Method, system, device and medium for screening marker for cancer diagnosis on basis of methylated cfdna fragment

Abstract

A method, a system, a device and a medium for screening a marker for cancer diagnosis on the basis of a methylated cfDNA fragment, which belongs to the technical fields of cancer marker screening and application. The method and the system can simultaneously identify a genomic window feature, a fragment feature and a terminal sequence feature, providing a new biomarker and target for disease diagnosis and treatment, and thus have a broad application prospect.

Description

Methods, systems, devices, and media for screening markers for diagnosing cancer based on methylated cfDNA fragments TECHNICAL FIELD

[0001] The present application relates to the technical field of cancer marker screening and application, in particular, relates to a method, system, device and medium for screening markers for diagnosing cancer based on methylated cfDNA fragments. BACKGROUND

[0002] With the development of biomedical research, the demand for detection of DNA methylation level, insert size and end sequence in the field of tumor liquid biopsy is increasingly urgent. DNA methylation level refers to the addition and removal of methyl groups on DNA molecules, which plays an important role in gene expression regulation, cell differentiation and disease development. Insert size generally refers to the size of DNA fragments obtained by breaking DNA molecules in samples using ultrasonic or enzyme cutting technology in library construction of second-generation sequencing, and end sequence refers to a certain length of sequence at both ends of DNA molecules.

[0003] At present, existing DNA methylation detection methods include WGBS and RRBS and other bisulfite conversion sequencing. However, there are two serious shortcomings of bisulfite conversion DNA, which are great DNA loss and destruction of the original DNA fragment structure. The cfDNA bisulfite conversion sequencing data cannot detect the insert size and end sequence, therefore, the current insert size distribution and end sequence distribution are generally detected using WGS sequencing data, which leads to a tedious detection process. SUMMARY

[0004] To solve the above technical problems, the technical scheme adopted by the present application is as follows:

[0005] The first aspect of the present application provides a method for screening markers for diagnosing cancer based on methylated cfDNA fragments, comprising the following steps:

[0006] S1, obtaining fragment sequence information and methylation information of cfDNA samples of cancer patients and normal people, the methylation information including methylation position and methylation level;

[0007] S2, analyzing the fragment sequence information and methylation information obtained in step S1 to obtain a candidate marker level spectrum as follows:

[0008] (1) dividing the human whole genome according to length to obtain different genomic windows, counting the number of methylated cfDNA fragments mapped to each genomic window, and obtaining a genomic window methylation level spectrum based on the methylation information;

[0009] (2) Based on the number of methylation cfDNA fragments of different lengths, the frequency of different step length fragment features is obtained, and a fragment feature frequency spectrum is obtained;

[0010] (3) The distribution frequency of the 5' end 4mer~6mer of the methylation cfDNA fragment is obtained, and a terminal sequence frequency spectrum is obtained,

[0011] Therefore, the candidate marker spectrum includes the genomic window methylation level spectrum, the fragment feature frequency spectrum, and the terminal sequence frequency spectrum,

[0012] S3, based on the candidate marker level spectrum obtained in step S2, screening the candidate markers with significant differences between cancer patients and normal people, i.e. obtaining markers for diagnosing cancer, including genomic windows, fragment features, and terminal sequences.

[0013] In the present application, cfDNA is the full name of "cell-free DNA", which is cell-free DNA, and the sources include but are not limited to peripheral blood, cerebrospinal fluid, saliva, pleural fluid, ascites, urine and feces.

[0014] In some embodiments of the present application, the fragment sequence information and methylation information are first obtained by using methylation enrichment protein enrichment, immunoprecipitation or enzyme conversion method to obtain the methylation cfDNA fragments of the sample to be tested, and then sequenced. This method can well preserve the fragment characteristics of cfDNA while capturing the methylation cfDNA, so as to detect the cfDNA methylation level, insert size and terminal sequence at the same time subsequently.

[0015] In some embodiments of the present application, between step S1 and step S2, a step of pre-processing the fragment information is further included, and the pre-processing includes:

[0016] (1) Removing the adapter sequence;

[0017] (2) Filtering low-quality sequences with a base quality value below Q15 of more than 40%;

[0018] (3) Filtering sequences containing more than 5 N;

[0019] (4) Filtering sequences with a sequence length less than 30 (too short sequence);

[0020] (5) Trimming 4 bases of the fragment end with an average quality <Q20.

[0021] In some embodiments of the present application, a methylation standard and an unmethylation standard are added to both the cfDNA sample of the cancer patient and the cfDNA sample of the normal person. In some specific embodiments of the present application, the methylation standard is fully methylated positive lambda DNA; the unmethylation standard refers to fully unmethylated negative lambda DNA.

