Methylation prediction marker as well as screening method and application thereof

Abstract

The invention provides a methylation prediction marker as well as a screening method and application thereof. Specifically, the invention provides application of the methylation gene marker or the detection reagent thereof to preparation of the detection reagent or the kit, and the detection reagent or the kit is used for detecting (a) methylation level; and / or (b) a risk of cancer onset. The invention also provides a kit or a device for cancer risk detection or prediction. The methylation gene marker provided by the invention can efficiently and accurately predict the methylation level and the cancer occurrence risk.

Description

Technical Field

[0001] The present invention belongs to the field of molecular biotechnology, and in particular, relates to methylation prediction markers and screening methods and applications thereof. Background Art

[0002] DNA methylation plays an important regulatory role in the progression of the disease. There have been a large number of studies on the role of cfDNA methylation levels in disease diagnosis and prognosis prediction. However, the gold standard sulfite methylation treatment will cause a large loss of cfDNA, and the enzyme conversion methylation technology also needs further standardization and evaluation.

[0003] cfDNA is not randomly fragmented, and its fragmentation pattern is related to nuclease activity and nucleosome structure. At the same time, studies have shown that the cfDNA fragmentation pattern is related to the epigenetic background. There are significant differences in the fragmentation patterns between methylated and unmethylated cfDNA molecules. Therefore, DNA methylation-related research can be conducted based on the cfDNA fragmentation pattern.

[0004] The study "Epigenetic analysis of cell-free DNA by fragmentomic profiling" published in PANS in 2022 showed that the whole-genome cfDNA 5' CGN / NCG motif ratio was verified to be positively correlated with the methylation level, and the "CGN / NCG motif ratio" can be used to characterize the methylation level; using CGN and NCG (including 8 base sequences, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG), cancer can be predicted.

[0005] It is well known that biological physiological activities are complex, but this method only extracts 8 CpG-related 5'-end base sequences, and does not analyze and verify the correlation between other base sequences at the end of cfDNA and methylation.

[0006] Therefore, there is still a lack of a more comprehensive, effective and accurate marker for predicting methylation in the art. Summary of the invention

[0007] The present invention provides a marker that can be used to efficiently and accurately predict methylation.

[0008] In a first aspect of the present invention, there is provided a use of a methylation gene marker or a detection reagent thereof for preparing a detection reagent or a kit, wherein the detection reagent or the kit is used to detect (a) methylation level; and / or (b) cancer risk; Wherein, the methylation gene marker is a set of base sequences obtained by taking n bp upstream, downstream, or upstream and downstream (upstream and downstream) at the 5' end breakpoint when cfDNA is aligned to the reference genome, with an interval of m bp, and taking n bp; m is any integer from 0 to 5, and n is any integer from 1 to 4; The methylation gene markers include a set of base sequences coded as A1 to A59 in the following table:

[0009] In another preferred embodiment, the methylation gene marker also includes a set of base sequences coded as B1 to B41 in the following table:

[0010] In another preferred embodiment, when the direction of the gene marker is upstream and downstream, its base length = 2*n.

[0011] In another preferred embodiment, the cancer is selected from the following group: gastric cancer, breast cancer, colon cancer, bile duct cancer, glioma, sarcoma, epithelial cancer, lymphoma, melanoma, fibroma, meningioma, brain cancer, kidney cancer, thyroid cancer, parathyroid cancer, pituitary tumor, adrenal tumor, osteogenic sarcoma tumor, neuroendocrine system tumor, lung cancer, head and neck cancer, prostate cancer, esophageal cancer, tracheal cancer, liver cancer, bladder cancer, pancreatic cancer, ovarian cancer, uterine cancer, cervical cancer, testicular cancer, skin cancer, or a combination thereof.

[0012] In another preferred embodiment, the cancer is selected from the group consisting of gastric cancer, breast cancer, colon cancer, bile duct cancer, or a combination thereof.

[0013] In a second aspect of the present invention, a method for constructing a cancer risk prediction model is provided, comprising the following steps: (s1) providing a first data set for model training and a second data set for model testing, wherein the first data set includes methylation gene marker data of a cancer positive sample group and methylation gene marker data of a cancer negative sample group; The second data set includes methylation gene marker data of a cancer positive sample group and methylation gene marker data of a cancer negative sample group; and (s2) constructing a cancer risk prediction model using a machine learning method based on the data in the first data set, and verifying it on the second data set, thereby obtaining the cancer risk prediction model.

[0014] In another preferred example, the methylation gene marker data is the frequency of the methylation gene marker.

[0015] In another preferred example, the frequency of the methylation gene marker is the proportion of the cfDNA fragments corresponding to the methylation gene marker in the sample to all the cfDNA fragments.

[0016] In another preferred embodiment, the machine learning method is a gradient boosting algorithm (GBM).

[0017] In another preferred embodiment, the methylation gene markers include a set of base sequences coded as A1 to A59 in the following table:

[0018] In another preferred embodiment, the methylation gene marker also includes a set of base sequences coded as B1 to B41 in the following table:

[0019] In another preferred embodiment, the methylation gene marker data is obtained by the following steps: (i) extracting cfDNA from the sample to be tested and obtaining read data; (ii) aligning the read data to a reference genome to obtain the position of the 5' end of the read on the reference genome; (iii) according to the position of the 5' end of the read segment on the reference genome, upstream, downstream, or upstream and downstream respectively, from the 5' end breakpoint, at intervals of m bp, and taking n bp to obtain a base sequence, thereby obtaining a set of base sequences of the methylation gene markers in Table A and / or Table B; and (iv) Counting the proportion of cfDNA fragments corresponding to each base sequence in the base sequence set in all cfDNA fragments, thereby obtaining the frequency of each base sequence, thereby obtaining the methylation gene marker data.

[0020] In another preferred embodiment, the sample to be tested is a body fluid sample.

[0021] In another preferred embodiment, the sample to be tested is selected from the following group: a plasma sample, a blood sample, a urine sample, an ascites sample, or a combination thereof.

[0022] In a third aspect of the present invention, a cancer risk prediction device is provided, the device comprising: (a) An input module, wherein the input module is configured to input methylation gene marker data in a sample to be tested; the methylation gene marker comprises a set of base sequences coded as A1 to A59 in the following table:

[0023] (b) a risk assessment module, wherein the risk assessment module is configured to input the input methylation gene marker data into the cancer risk prediction model constructed by the method according to the second aspect of the present invention to perform risk assessment, thereby obtaining a risk assessment result: (3) an output module, wherein the output module is configured to output the cancer risk assessment result.

