methylated and unmethylated dinucleotides, such as methylated and unmethylated CpG dinucleotides, within at least one target region of the marker. The kit or microarray according to claim 7 or 8, wherein the marker is CARKD; or a marker combination selected from the following: i) TTLL10, FAM150B, BEND6, ELN, TMEM179 and MYO15B; or ii) EPS8L3, IRF2BP2, TERT, TH, CARKD, SPNS1 and PSG8; or the marker is LZTS1; or a marker combination selected from the following: i) SKI, PRDM16, LZTS1, CCNA1, PIP5K1C and WISP2; or ii) PIAS3, CHD7, CACNA1B, ACVRL1, SNX20, TBCD and ZBTB7A; or the marker is LRRC4; or a marker combination selected from the following: i) ITIH4, FOXC1, PDLIM2, MVK, NLRC5, TCF4 and PLIN3; or ii) RNF144A, SIX2, DGKG, RPL9, LRRC4 and ZSCAN10.