[0022] Further, the base sequences of the cleaned sequencing data are respectively aligned to the human reference genome hg19 (GRCH37) and the lambda DNA reference genome using bowtie2-2.3.4.2 software to generate bam files, and the bam files are sorted according to the genome coordinates, the sorted bam is deduplicated using picard MarkDuplicates-2.18.25-SNAPSHOT, and finally the reads that are both aligned to the reference genome and have MAPQ>20 are screened.

[0023] According to the alignment results of the fully methylated positive lambda DNA and the fully unmethylated negative lambda DNA, the reaction specificity rate is calculated. Preferably, in step S1, the methylation sequencing data with an effective sequencing data amount greater than a first preset threshold and a reaction specificity rate greater than a second preset threshold are selected, wherein the first preset threshold is 4G-9G, and the second preset threshold is 0.7-0.9. If no methylation sequencing data of the cfDNA sample of the cancer patient or the cfDNA sample of the normal person meets the requirement, the library construction and sequencing are performed again, or the sample is obtained again for library construction and sequencing.

[0024] In some embodiments of the present application, in step S2, the genomic window methylation level profile is based on the following steps:

[0025] The human whole genome is divided into different genomic windows according to the length, and based on the methylation sequencing data, for each genomic window, the number of methylated cfDNA fragments in the genomic window is calculated, and the methylation level in the genomic window is obtained by standardization.

[0026] In some specific embodiments of the present application, the standardized methylation level is obtained by using the following formula:

[0027] 。

[0028] Wherein, the total number of methylated cfDNA fragments is in millions, and the length of the genomic window is in kb.

[0029] In some embodiments of the present application, the genome is divided into 10318991 windows with a window size of 300 bp. Those skilled in the art can choose different window sizes, or perform an exhaustive search within the range of 1- genome size.

[0030] In the present application, the "fragment feature" is also referred to as "insert size feature", which refers to dividing the methylated cfDNA fragments into different fragment intervals according to different lengths (step size). In some embodiments of the present application, the step size is 2 bp-10 bp. For example, if the step size is 2 bp, the fragment intervals are 61-62 bp, 63-64 bp, …, 399-400 bp; if the step size is 3 bp, the fragment intervals are 61-63 bp, 64-66 bp, …, 397-399 bp; if the step size is 10 bp, the fragment intervals are 61-70 bp, 71-80 bp, …, 391-400 bp. All methylated cfDNA fragments included in each fragment interval are defined as a fragment feature. For example, the fragment feature is 61-65 bp, including methylated cfDNA fragments with lengths of 61 bp, 62 bp, 63 bp, 64 bp and 65 bp. For another example, the fragment feature is 74-75 bp, including methylated cfDNA fragments with lengths of 74 bp and 75 bp.

[0031] The fragment feature frequency refers to the proportion of the number of methylated cfDNA fragments in a fragment feature to the total number of methylated cfDNA fragments.

[0032] The end sequence frequency refers to the proportion of the number of methylated cfDNA fragments with the same end sequence to the total number of methylated cfDNA fragments.

[0033] In the present application, the distribution frequency of 5' end 4mer (such as CCGT, AGTT, etc.) and 6mer (such as CCGATC, TCGGAT, etc.) end sequences of methylated cfDNA fragments is obtained.

[0034] In some embodiments of the present application, in step S3, the candidate markers with significant differences between cancer patients and normal persons are obtained by statistical or machine learning methods.

[0035] In some embodiments of the present application, in step S3, the machine learning method includes logistic regression, decision tree, random forest, support vector machine, naive Bayes, K-nearest neighbor and neural network.

[0036] The second aspect of the present application provides a system for screening markers for diagnosing cancer based on methylated cfDNA fragments, comprising:

[0037] a methylation data input module, configured to receive fragment sequence information and methylation information of a cfDNA sample of a cancer patient and a cfDNA sample of a normal person;

[0038] a genomic data storage module, configured to store human genomic data;

[0039] a data comparison module, connected with the methylation data input module and the genomic data storage module respectively, configured to compare the fragment information and the methylation information with the human genomic data;

[0040] an analysis module, connected with the data comparison module, configured to obtain a candidate marker level spectrum by performing the following analysis:

[0041] (1) dividing the whole human genome according to length to obtain different genomic windows, counting the number of methylation fragments aligned to each genomic window, and obtaining a genomic window methylation level spectrum based on the methylation information;

[0042] (2) obtaining the frequency of different step length fragment features based on the number of methylation cfDNA fragments of different lengths, to obtain a fragment feature frequency spectrum;

[0043] (3) obtaining the distribution frequency of 5' end 4mer~6mer terminal sequences of methylation cfDNA fragments, to obtain a terminal sequence frequency spectrum,

[0044] a screening module, connected with the analysis module, configured to screen candidate markers with significant differences between cancer patients and normal persons based on the candidate marker level spectrum.