[0024] In another preferred embodiment, the methylation gene marker also includes a set of base sequences coded as B1 to B41 in the following table:

[0025] In another preferred embodiment, the risk assessment module performs the following risk assessment: When the input methylation marker is an up-regulated marker (an up-regulated marker in Table A and / or Table B), and it is up-regulated in the sample to be tested, and when the input methylation marker is a down-regulated marker (a down-regulated marker in Table A and / or Table B), and it is down-regulated in the sample to be tested, it indicates that the subject to be tested has a high risk of cancer; otherwise, it indicates that the risk of cancer is not high.

[0026] In another preferred example, the device further includes a sequencing module, which is configured to extract and sequence cfDNA from the sample to be tested to obtain read data.

[0027] In another preferred embodiment, the sample or specimen to be tested is a body fluid sample of the subject to be tested.

[0028] In another preferred embodiment, the sample to be tested is selected from the following group: a plasma sample, a blood sample, a urine sample, an ascites sample, or a combination thereof.

[0029] In another preferred embodiment, the sequencing is whole genome sequencing.

[0030] In another preferred embodiment, the sequencing is low-depth whole genome sequencing.

[0031] In another preferred embodiment, the sequencing depth of the sequencing is 1~2X.

[0032] In another preferred example, the device also includes a comparison module, which is configured to compare the read data to a reference genome to obtain the position of the 5' end of the read on the reference genome; and obtain a base sequence obtained by taking n bp upstream, downstream, or upstream and downstream respectively, at an interval of m bp at the 5' end point, thereby obtaining the methylation gene markers (or base sequences) in Table A and / or Table B.

[0033] In another preferred example, the device further includes a statistical module, which is configured to calculate the proportion of cfDNA corresponding to each of the methylation gene markers in the total cfDNA, thereby obtaining methylation gene marker data.

[0034] In a fourth aspect of the present invention, a set of methylation gene markers is provided, wherein the set of methylation gene markers is a set of base sequences obtained by taking n bp upstream, downstream, or upstream and downstream, respectively, at intervals of m bp at the 5' end breakpoint when cfDNA is aligned to a reference genome; The methylation gene marker set includes a set of base sequences coded A1 to A59 in the following table:

[0035] In another preferred embodiment, the methylation gene marker set also includes a set of base sequences B1 to B41 in the following table:

[0036] In a fifth aspect of the present invention, a computer storage device is provided, wherein the device stores a computer program corresponding to the algorithm of the cancer risk prediction model constructed by the method according to the second aspect of the present invention.

[0037] In a sixth aspect of the present invention, a method for screening methylation gene markers is provided, wherein the method analyzes the differences between the end sequences of fragments in the high methylation level interval and the low methylation level interval in the plasma of healthy people, thereby discovering gene markers with significant differences between the high methylation level interval and the low methylation level interval. Specifically, the method comprises the following steps: (i) Obtaining high-methylation intervals and low-methylation intervals in chromosomes of healthy people; (ii) extracting cfDNA from healthy human samples and obtaining cfDNA read data; (iii) aligning the cfDNA read data to a reference genome to obtain the position of the 5' end of the read on the reference genome; and obtaining the cfDNA read located in the high methylation interval and the cfDNA read located in the low methylation interval respectively; (iv) according to the position of the 5' end of the read segment in step (iii) on the reference genome, from the 5' end breakpoint upstream, downstream, and upstream and downstream respectively, at intervals of m bp, take n bp to obtain the base sequence; wherein m is any integer from 0 to 5, and n is any integer from 1 to 4; (v) counting the proportion of cfDNA fragments corresponding to each base sequence in the high methylation interval in all cfDNA fragments, thereby obtaining the frequency of each base sequence in the high methylation interval; counting the proportion of cfDNA fragments corresponding to each base sequence in the low methylation interval in all cfDNA fragments, thereby obtaining the frequency of each base sequence in the low methylation interval; (vi) Count the pvalues ​​of each base sequence whose frequency in the high methylation interval is higher than that in the low methylation interval, perform multiple hypothesis verification, and select base sequences whose Q value (qvalue) is less than a certain threshold; Count the pvalues ​​of each base sequence whose frequency in the high methylation interval is lower than that in the low methylation interval, perform multiple hypothesis verification, and select base sequences whose qvalue is less than a certain threshold; and (vii) Using the Relief method to filter and screen the base sequences in step (vi), and selecting the base sequences with the largest Relief values, thereby obtaining methylation gene markers (i.e., a collection of base sequences with the largest Relief values).

[0038] In another preferred embodiment, the methylation gene marker is used to detect (a) methylation level; and / or (b) cancer risk.

[0039] In another preferred embodiment, the sample is a body fluid sample.

[0040] In another preferred embodiment, the sample is selected from the following group: a plasma sample, a blood sample, a urine sample, an ascites sample, or a combination thereof.

[0041] In another preferred embodiment, in step (vi), the following steps are included: using Wilcoxon test to count the pvalue of each base sequence frequency in the high methylation interval that is higher than that in the low methylation interval, using Benjamini & Hochberg method to perform multiple hypothesis verification, and selecting base sequences with FDR less than a certain threshold; using Wilcoxon test to count the pvalue of each base sequence frequency in the high methylation interval that is lower than that in the low methylation interval, using Benjamini & Hochberg method to perform multiple hypothesis verification, and selecting base sequences with FDR less than a certain threshold.

[0042] It should be understood that within the scope of the present invention, the above-mentioned technical features of the present invention and the technical features specifically described below (such as embodiments) can be combined with each other to form a new or preferred technical solution. Due to space limitations, they will not be described one by one here. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1The correlation between the gold standard methylation level and the methylation level calculated based on the tissue (cell) methylation level and the proportion of healthy human cells is shown.

[0044] Figure 2 The correlation between all methylation-related base sequences and methylation levels is shown.

[0045] Figure 3 The correlation between methylation-related base sequences that are not directly related to CGN and NCG and methylation levels is shown.

[0046] Figure 4 The correlation between CGN, NCG and methylation levels is shown.

[0047] Figure 5 The performance of methylation-related base sequences in cancer prediction on the whole genome is shown.

[0048] Figure 6 The performance of methylation-related base sequences in cancer prediction in the CpGisland and CpGshore intervals is shown.

[0049] Figure 7 The performance of methylation-related base sequences in cancer prediction in the CpGisland interval is shown. DETAILED DESCRIPTION

[0050] After extensive and in-depth research, the inventors have found, through a large number of screenings, for the first time, base sequences with significant differences between high methylation intervals and low methylation intervals, and the base sequences can be used to judge the level of methylation and / or the risk of cancer. Specifically, the experiments of the present invention show that the model constructed based on 59 methylation gene markers of the present invention has good cancer risk prediction performance in the whole genome, CpG island and CpG shore intervals, or CpG island intervals, and the AUC values ​​verified are 0.98, 0.96, and 0.91, respectively. On this basis, the present invention has been completed.

[0051] In addition, the 59 methylation gene markers of the present invention can also be combined with 41 base sequences directly related to CGN and NCG to form gene markers for judging the level of methylation and / or the risk of cancer. And the model constructed based on the 100 methylation gene markers of the present invention also has good cancer risk prediction performance in the whole genome, CpG island and CpG shore interval, or CpG island interval, and its verified AUC values ​​are 0.97, 0.95, and 0.91, respectively.

[0052] the term In order to more easily understand the present disclosure, some terms are first defined. As used in this application, unless otherwise expressly provided herein, each of the following terms should have the meaning given below. Other definitions are set forth throughout the application.

[0053] As used herein, the term "comprising" or "including (comprising)" may be open, semi-closed and closed. In other words, the term also includes "consisting essentially of" or "consisting of".

[0054] As used herein, the term "reference value" or "control reference value" refers to a value that is statistically correlated with a particular result when compared to the results of an analysis. Research from the literature and user experience of the methods disclosed herein can also be used to produce or adjust reference values. Reference values ​​can also be determined by considering circumstances and results that are particularly relevant to the patient's ethnic group, medical history, genetics, age, and other factors.

[0055] As used herein, "CpG shore" refers to a 2 kb interval extending upstream and downstream of a CpG island.

[0056] As used herein, "CGNNCG" refers to the 5'-terminal 3 bp bases starting with CG or ending with CG.

[0057] As used herein, "taking a base sequence downstream" includes this position.

[0058] As used herein, "upstream fetching sequence" does not include this position.

[0059] CGNNCG characterization of methylation: The study "Epigenetic analysis of cell-free DNA by fragmentomic profiling" published in PANS in 2022 showed that the 5' CGN / NCG motifratio of whole-genome cfDNA was verified to be positively correlated with the methylation level, and the "CGN / NCG motif ratio" can be used to characterize the methylation level; using CGN and NCG (including 8 base sequences, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG), cancer can be predicted.

[0060] This method infers and verifies that the shearing ratio of methylated CG at the CG position is higher than that of unmethylated CG by counting the differences in the shearing ratio of methylated CG and the bases at both ends of the methylated CG in the sample, and the shearing ratio at the position 1bp before the methylated CG is lower than that of unmethylated CG. Therefore, 5' end CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG are used to characterize the methylation status. Since the methylation status of the sample is closely related to cancer, these 8 base sequences can be used to predict cancer. Gradient Boosting Machine (GBM) is a common type of algorithm in machine learning. Its basic principle is to train the newly added weak classifier according to the negative gradient information of the current model loss function, and then combine the trained weak classifier into the existing model in an accumulated form to obtain the optimal model. This model has the advantages of good training effect and not easy to overfit.

[0061] The methylation level in the present invention refers to the degree of DNA methylation at a specific site or region in an organism. Significant increase or decrease in the methylation level indicates abnormal methylation level. Abnormal DNA methylation is closely related to the occurrence, development and cell carcinogenesis of tumors.

[0062] In tumors, the promoter region of tumor suppressor genes is often in a hypermethylated state, leading to gene silencing and uncontrolled cell proliferation, thereby increasing the risk of cancer.

[0063] In the present invention, the terms "methylation gene markers of the present invention", "gene markers of the present invention", "markers of the present invention", or "markers shown in Table A and / or Table B" are used interchangeably and refer to any one or more of the methylation gene markers of the present invention.

[0064] In the present invention, the term "methylation gene marker of the present invention" refers to any base sequence or nucleotide sequence shown in Table A and / or Table B.

[0065] In the present invention, the methylation gene marker includes any base sequence shown in A1 to A59 in the following Table A, or a combination thereof: Table A

[0066] In the present invention, the methylation gene marker also includes any base sequence shown in B1 to B41 in the following Table B, or a combination thereof: Table B

[0067] The main advantages of the present invention include: (a) The research object of CGN and NCG in the prior art is CpG, which only studies CpG and its upstream and downstream sequences, and finds that the proportion of CpG in methylation state and unmethylation state at the 5' end is different, so as to perform methylation-related analysis based on CGN and NCG at the 5' end of cfDNA fragments. The method of the present invention obtains methylation-related base sequences by evaluating the differences between the terminal sequences of various forms of cfDNA fragments in the high methylation interval and the low methylation interval, which is more comprehensive and effective.

[0068] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are intended to illustrate the present invention only and are not intended to limit the scope of the present invention. The experimental methods in the following examples where specific conditions are not specified are usually performed under conventional conditions, such as those described in Sambrook et al., Molecular Cloning: A Laboratory Manual (New York: Cold Spring Harbor Laboratory Press, 1989), or according to the conditions recommended by the manufacturer. Unless otherwise indicated, percentages and parts are weight percentages and weight parts.

[0069] Example 1 Correlation between the actual methylation level and the methylation level calculated based on the tissue (cell) methylation level and the proportion of healthy human cells Based on the database of 5,820 units of methylation levels in different tissues provided in the existing technical literature ("DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments") and the proportion of tissues (cells) from different sources of cfDNA in healthy human plasma, the methylation level of each unit of healthy human plasma cfDNA was calculated, and units with the same methylation level were merged into the same methylation level range.

[0070] For the convenience of description, "the methylation level of cfDNA in healthy people calculated based on the methylation level of tissues (cells) and the proportion of different cells in healthy people" is referred to as "methylation level" in this article. At the same time, the whole genome methylation sequencing data of healthy human plasma samples were analyzed, and the methylation level in the above-mentioned methylation level range was calculated as the "gold standard methylation level", and the correlation between the two was compared.

[0071] In this example, the plasma cfDNA samples of healthy people were subjected to whole genome methylation sequencing using sulfite methylation, and the sequencing data volume was 51.93G. After sulfite treatment, the methylated C in CpG remained unchanged, and the unmethylated C was converted to T.