[0045] In some embodiments of the present application, the methylation data input module is further configured to pre-process the fragment sequence information, and the pre-processing includes:

[0046] (1) removing adapter sequences;

[0047] (2) filtering low-quality sequences with a quality value of more than 40% of bases lower than Q15;

[0048] (3) filtering sequences containing more than 5 Ns;

[0049] (4) filtering sequences with a length less than 30 (too short sequences);

[0050] (5) trimming 4 bases at the end of fragments with an average quality lower than Q20.

[0051] The third aspect of the present application provides a marker combination for diagnosing cancer, comprising markers screened by the method of any one of the first aspect of the present application or the system of any one of the second aspect of the present application.

[0052] The fourth aspect of the present application provides another marker combination for diagnosing cancer, comprising:

[0053] (1) at least one of the genomic window combinations consisting of chr15:31775701-31776000, chr7:32467501-32467800, chr8:24771301-24771600;

[0054] (2) at least one of the fragment feature combinations consisting of 165-166bp, 163-165bp, 167-168bp; and

[0055] (3) at least one of the end sequence combinations consisting of ATGGGG, ATAGGC, ATGAGG,

[0056] Preferably, the cancer is colorectal cancer;

[0057] or comprising:

[0058] (1) at least one of the genomic window combinations consisting of chr7:32467501-32467800, chr7:32467801-32468100 and chr20:21376801-21377100;

[0059] (2) at least one of the fragment feature combinations consisting of 149-150bp, 151-152bp and 153-154bp; and

[0060] (3) at least one of the end sequence combinations consisting of ATGAGC, ATGG, ATAGCG,

[0061] Preferably, the cancer is liver cancer;

[0062] or comprising:

[0063] (1) at least one of the genomic window combinations consisting of chr20:43726801-43727100, chr6:107955901-107956200 and chr19:19650901-19651200;

[0064] (2) at least one of the fragment feature combinations consisting of 279-280bp, 277-279bp and 280-282bp; and

[0065] (3) at least one of the end sequence combinations consisting of CTAGGG, TTCAGC, ATAGGC,

[0066] Preferably, the cancer is pancreatic cancer;

[0067] or comprises:

[0068] (1) at least one of the genomic window combinations consisting of chr7:32467501-32467800, chr6:107955901-107956200 and chr2:39187201-39187500;

[0069] (2) at least one of the fragment feature combinations consisting of 163-164bp, 165-166bp and 156-160bp; and

[0070] (3) at least one of the terminal sequence combinations consisting of AGGGAG, AGGGGA, TGAAAC,

[0071] Preferably, the cancer is gastric cancer.

[0072] or comprises:

[0073] (1) at least one of the genomic window combinations consisting of chr3:51740701-51741000, chr9:140128201-140128500 and chr17:7670401-7670700;

[0074] (2) at least one of the fragment feature combinations consisting of 267-268bp, 259-260bp and 268-270bp; and

[0075] (3) at least one of the terminal sequence combinations consisting of GGGAAC, GGGAGT, GGGAAG,

[0076] Preferably, the cancer is lung cancer.

[0077] The fifth aspect of the present application provides use of the detection reagent and / or device of the marker combination of the third aspect or the fourth aspect of the present application in the preparation of a kit for diagnosing cancer.

[0078] The sixth aspect of the present application provides a computer device, comprising:

[0079] a memory for storing a computer program;

[0080] a processor for executing the computer program to implement the steps of the method for screening a marker for diagnosing cancer based on methylated cfDNA fragments according to the first aspect of the present application.

[0081] The seventh aspect of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method for screening markers for diagnosing cancer based on methylated cfDNA fragments according to the first aspect of the present application.

[0082] The eighth aspect of the present application provides a system for diagnosing cancer, comprising the following modules:

[0083] a marker data input module, configured to input the level of each marker in the marker combination according to the third aspect or the fourth aspect of the present application;

[0084] a cancer judging module, connected to the data input module, configured to judge whether the subject has cancer or has the risk of cancer according to the level of each marker in the marker combination.

[0085] In some embodiments of the present application, the marker combination comprises genomic windows, fragment features and / or terminal sequences.

[0086] When the marker is a genomic window, the level thereof refers to the methylation level, which is obtained based on the number of methylated cfDNA fragments aligned to the genomic window.

[0087] When the marker is a fragment feature, the level thereof refers to the fragment feature frequency.

[0088] When the marker is a terminal sequence, the level thereof refers to the terminal sequence frequency.

[0089] Further, the present application provides a computer readable medium, which stores computer program instructions, wherein the computer program instructions are executed by a processor to run any of the above-mentioned methods of the present application.

[0090] Still further, the present application provides an apparatus, which comprises:

[0091] a memory for storing computer program instructions, and a processor for executing the computer program instructions, wherein the apparatus runs any of the above-mentioned methods of the present application when the computer program instructions are executed by the processor.

[0092] In the present application, the cancer includes solid tumors and blood cancers, such as rectal cancer, pancreatic cancer, prostate cancer, squamous cell carcinoma, basal cell carcinoma, adenocarcinoma, sweat gland carcinoma, sebaceous gland carcinoma, papillary carcinoma, papillary adenocarcinoma, cystadenocarcinoma, medullary carcinoma, bronchogenic carcinoma, hepatocellular carcinoma, cholangiocarcinoma, choriocarcinoma, kidney cancer, cervical cancer, testicular cancer, lung cancer, melanoma; leukemia, such as acute lymphoblastic leukemia and acute myeloblastic leukemia (myeloblast, promyelocyte, myelomonocyte, monocyte and erythroid leukemia); chronic leukemia (chronic myelocytic (granulocytic) leukemia and chronic lymphocytic leukemia), etc.

[0093] Advantages of the present application

[0094] The method combines advanced methylation cfDNA sequencing technology and molecular biology methods, and has the characteristics of high throughput, high sensitivity and high accuracy.

[0095] In the method of the present application, the methylation cfDNA fragments of the sample to be tested are first obtained by protein enrichment, immunoprecipitation or enzyme conversion method. The cfDNA fragments that have been methylated are captured, and the fragment characteristics of the cfDNA are well preserved at the same time, so as to detect the cfDNA methylation level, the insert size and the end sequence at the same time subsequently. The present application also provides a corresponding system, which includes a methylation data input module, a genomic data storage module, a data comparison module, an analysis module and a screening module.

[0096] The method and system of the present application can simultaneously detect the cfDNA methylation level, the fragment characteristics and the end sequence, provide new biomarkers and targets for disease diagnosis and treatment, and have a wide application prospect.

[0097] BRIEF DESCRIPTION OF DRAWINGS

[0098] FIG. 1 shows a method flow diagram for screening markers for diagnosing cancer based on methylation cfDNA fragment sequencing in Example 1 of the present application.

[0099] FIG. 2 shows the distribution density of the insert size obtained by insert size analysis in Example 1 of the present application.

[0100] FIG. 3 shows a system diagram for screening markers for diagnosing cancer based on methylation cfDNA fragment sequencing in Example 1 of the present application.

[0101] Figure 4 shows the box plot of the distribution of the methylation level feature chrl5:31775701-31776000, the fragment feature 165-166bp and the terminal sequence feature ATGGGG which are the most enriched in colorectal cancer, and the ROC curve of the three features distinguishing colorectal cancer and normal samples. The AUC value of the methylation level feature chrl5:31775701-31776000 distinguishing colorectal cancer and normal samples is 0.845, the AUC value of the fragment feature 165-166bp distinguishing colorectal cancer and normal samples is 0.773, the AUC value of the terminal sequence feature ATGGGG distinguishing colorectal cancer and normal samples is 0.778, and the AUC value of the three features fused into one feature distinguishing colorectal cancer and normal samples is 0.87.

[0102] Figure 5 shows the box plot of the distribution of the methylation level feature chr7:32467501-32467800, the fragment feature 163-165bp and the terminal sequence feature ATAGGC which are the most enriched in colorectal cancer, and the ROC curve of the three features distinguishing colorectal cancer and normal samples. The AUC value of the methylation level feature chr7:32467501-32467800 distinguishing colorectal cancer and normal samples is 0.843, the AUC value of the fragment feature 163-165bp distinguishing colorectal cancer and normal samples is 0.763, the AUC value of the terminal sequence feature ATAGGC distinguishing colorectal cancer and normal samples is 0.776, and the AUC value of the three features fused into one feature distinguishing colorectal cancer and normal samples is 0.862.