[0072] The specific steps are as follows: (1) Based on the methylation database information and the proportion of tissues (cells) from different sources of cfDNA in plasma, the methylation level of each unit in healthy people is calculated using the following formula:

[0073] in is the methylation level of tissue (cell) 𝑖, is the proportion of tissue (cell) 𝑖 in plasma cfDNA, is the coefficient of tissue (cell) 𝑖. In this embodiment, the coefficients are all 1, and the proportions of neutrophils, B cells, T cells, liver tissue, colon tissue, heart tissue, brain tissue and lung tissue in healthy human plasma cfDNA are set to 0.667, 0.164, 0.175, 0.058, 0.04, 0.035, 0.031 and 0.024; (2) The units were grouped according to the methylation level. The units with methylation level <=10 were merged into the M0_10 interval; the units with methylation level >10 and <=20 were merged into the M10_20 interval; the units with methylation level >20 and <=30 were merged into the M20_30 interval; the units with methylation level >30 and <=40 were merged into the M30_40 interval; the units with methylation level >40 and <=50 were merged into the M40_50 interval; the units with methylation level >50 and <=60 were merged into the M50_60 interval; the units with methylation level >60 and <=70 were merged into the M60_70 interval; the units with methylation level >70 and <=80 were merged into the M70_80 interval; the units with methylation level >80 and <=90 were merged into the M80_90 interval; the units with methylation level >90 were merged into the M90 ​​interval; (3) Calculate the average methylation level of each methylation level interval as the methylation level of the interval; (4) The software Msuit2 (v2.2.0) was used to calculate the methylation level of CpG in healthy human plasma cfDNA, with the reference sequence hg19 and default parameters selected to obtain the methylation information of each CpG; (5) Count the reads supporting the detection of C or T in CpG sites in the intervals of M0_10, M10_20, M20_30, M30_40, M40_50, M50_60, M60_70, M70_80, M80_90 and M90 respectively; count the reads supporting the detection of C as C in CpG sites in the intervals of M0_10, M10_20, M20_30, M30_40, M40_50, M50_60, M60_70, M70_80, M80_90 and M90 respectively; (6) Calculate the gold standard methylation levels in the intervals of M0_10, M10_20, M20_30, M30_40, M40_50, M50_60, M60_70, M70_80, M80_90 and M90 using the following formula: Gold standard methylation level = NC / (NC+NT); Among them, NC is the number of reads supporting that C in the CpG site is detected as C, and NT is the number of reads supporting that C in the CpG site is detected as T.

[0074] (7) Compare the Pearson correlation between the methylation level described in step (3) and the gold standard methylation described in step (6).

[0075] When calculating the methylation level of each unit, considering that white blood cells are dominant in the cfDNA source and the deviation of white blood cells in cfDNA is large (mean 79%, IQR 74%~88%). In order to reduce the impact of the fluctuation of the proportion of white blood cells in healthy human plasma, the white blood cell (neutrophil, B cell and T cell) coefficient is set to 1 when calculating the methylation level. When calculating the methylation level of healthy people, its value may be greater than 1. The results are shown in Table 1.

[0076] Table 1

[0077] The correlation between the gold standard methylation level and the methylation level is 0.998. The methylation level calculated based on the tissue (cell) methylation level and the proportion of healthy human cells can be used to characterize the true methylation level, such as Figure 1 shown.

[0078] Example 2 Marker Screening In order to study the differences between the high methylation level interval and the low methylation level interval in the end sequence of the fragment in the plasma of healthy people, the methylation level of the plasma cfDNA of healthy people was first calculated in the database of 5820 units provided in the prior art literature ("DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments"). When the methylation level was >= 0.8, the unit was considered to be a high methylation unit, and when the methylation level was <= 0.1, the unit was considered to be a low methylation unit. Then the high methylation units were merged into high methylation intervals, and the low methylation units were merged into low methylation intervals. Finally, the differences between various base sequences in high methylation intervals and low methylation intervals were calculated, and the top 100 base sequences with significant differences were taken as markers for methylation prediction.

[0079] The specific steps are as follows: (1) Based on the methylation database information and the proportion of tissues (cells) from different sources of cfDNA in plasma, the methylation level of each unit in healthy people is calculated using the following formula:

[0080] in is the methylation level of tissue (cell) 𝑖, is the proportion of tissue (cell) 𝑖 in plasma cfDNA, is the coefficient of tissue (cell) 𝑖. In this embodiment, the coefficients are all 1, and the proportions of neutrophils, B cells, T cells, liver tissue, colon tissue, heart tissue, brain tissue and lung tissue in healthy human plasma cfDNA are set to 0.667, 0.164, 0.175, 0.058, 0.04, 0.035, 0.031 and 0.024 respectively.

[0081] (2) The unit is defined as a high-methylation or low-methylation unit according to the methylation level. When the methylation level is greater than or equal to 0.8, the unit is considered high-methylation, and when the methylation level is less than or equal to 0.1, the unit is considered low-methylation. (3) All low-methylation units are merged into low-methylation intervals, and all high-methylation units are merged into high-methylation intervals, in the format of bed; (4) 28 healthy human plasma samples were selected for low-depth whole-genome sequencing data, with a sequencing depth of 1~2X. The obtained fastq was filtered and aligned with the human genome using the software BWA. The alignment files were sorted using the software samtools to obtain the sorted alignment files. The sorted alignment files were deduplicated using the software picard to obtain the deduplicated alignment files, remove the alignment information with low alignment quality, and remove the alignment information on chromosomes X and Y. Extract the position information of the cfDNA molecules aligned to the chromosomes and convert it into bed format; (5) using the software bedtools, the position information of the cfDNA molecules of the samples described in step (4) on the chromosome is compared with the position information of the high methylation interval described in step (3), and the cfDNA fragments of the 28 healthy human samples located in the high methylation interval are obtained; the position information of the cfDNA molecules of the samples described in step (4) on the chromosome is compared with the position information of the low methylation interval described in step (3), and the cfDNA fragments of the 28 healthy human samples located in the low methylation interval are obtained; (6) extracting the locations of the 5' ends of the cfDNA fragments located in the high methylation interval and the cfDNA fragments located in the low methylation interval described in step (5) on the chromosome respectively; (7) According to the position described in step (6), 1 bp, 2 bp, 3 bp, 4 bp, and 5 bp are respectively separated and taken downstream (from 5' to 3'). According to the position described in step (6), 1 bp, 2 bp, 3 bp, and 4 bp are respectively separated and taken upstream (from 3' to 5'). According to the position described in step (6), 1 bp, 2 bp, 3 bp, 4 bp, and 5 bp are respectively separated and taken upstream (from 3' to 5'). According to the position described in step (6), 1 bp, 2 bp, 3 bp, and 4 bp are respectively taken upstream and downstream. (8) calculating the frequencies of the various base sequences described in step (7); (9) Use Wilcoxon test to calculate the pvalue of each base sequence frequency in the high methylation interval that is higher than that in the low methylation interval, use Benjamini & Hochberg method to verify multiple hypotheses, and select base sequences with FDR < 0.05; Use Wilcoxon test to calculate the pvalue of each base sequence frequency in the high methylation interval that is lower than that in the low methylation interval, use Benjamini & Hochberg method to verify multiple hypotheses, and select base sequences with FDR < 0.05; (10) Using the Relief method, filter and screen the base sequences described in step (9), and select the 100 base sequences with the largest Relief values ​​as methylation-related markers.