[0103] Figure 6 shows the box plot of the distribution of the methylation level feature chr8:24771301-24771600, the fragment feature 167-168bp and the terminal sequence feature ATGAGG which are the most enriched in colorectal cancer, and the ROC curve of the three features distinguishing colorectal cancer and normal samples. The AUC value of the methylation level feature chr8:24771301-24771600 distinguishing colorectal cancer and normal samples is 0.826, the AUC value of the fragment feature 167-168bp distinguishing colorectal cancer and normal samples is 0.762, the AUC value of the terminal sequence feature ATGAGG distinguishing colorectal cancer and normal samples is 0.771, and the AUC value of the three features fused into one feature distinguishing colorectal cancer and normal samples is 0.870.

[0104] DETAILED DESCRIPTION

[0105] Unless otherwise indicated, all parts and percentages expressed herein are based upon weight and all tests and measurements are made at the date of filing of this application. To the extent that any patent, patent application or publication containing disclosu re is incorporated by reference into this application, the reference is only incorporated to the extent that the incorporated subject matter does not contradict the definitions, statements, or other disclosure of the present application. In the event of a contradiction, including definitions, the definitions provided herein prevail.

[0106] In order to make the technical problems, technical solutions and beneficial effects solved by the present application clearer, the present application is further described in detail below in combination with embodiments.

[0107] The following examples are presented to demonstrate preferred embodiments of the present application. Those skilled in the art will appreciate that the technology disclosed in the following examples represents the best attempts of the inventors to use the technology discovered to practice the present application, and therefore can be considered preferred modes for practicing the present application. However, those skilled in the art will further appreciate, in light of the present specification, that the particular examples disclosed herein can be modified in a variety of ways without departing from the spirit or scope of the present application.

[0108] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. References to the materials incorporated herein by reference are to be taken as incorporated herein by reference in their entirety.

[0109] Those of skill in the art will appreciate that many of the specific details of the technology described herein can be implemented in any of a variety of different technologies. Those of skill in the art will also appreciate that the technology described herein can be implemented in a variety of different technologies and that the technology described herein is not limited to any particular technology.

[0110] The experimental methods in the following examples are routine methods unless otherwise specified. The instruments and equipment used in the following examples are routine laboratory instruments and equipment unless otherwise specified. The test materials used in the following examples are commercially available from routine biochemical reagent stores unless otherwise specified.

[0111] Example 1 Screening of markers for diagnosing cancer based on methylated cfDNA fragments

[0112] In combination with FIG. 1, the present example elaborates on the method of screening markers for diagnosing cancer based on cfDNA methylation sequencing.

[0113] 1. Obtain methylation sequencing data

[0114] Take 10 ng of human peripheral blood cfDNA, 10 pg of fully methylated positive lambda DNA, and 10 pg of fully unmethylated negative lambda DNA for mixing, and perform sequencing by methylation protein enrichment, immunoprecipitation or enzyme conversion sequencing. The sequencing platform uses Illumina NovaSeq 6000.

[0115] After sequencing, the data is obtained.

[0116] 2. Data preprocessing

[0117] The following preprocessing is performed on the data using fastp-0.20.0 software:

[0118] (1) Remove adapter sequences;

[0119] (2) Filter low-quality sequences with more than 40% of base quality values below Q15;

[0120] (3) Filter sequences containing more than 5 Ns;

[0121] (4) Filter sequences with a length of less than 30 (too short sequences);

[0122] (5) Trim 4 bases of the end of the fragment with an average quality < Q20.

[0123] The data before and after preprocessing is shown in Table 1:

[0124] Table 1 Comparison of data before and after preprocessing

[0125]

[0126] The preprocessing results are shown in Table 2:

[0127] Table 2 Preprocessing results

[0128]

[0129] Next, the filtered sequence data is aligned with the human and lambda DNA reference genomes using the command alignment software bowtie2 (v 2.3.4.2), to obtain alignment files (SAM format), and samtools (v 1.3.1) sort is used to sort the SAM files according to the genome coordinates to generate BAM files, and then the samtools index command is used to build an index;

[0130] Then use the command picard (v 2.18.25) MarkDuplicates to remove PCR-generated duplicate sequences;

[0131] Further filter paired reads that are mapped to the reference genome and MAPQ>20 using command samtools view;

[0132] Finally, count the final mapping results, as shown in Table 3:

[0133] Table 3 Comparison results

[0134]

[0135] 3. Count the effective sequencing data amount and reaction specificity rate of the cfDNA of the sample to be tested

[0136] According to the total reads after mapping to the human reference genome, filtering, the effective sequencing data amount of the cfDNA of the sample (69495052x150 / 1024 / 1024 / 1000=9.941G) is calculated; according to the mapping results of the fully methylated positive lambda DNA and the fully unmethylated negative lambda DNA, the reaction specificity rate (544928 / (544928+53034)=0.911) is calculated, as shown in Table 4:

[0137] Table 4 Effective sequencing data amount of cfDNA

[0138]

[0139] 4. Analysis of insert size distribution

[0140] According to the effective data of the cfDNA of the sample to be tested, the insert size is analyzed; according to all the inserts after mapping to the human reference genome, filtering, the length of each insert is calculated, the distribution frequency of the inserts of different lengths is counted, part of the results are shown in Table 5, and the distribution density of the insert size is shown in Figure 2:

[0141] Table 5 Insert size distribution (part)

[0142]

[0143] Further, the cfDNA insert size of the sample to be tested is divided into different fragment intervals (e.g. 61-62bp, 63-64bp, …, 399-400bp if the step is 2bp; e.g. 61-63bp, 64-66bp, …, 397-399bp if the step is 3bp; e.g. 61-70bp, 71-80bp, …, 391-400bp if the step is 10bp) with a step of 2bp, 3bp, 4bp, 5bp, …, 10bp, and all cfDNA fragments in each fragment interval are defined as a fragment feature (insert size feature), and the proportion of the number of cfDNA fragments in each fragment feature to the total number of fragments is calculated to obtain a fragment feature spectrum. Some fragment feature ratios are shown in Table 6:

[0144] Table 6 Fragment feature distribution (part)

[0145]

[0146] 5. Terminal sequence analysis

[0147] According to the effective data of the cfDNA of the sample to be tested, the 4-6 base sequences at the 5' end of the insert fragments are extracted according to all the insert fragments after alignment to the human reference genome and de-duplication and screening, the distribution frequency of the 4mer and 6mer terminal sequences at the 5' end of the cfDNA is counted, and a terminal sequence feature spectrum is obtained. Some results are shown in Table 7:

[0148] Table 7 Terminal sequence distribution (part)

[0149]

[0150] 6. Methylation level analysis

[0151] According to the effective data of the cfDNA of the sample to be tested, the methylation level of any window including but not limited to 300bp of the human whole genome is counted, and a methylation level feature spectrum is obtained. This embodiment counts the methylation level of the 300bp window of the CpG island, as shown in Table 8:

[0152] Table 8 Methylation level of 300bp genomic window of CpG island

[0153]

[0154] Based on the above method, this embodiment also provides a system for screening markers for diagnosing cancer based on cfDNA methylation sequencing, as shown in FIG. 3, which comprises:

[0155] a methylation data input module, configured to receive methylation sequencing data of a cancer patient cfDNA sample and a normal person cfDNA sample and perform data preprocessing;

[0156] a genomic data storage module, configured to store human genomic data;

[0157] a data comparison module, connected with the methylation data input module and the genomic data storage module respectively, configured to compare the methylation sequencing data of the cancer patient cfDNA sample and the normal person cfDNA sample with the human genomic data;

[0158] an analysis module, connected with the data comparison module, configured to perform the following analysis to obtain a candidate marker level spectrum:

[0159] (1) obtaining the number of methylated cfDNA fragments in a 300bp genomic window to obtain a genomic window methylation level spectrum;

[0160] (2) obtaining the frequency of fragment features with a step of 2-10 based on the number of methylated cfDNA fragments of different lengths to obtain a fragment feature frequency spectrum;

[0161] (3) obtaining the distribution frequency of 5' end 4mer and 6mer terminal sequences of methylated cfDNA fragments to obtain a terminal sequence frequency spectrum,

[0162] a screening module, connected with the analysis module, configured to screen candidate markers with significant differences between cancer patients and normal persons based on the candidate marker level spectrum.

[0163] Example 2 Application of the marker for diagnosing cancer based on methylated cfDNA fragments

[0164] In order to verify the effectiveness of the method in Example 1 for tumor diagnosis marker identification, the inventors collected 181 colorectal cancer patient and 147 normal person blood samples, extracted peripheral blood cfDNA and obtained methylation sequencing data using the method of Example 1; then the sequencing data was preprocessed to obtain effective sequencing data, and further using the same method, the genomic window methylation level spectrum, the fragment feature frequency spectrum and the terminal sequence frequency spectrum of the sample were obtained respectively.

[0165] Using the wilcox rank sum test and the 5-fold cross-validation method to calculate the AUC value, the colorectal cancer specific enriched features were identified from the genomic window features, the fragment features and the terminal sequence features respectively. The results are shown in Table 9.