[0082] In the results, the length of the low methylation interval is 0.543M, and the length of the high methylation interval is 0.480M. The detailed information of the 100 base sequences is shown in Table 2, of which 41 base fragments are directly related to the 5' end CGN and NCG.

[0083] Table 2 Base sequence information of 100 methylation predictions

[0084] Example 3 Correlation between markers and methylation levels Another 30 healthy samples were used to verify the correlation between the markers and methylation levels. The specific steps are as follows: (1) According to step (1) in Example 1, the methylation level of each unit of a healthy person is calculated; (2) Group the units according to the methylation level. The units with methylation level <=10 are merged into the M0_10 interval; the units with methylation level >10 and <=20 are merged into the M10_20 interval; the units with methylation level >20 and <=30 are merged into the M20_30 interval; the units with methylation level >30 and <=40 are merged into the M30_40 interval; the units with methylation level >40 and <=50 are merged into the M40_50 interval; the units with methylation level >50 and <=60 are merged into the M50_60 interval; the units with methylation level >60 and <=70 are merged into the M60_70 interval; the units with methylation level >70 and <=80 are merged into the M70_80 interval; the units with methylation level >80 and <=90 are merged into the M80_90 interval; the units with methylation level >90 are merged into the M90 ​​interval; filter out the methylation level intervals with a length less than 0.2M; (3) Calculate the average methylation level of each methylation level interval as the methylation level of the interval; (4) 30 healthy human plasma samples were selected for low-depth whole-genome sequencing data, with a sequencing depth of 1~2X. The obtained fastq was filtered and aligned with the human genome using the software BWA. The alignment files were sorted using the software samtools to obtain the sorted alignment files. The sorted alignment files were deduplicated using the software picard to obtain the deduplicated alignment files, remove the alignment information with low alignment quality, and remove the alignment information on chromosomes X and Y. Extract the position information of the cfDNA molecules aligned to the chromosomes and convert it into bed format; (5) using the software bedtools, respectively comparing the position information of the cfDNA molecules of the samples described in step (4) on the chromosome with the position information of the different methylation level intervals described in step (2), and obtaining the cfDNA fragments of 30 healthy human samples located in different methylation level intervals; (6) extracting the positions of the 5' ends of the cfDNA fragments located in different methylation level intervals on the chromosome respectively in step (5); (7) according to the position described in step (6), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken downstream (from 5' to 3'); according to the position described in step (6), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken upstream (from 3' to 5'); according to the position described in step (6), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken upstream (from 3' to 5'); according to the position described in step (6), 1 bp, 2 bp, 3 bp and 4 bp are respectively taken upstream and downstream; (8) calculating the frequencies of the various base sequences described in step (7) for each methylation level interval; (9) Extract the frequencies of the 100 base sequences described in Table 2, add the frequencies of base sequences that are "upregulated" in the high methylation interval relative to the low methylation interval to obtain the sum of the frequencies of the upregulated base sequences SU, add the frequencies of base sequences that are "downregulated" in the high methylation interval relative to the low methylation interval to obtain the sum of the frequencies of the downregulated base sequences SD, calculate SU / SD, and use the Spearman method to calculate the correlation between SU / SD and methylation level; (10) Extract the frequencies of 51 base sequences that have no direct relationship with CGN and NCG in the base sequences described in Table 2, add the frequencies of base sequences that are "upregulated" in the high methylation interval relative to the low methylation interval to obtain the sum of the frequencies of upregulated base sequences SU, add the frequencies of base sequences that are "downregulated" in the high methylation interval relative to the low methylation interval to obtain the sum of the frequencies of downregulated base sequences SD, calculate SU / SD, and use the Spearman method to calculate the correlation between SU / SD and methylation level; (11) Extract the frequencies of the eight base sequences at the 5' end, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG. Add the frequencies of CGA, CGC, CGT and CGG to obtain the sum of the base sequence frequencies SU. Add the frequencies of ACG, CCG, CGT and GCG to obtain the sum of the base sequence frequencies SD. Calculate SU / SD. Use the Spearman method to calculate the correlation between SU / SD and methylation level. In the results, the lengths of M50_60, M60_70, M70_80, and M80_90 were less than 0.2M and were not included in the analysis. The methylation levels of M0_M10, M10_M20, M20_M30, M30_M40, M40_M50, and M90 were 6.11, 14.86, 24.66, 34.69, 44.84, and 104.51, respectively. Table 3 shows the frequencies of methylation predicted base sequences in sample S1 at different methylation level intervals. The SU, SD, and SU / SD values ​​of different numbers of base sequences were calculated based on Table 3, as shown in Table 4.

[0085] Table 3 The frequency of methylation predicted base sequences in different methylation level ranges in sample S1

[0086] Table 4 Values ​​of SU, SD and SU / SD in different methylation levels of sample S1

[0087] From the results, we can see that the correlation between SU / SD and methylation level of all base fragments of 30 samples is 0.977 ( Figure 1 ), the correlation between SU / SD and methylation level of base fragments unrelated to CGN and NCG is 0.970 ( Figure 2 ), the correlation between SU / SD of CGNNCG and methylation level was 0.940 ( Figure 3 ). When performing differential analysis between high-methylation intervals and low-methylation intervals, the chromosome locations of high-methylation intervals and low-methylation intervals are different. The results showed that: (1) the base sequences with significant frequency differences were caused by methylation rather than the different chromosomal positions between high-methylation intervals and low-methylation intervals, and the base sequences were related to methylation. (2) The correlation between the SU / SD of base fragments unrelated to CGN and NCG and the methylation level was higher than that of CGNNCG.

[0088] Example 4 Evaluation of the performance of markers for cancer prediction on the whole genome 58 healthy plasma samples and 40 cancer plasma samples (10 gastric cancer, 10 breast cancer, 10 colon cancer and 10 bile duct cancer) in Example 2 and Example 3 were used as training sets, and another 87 healthy plasma samples and 60 cancer plasma samples (15 gastric cancer, 15 breast cancer, 15 colon cancer and 15 bile duct cancer) were used as test sets to verify the performance of methylation-related base fragments as markers for early cancer screening. The specific steps are as follows: (1) Low-depth whole-genome sequencing data of 245 plasma samples was performed, with a sequencing depth of 1~2X. The obtained fastq was filtered and aligned with the human genome using the software BWA. The alignment files were sorted using the software samtools to obtain the sorted alignment files. The sorted alignment files were deduplicated using the software picard to obtain the deduplicated alignment files, remove alignment information with low alignment quality, and remove alignment information aligned to chromosomes X and Y. Extract the position information of cfDNA molecules aligned to chromosomes and convert it into bed format; (2) The location of the 5' end of the extracted cfDNA fragment on the chromosome; (3) According to the positions described in step (2), the intervals are 0 bp, 1 bp, 2 bp, 3 bp, 4 bp and 5 bp respectively. bp downstream (from 5' to 3' end), 1 bp, 2 bp, 3 bp and 4 bp are selected; according to the position described in step (2), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are selected upstream (from 3' to 5' end); according to the position described in step (2), 1 bp, 2 bp, 3 bp and 4 bp are selected upstream and downstream, respectively; (4) calculating the frequencies of various base sequences in step (3); (5) Extract the frequencies of the 100 base sequences in Table 2 in the training set as input values ​​to build the model; (6) The gbm algorithm is used to construct the model, where the parameters of the trainControl function are: "method = "repeatedcv",number = 10, repeats = 10, verboseIter =FALSE, savePredictions=TRUE,classProbs=TRUE,summaryFunction =twoClassSummary"; The parameters of the training model train function are: "method = 'gbm', tuneGrid=data.frame(n.trees=150, interaction.depth=3, shrinkage=0.1, n.minobsinnode=10), preProcess = c("corr", "nzv"))" (7) Extract the frequencies of the 100 base sequences in Table 2 in the test set and input them into the model constructed in step (6) to predict health and malignancy; (8) Extract the frequencies of 51 base sequences that have no direct relationship with CGN and NCG from the base sequences in Table 2 in the training set as input values, and build the model according to step (6); (9) Extract the frequencies of 59 base sequences that have no direct relationship with CGN and NCG from the base sequences in Table 2 in the test set and input them into the model constructed in step (8) to predict health and malignancy; (10) Extract the frequencies of eight base motifs, CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG, at the 5' end of the training set as input values, and construct the model according to step (6); (11) extracting the frequencies of eight base sequences, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG, at the 5' end of the test set and inputting them into the model constructed in step (10) to predict health and malignancy; The results showed that in the test set, when all methylation-related base sequences were used as markers for prediction, the AUC was 0.97, when 51 base sequences that were not directly related to CGN and NCG were used as markers for prediction, the AUC was 0.98, and when 8 base sequences of 5' end CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG were used as markers for prediction, the AUC was 0.90, and the performance was significantly improved. Figure 5 shown.

[0089] Example 5 Evaluation of the performance of markers for cancer prediction in CpG island and CpG shore intervals The performance of the markers in cancer prediction at the CpG island and CpG shore levels was evaluated using the training set and test set in Example 3. The specific steps are as follows: (1) Low-depth whole-genome sequencing data of 245 plasma samples was performed, with a sequencing depth of 1~2X. The obtained fastq was filtered and aligned with the human genome using the software BWA. The alignment files were sorted using the software samtools to obtain the sorted alignment files. The sorted alignment files were deduplicated using the software picard to obtain the deduplicated alignment files, remove alignment information with low alignment quality, and remove alignment information aligned to chromosomes X and Y. Extract the position information of cfDNA molecules aligned to chromosomes and convert it into bed format; (2) using the software bedtools, the location information of the cfDNA molecules of the samples described in step (1) on the chromosome was compared with the location information of the CpGisland and CpGshore intervals, and the cfDNA fragments of 245 samples located in the CpGisland and CpGshore intervals were obtained; (3) The location of the 5' end of the cfDNA fragment extracted in step (2) on the chromosome; (4) according to the position described in step (3), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken downstream (from 5' to 3'); according to the position described in step (3), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken upstream (from 3' to 5'); according to the position described in step (3), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken upstream (from 3' to 5'); according to the position described in step (3), 1 bp, 2 bp, 3 bp and 4 bp are respectively taken upstream and downstream; (5) calculating the frequencies of the various base sequences described in step (4); (6) Extracting the frequencies of the 100 base sequences in Table 2 in the training set as input values ​​and constructing a model according to step (6) in Example 4; (7) Extract the frequencies of the 100 base sequences in Table 2 in the test set and input them into the model constructed in step (6) to predict health and malignancy; (8) Extract the frequencies of 51 base sequences that have no direct relationship with CGN and NCG from the base sequences in Table 2 in the training set as input values, and construct the model according to step (6) in Example 4; (9) Extract the frequencies of 51 base sequences that have no direct relationship with CGN and NCG from the base sequences in Table 2 in the test set and input them into the model constructed in step (8) to predict health and malignancy; (10) extracting the frequencies of eight base motifs, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG at the 5' end in the training set as input values, and constructing a model according to step (6) in Example 4; (11) The frequencies of eight base sequences, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG, at the 5' end of the test set were extracted and input into the model constructed in step (10) to predict health and malignancy.

[0090] The results showed that in the test set, when all methylation-related base sequences were used as markers for prediction, the AUC was 0.95, when 59 base sequences that were not directly related to CGN and NCG were used as markers for prediction, the AUC was 0.96, and when 8 base sequences of 5' end CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG were used as markers for prediction, the AUC was 0.77, and the performance was significantly improved. Figure 6 shown.