[0166] Table 9 Colorectal cancer specific enriched features

[0167]

[0168] As shown in Table 9, the genomic window feature chrl5:31775701-31776000, the fragment feature 165-166 bp and the terminal sequence feature ATGGGG have the highest AUC for distinguishing colorectal cancer and normal samples (as shown in Figure 4). Further, the inventors fused the three features into one feature using the average of the minimum-maximum normalization of the three features, and the AUC value for distinguishing colorectal cancer and normal samples was improved to 0.87, which is higher than the performance of using the three-dimensional features alone to distinguish colorectal cancer and normal samples (as shown in Figure 4).

[0169] Similarly, the genomic window feature chr7:32467501-32467800, the fragment feature 163-165 bp and the terminal sequence feature ATAGGC have the second highest AUC for distinguishing colorectal cancer and normal samples (as shown in Figure 5). Further, the inventors fused the three features into one feature using the average of the minimum-maximum normalization of the three features, and the AUC value for distinguishing colorectal cancer and normal samples was improved to 0.862, which is higher than the performance of using the three-dimensional features alone to distinguish colorectal cancer and normal samples (as shown in Figure 5).

[0170] Similarly, the genomic window feature chr8:24771301-24771600, the fragment feature 167-168 bp and the terminal sequence feature ATGAGG have the third highest AUC for distinguishing colorectal cancer and normal samples (as shown in Figure 6). Further, the inventors fused the three features into one feature using the average of the minimum-maximum normalization of the three features, and the AUC value for distinguishing colorectal cancer and normal samples was improved to 0.870, which is higher than the performance of using the three-dimensional features alone to distinguish colorectal cancer and normal samples (as shown in Figure 6).

[0171] Based on the above method, the embodiment also provides a system for diagnosing cancer, comprising the following modules:

[0172] a marker data input module for inputting the level of the feature;

[0173] a cancer judgment module connected with the data input module, for judging whether the subject has cancer or has the risk of suffering from cancer according to the level of the feature.

[0174] Example 3 Performance of the marker for diagnosing cancer based on methylated cfDNA fragments in other cancers

[0175] To further verify the effectiveness of the method in Example 1 for tumor diagnostic marker identification, the inventors collected blood samples from 50 liver cancer patients, 123 pancreatic cancer patients, 75 gastric cancer patients, 57 lung cancer patients and 142 healthy people, extracted the peripheral blood cfDNA and obtained methylation sequencing data using the method in Example 1; then the sequencing data was pre-processed to obtain effective sequencing data, and further using the same method, the genomic window methylation level spectrum, the fragment feature frequency spectrum and the end sequence frequency spectrum of the samples were obtained respectively.

[0176] Using the same calculation method in Example 2, liver cancer, pancreatic cancer, gastric cancer, lung cancer specific enrichment features were identified from the genomic window features, fragment features and end sequence features respectively. The results are shown in Tables 10-13.

[0177] Table 10 Liver cancer specific enrichment features

[0178]

[0179] Table 11 Pancreatic cancer specific enrichment features

[0180]

[0181] Table 12 Gastric cancer specific enrichment features

[0182]

[0183] Table 13 Lung cancer specific enrichment features

[0184]

[0185] All documents mentioned in this application are incorporated herein by reference. In addition, it is to be understood that various modifications can be made to the application as described above and such modifications, if within the scope of the application as defined by the appended claims, are to be considered equivalents.

Claims

1. A method of screening markers for diagnosing cancer based on methylated cfDNA fragments, characterized by, Comprising the following steps: S1, obtaining fragment sequence information and methylation information of a cancer patient cfDNA sample and a normal person cfDNA sample, the methylation information including methylation position and methylation level; S2, analyzing the fragment sequence information and methylation information obtained in step S1 to obtain a candidate marker level spectrum: (1) dividing the human whole genome according to length to obtain different genomic windows, counting the number of methylation cfDNA fragments aligned to each genomic window, and obtaining a genomic window methylation level spectrum based on the methylation information; (2) based on the number of methylation cfDNA fragments of different lengths, obtaining the frequency of different step length fragment characteristics, and obtaining a fragment characteristic frequency spectrum; (3) obtaining the distribution frequency of the 5' end 4mer~6mer terminal sequence of the methylation cfDNA fragment, and obtaining a terminal sequence frequency spectrum, Therefore, the candidate marker spectrum includes the genomic window methylation level spectrum, the fragment characteristic frequency spectrum and the terminal sequence frequency spectrum, S3, screening the candidate markers with significant differences between cancer patients and normal people based on the candidate marker level spectrum obtained in step S2, that is, obtaining markers for diagnosing cancer, including genomic windows, fragment characteristics and terminal sequences.

2. The method of claim 1, wherein, In step S2, the genomic window methylation level spectrum is obtained based on the following steps: Divide the human whole genome according to length to obtain different genomic windows, count the number of methylation cfDNA fragments in each genomic window, and standardize to obtain the methylation level of the genomic window.