[0091] Example 6 Evaluation of the performance of markers for cancer prediction in the CpG island interval The performance of the markers in cancer prediction on the CpG island interval was evaluated using the training set and test set in Example 4. The specific steps are as follows: (1) Low-depth whole-genome sequencing data of 245 plasma samples was performed, with a sequencing depth of 1~2X. The obtained fastq was filtered and aligned with the human genome using the software BWA. The alignment file was sorted using the software samtools to obtain the sorted alignment file. The sorted alignment file was deduplicated using the software picard to obtain a deduplicated alignment file, remove alignment information with low alignment quality, and remove alignment information aligned to chromosomes X and Y. Extract the position information of cfDNA molecules aligned to chromosomes and convert it into bed format; (2) using the bedtools software, the location information of the cfDNA molecules of the samples described in step (1) on the chromosome was compared with the location information of the CpG island interval, and the cfDNA fragments of 245 samples located in the CpG island interval were obtained; (3) The location of the 5' end of the cfDNA fragment extracted in step (2) on the chromosome; (4) according to the position described in step (3), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken downstream (from 5' to 3'); according to the position described in step (3), 1 bp, 2 bp, 3 bp and 4 bp are respectively separated and taken upstream (from 3' to 5'); according to the position described in step (3), 1 bp, 2 bp, 3 bp, 4 bp and 5 bp are respectively separated and taken upstream (from 3' to 5'); according to the position described in step (3), 1 bp, 2 bp, 3 bp and 4 bp are respectively taken upstream and downstream; (5) calculating the frequencies of the various base sequences described in step (4); (6) Extracting the frequencies of the 100 base sequences in Table 2 in the training set as input values, and constructing a model according to step (6) in Example 4; (7) Extract the frequencies of the 100 base sequences in Table 2 in the test set and input them into the model constructed in step (6) to predict health and malignancy; (8) Extract the frequencies of 59 base sequences that have no direct relationship with CGN and NCG from the base sequences in Table 2 in the training set as input values, and construct a model according to step (6) in Example 4; (9) Extract the frequencies of 59 base sequences that have no direct relationship with CGN and NCG from the base sequences in Table 2 in the test set and input them into the model constructed in step (8) to predict health and malignancy; (10) extracting the frequencies of eight base motifs, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG at the 5' end in the training set as input values, and constructing a model according to step (6) in Example 4; (11) The frequencies of eight base sequences, namely CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG, at the 5' end of the test set were extracted and input into the model constructed in step (10) to predict health and malignancy.

[0092] The results showed that in the test set, when all methylation-related base sequences were used as markers for prediction, the AUC was 0.91. When 51 base sequences that were not directly related to CGN and NCG were used as markers for prediction, the AUC was 0.91. When 8 base sequences including 5' end CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG were used as markers for prediction, the AUC was 0.74. The performance was significantly improved. Figure 7 shown.

[0093] From Example 4 to Example 6, it can be seen that the performance of 59 methylation-related base sequences as markers for cancer prediction is significantly improved compared with the prior art CGNNCG.

[0094] Example 7 The results of marker determination on the whole genome, CpG island, CpG shore and CpG island In this embodiment, the inventors used 59 base sequences of the present invention, 59 base sequences and 41 base sequences directly related to CGN, NCG (100 base sequences) and 8 CGNNCG base sequences to judge the plasma sample data of 3 individuals randomly selected from the test set (sample 001, sample 002 and sample 003) on the whole genome, CpG island (CpG island) and CpG shore (CpG shore) and CpG island (CpG island). When the specificity is 0.95, the judgment results are shown in Table 5, Table 6 and Table 7 respectively.

[0095] Table 5 Judgment results of 59 base sequences

[0096] Table 6 Judgment results of 100 base sequences

[0097] Table 7 Judgment results of 8 base sequences of CGNNCG

[0098] From the results of Tables 5-7 above, it can be seen that the 59 base sequences of the present invention, 59 base sequences and 41 base sequences directly related to CGN and NCG (100 base sequences) all have very good judgment accuracy, and can give correct judgment results on the whole genome, CpG island, CpG shore and CpG island. The 8 CGNNCG base sequences have good judgment results on the whole genome, the judgment results on CpG island and CpG shore are second, and the judgment results on CpG island are poor.

[0099] It can be seen that the 59 base sequences of the present invention (sequences coded as A1 to A59) have a very good cancer risk assessment effect.

[0100] The existing "CGNNCG modif ratio" method infers and verifies that the shearing ratio of methylated CG at the CG position is higher than that of unmethylated CG by counting the differences in the shearing ratio of methylated and unmethylated CpG sites and their two end bases in the sample, and the shearing ratio at the position 1bp before the methylated CG is lower than that of unmethylated CG. Therefore, 5' end CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG are used to characterize the methylation state, and since the methylation state of the sample is closely related to cancer, these 8 base sequences can be used to predict cancer. It is well known that biological physiological activity is complex, but this method only extracts 8 base sequences related to the 5' end of CpG, and does not analyze and verify the correlation between other base sequences at the end of cfDNA and methylation.

[0101] “Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments” published in PANS PLUS in 2015 studied the methylation levels of 14 tissues (liver, lung, colon, small intestine, pancreas, adrenal gland, esophagus, adipose tissue, heart, brain, T cells, B cells, neutrophils, and placenta) in the CpG island and CpG shore intervals, divided the CpG island and CpG shore intervals into non-overlapping 500 bp units, and obtained 5820 units with significant differences in different tissues (cells) and the methylation levels of 14 tissues in each unit. The article "The origin of highly elevated cell-free DNA in healthy individuals and patients with pancreatic, colorectal, lung, or ovarian cancer" published in CancerDiscov in 2023 found that cfDNA in healthy people mainly comes from white blood cells, accounting for an average of 79%, of which neutrophils account for 66.7%, B cells and T cells account for 16.4 ± 12.0% and 17.5 ± 4.3%, respectively. The remaining cfDNA from the liver, colon, heart, brain and lungs account for 5.8 ± 6.6%, 4.0 ± 7.1%, 3.5 ± 3.4%, 3.1 ± 3.3% and 2.4 ± 3.4% of all cfDNA, respectively. Therefore, we calculated the methylation level of cfDNA in healthy people based on the methylation level of tissues (cells) and the proportion of different cells in healthy people, obtained chromosome intervals with different methylation levels, and compared the differences in base sequences in low methylation level intervals and high methylation level intervals to obtain base sequences that can predict methylation levels.

[0102] First, the inventors evaluated the correlation between the gold standard methylation level of healthy people obtained by whole genome methylation sequencing and the methylation level calculated according to the tissue methylation level and the proportion of healthy human cells. The results showed that the Pearson correlation coefficient was 0.998, indicating that the methylation level calculated according to the tissue (cell) methylation level and the proportion of healthy human cells can be used to characterize the true methylation level. Then the inventors traversed the various forms of base sequences at the end of the cfDNA fragments, and compared the frequency differences between the high methylation level chromosome intervals and the low methylation level chromosome intervals of 28 healthy people, and selected the top 100 base sequences with significant differences, of which 41 were directly related to the 5' end CGN and NCG. Then, another 30 healthy human plasma samples were used to prove the strong correlation between the frequency of significantly different base sequences and the methylation level. 60 cancer plasma samples and 87 healthy human plasma samples were used as test sets for verification.