3. The method of claim 1, wherein, In step S3, the candidate markers with significant differences between cancer patients and normal people are screened by statistical or machine learning methods.

4. The method of claim 3, wherein, In step S3, the machine learning method includes logistic regression, decision tree, random forest, support vector machine, naive Bayes, K nearest neighbor and neural network.

5. A system for screening markers for diagnosing cancer based on methylated cfDNA fragments, characterized by, Comprising: A methylation data input module for receiving fragment sequence information and methylation information of a cancer patient cfDNA sample and a normal person cfDNA sample; A genomic data storage module for storing human genome data; A data comparison module connected with the methylation data input module and the genomic data storage module respectively, for aligning the fragment sequence information and the methylation information with the human genome data; An analysis module connected with the data comparison module, for analyzing as follows to obtain a candidate marker level spectrum: (1) dividing the human whole genome according to length to obtain different genomic windows, counting the number of methylation fragments aligned to each genomic window, and obtaining a genomic window methylation level spectrum based on the methylation information; (2) based on the number of methylation cfDNA fragments of different lengths, obtaining the frequency of different step length fragment characteristics, and obtaining a fragment characteristic frequency spectrum; (3) obtaining the distribution frequency of the 5' end 4mer~6mer terminal sequence of the methylation cfDNA fragment, and obtaining a terminal sequence frequency spectrum, The screening module is connected with the analysis module, and is used for screening candidate markers with significant differences between cancer patients and normal people based on the candidate marker level spectrum.

6. A marker combination for diagnosing cancer, comprising markers screened by the method of any one of claims 1-4 or the system of claim 6.

7. A marker combination for diagnosing cancer, wherein, Optionally, the cancer is colorectal cancer, and the marker combination comprises: (1) at least one of the genomic window combination consisting of chr15:31775701-31776000, chr7:32467501-32467800, and chr8:24771301-24771600; (2) at least one of the fragment feature combination consisting of 165-166 bp, 163-165 bp, and 167-168 bp; and (3) at least one of the terminal sequence combination consisting of ATGGGG, ATAGGC, and ATGAGG, Optionally, the cancer is liver cancer, and the marker combination comprises: (1) at least one of the genomic window combination consisting of chr7:32467501-32467800, chr7:32467801-32468100, and chr20:21376801-21377100; (2) at least one of the fragment feature combination consisting of 149-150 bp, 151-152 bp, and 153-154 bp; and (3) at least one of the terminal sequence combination consisting of ATGAGC, ATGG, and ATAGCG, Optionally, the cancer is pancreatic cancer, and the marker combination comprises: (1) at least one of the genomic window combination consisting of chr20:43726801-43727100, chr6:107955901-107956200, and chr19:19650901-19651200; (2) at least one of the fragment feature combination consisting of 279-280 bp, 277-279 bp, and 280-282 bp; and (3) at least one of the terminal sequence combination consisting of CTAGGG, TTCAGC, and ATAGGC, Optionally, the cancer is gastric cancer, and the marker combination comprises: (1) at least one of the genomic window combination consisting of chr7:32467501-32467800, chr6:107955901-107956200, and chr2:39187201-39187500; (2) at least one of the fragment feature combination consisting of 163-164 bp, 165-166 bp, and 156-160 bp; and (3) at least one of the terminal sequence combination consisting of AGGGAG, AGGGGA, and TGAAAC, Optionally, the cancer is lung cancer, and the marker combination comprises: (1) at least one of the genomic window combinations consisting of chr3:51740701-51741000, chr9:140128201-140128500 and chr17:7670401-7670700; (2) at least one of the fragment feature combinations consisting of 267-268bp, 259-260bp and 268-270bp; and (3) at least one of the end sequence combinations consisting of GGGAAC, GGGAGT, GGGAAG.

8. A computer device, characterized by Comprising: a memory for storing a computer program; a processor for executing the computer program to implement the steps of a method for screening markers for diagnosing cancer based on methylated cfDNA fragments according to any one of claims 1-4.

9. A computer readable storage medium, characterized in that, a computer program is stored on the computer readable storage medium, and the computer program is executed by a processor to implement the steps of a method for screening markers for diagnosing cancer based on methylated cfDNA fragments according to any one of claims 1-4.

10. A system for diagnosing cancer, characterized by, comprising the following modules: a marker data input module for inputting the levels of each marker in the marker combination according to claim 7; a cancer judgment module connected with the data input module, for judging whether a subject has cancer or has the risk of having cancer according to the levels of each marker in the marker combination.

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