[0103] The results showed that: at the whole genome level, the frequencies of the 100 base sequences shown in Table 1 were used as input values ​​to construct the model, and the AUC was 0.97; after removing the 41 base sequences related to CGN and NCG, the frequencies of the remaining 59 base sequences were used as input values ​​to construct the model, and the AUC was 0.98; the frequencies of CGA, CGC, CGT, CGG, ACG, CCG, CGT and GCG were used as input values ​​to construct the model, and the AUC was 0.90.

[0104] All documents mentioned in the present invention are cited as references in this application, just as each document is cited as reference individually. In addition, it should be understood that after reading the above teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the claims attached to this application.

Claims

1. A use of a methylation gene marker or a detection reagent thereof, characterized in that: Used for preparing a detection reagent or a kit, wherein the detection reagent or the kit is used for detecting (a) methylation level; and / or (b) cancer risk; Wherein, the methylation gene marker is a set of base sequences obtained by taking n bp upstream, downstream, or upstream and downstream of the 5' end breakpoint with an interval of m bp when cfDNA is aligned to the reference genome; m is any integer from 0 to 5, and n is any integer from 1 to 4; The methylation gene markers include a set of base sequences coded as A1 to A59 in the following table: 。 2. The use according to claim 1, characterized in that The methylation gene markers also include a set of base sequences coded as B1 to B41 in the following table: 。 3. The use according to claim 1, characterized in that The cancer is selected from the group consisting of gastric cancer, breast cancer, colon cancer, bile duct cancer, glioma, sarcoma, epithelial cancer, lymphoma, melanoma, fibroma, meningioma, brain cancer, kidney cancer, thyroid cancer, parathyroid cancer, pituitary tumor, adrenal tumor, osteogenic sarcoma tumor, neuroendocrine system tumor, lung cancer, head and neck cancer, prostate cancer, esophageal cancer, tracheal cancer, liver cancer, bladder cancer, pancreatic cancer, ovarian cancer, uterine cancer, cervical cancer, testicular cancer, skin cancer, or a combination thereof.

4. A method for constructing a cancer risk prediction model, characterized in that: The steps include: (s1) providing a first data set for model training and a second data set for model testing, wherein the first data set includes methylation gene marker data of a cancer positive sample group and methylation gene marker data of a cancer negative sample group; The second data set includes methylation gene marker data of a cancer positive sample group and methylation gene marker data of a cancer negative sample group; and (s2) constructing a cancer risk prediction model using a machine learning method based on the data in the first data set, and verifying it on the second data set, thereby obtaining the cancer risk prediction model.

5. The method according to claim 4, characterized in that The methylation gene marker data is the frequency of the methylation gene marker; the frequency of the methylation gene marker is the proportion of the cfDNA fragments corresponding to the methylation gene marker in the sample to all the cfDNA fragments.

6. The method according to claim 4, characterized in that The methylation gene marker data is obtained by the following steps: (i) extracting cfDNA from the sample to be tested and obtaining read data; (ii) aligning the read data to a reference genome to obtain the position of the 5' end of the read on the reference genome; (iii) according to the position of the 5' end of the read segment on the reference genome, from the 5' end breakpoint upstream, downstream, or upstream and downstream, at intervals of m bp, take n bp to obtain a base sequence, thereby obtaining a set of base sequences coded A1 to A59 in the following table: ； and (iv) Counting the proportion of cfDNA fragments corresponding to each base sequence in the base sequence set in all cfDNA fragments, thereby obtaining the frequency of each base sequence, thereby obtaining the methylation gene marker data.

7. A cancer risk prediction device, characterized in that: The device comprises: (a) An input module, wherein the input module is configured to input methylation gene marker data in a sample to be tested; the methylation gene marker comprises a set of base sequences coded as A1 to A59 in the following table: ； (b) a risk assessment module, wherein the risk assessment module is configured to input the input methylation gene marker data into the cancer risk prediction model constructed according to the method of claim 4 to perform risk assessment, thereby obtaining a risk assessment result: (3) an output module, wherein the output module is configured to output the cancer risk assessment result.

8. A collection of methylation gene markers, characterized in that: The methylation gene marker set is a set of base sequences obtained by taking n bp upstream, downstream, or upstream and downstream of the 5' end breakpoint with an interval of m bp when cfDNA is aligned to the reference genome; The methylation gene marker set includes a set of base sequences coded A1 to A59 in the following table: 。 9. A computer storage device, characterized in that: The device stores a computer program corresponding to the algorithm of the cancer risk prediction model constructed according to the method of claim 4.

10. A method for screening methylation gene markers for predicting methylation levels, characterized in that: Includes steps: (i) Obtaining high-methylation intervals and low-methylation intervals in chromosomes of healthy people; (ii) extracting cfDNA from healthy human samples and obtaining cfDNA read data; (iii) aligning the cfDNA read data to a reference genome to obtain the position of the 5' end of the read on the reference genome; and obtaining the cfDNA read located in the high methylation interval and the cfDNA read located in the low methylation interval respectively; (iv) according to the position of the 5' end of the read segment in step (iii) on the reference genome, from the 5' end breakpoint upstream, downstream, and upstream and downstream respectively, at intervals of m bp, take n bp to obtain the base sequence; wherein m is any integer from 0 to 5, and n is any integer from 1 to 4; (v) counting the proportion of cfDNA fragments corresponding to each base sequence in the high methylation interval in all cfDNA fragments, thereby obtaining the frequency of each base sequence in the high methylation interval; counting the proportion of cfDNA fragments corresponding to each base sequence in the low methylation interval in all cfDNA fragments, thereby obtaining the frequency of each base sequence in the low methylation interval; (vi) Count the pvalues ​​of each base sequence whose frequency in the high methylation interval is higher than that in the low methylation interval, perform multiple hypothesis verification, and select base sequences whose Qvalues ​​are less than a certain threshold; Count the pvalues ​​of each base sequence whose frequency in the high methylation interval is lower than that in the low methylation interval, perform multiple hypothesis verification, and select base sequences whose qvalues ​​are less than a certain threshold; and (vii) Using the Relief method to filter and screen the base sequence in step (vi), and selecting the base sequence with the largest Relief value, thereby obtaining a methylation gene marker.

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