Comprehensive analysis method for searching peripheral nerve infringement DNA methylation site marker of colorectal cancer patient

Abstract

The invention discloses a comprehensive analysis method for searching DNA methylation site markers invaded by peripheral nerves of a colorectal cancer patient. The method comprises the following steps: collecting an original beta matrix and clinical features from a certain TCGA cancer species queue, and dividing the original beta matrix and clinical features into a PNI group and a non-PNI group; analyzing the methylation level of the genome DNA in the two groups; carrying out passage enrichment on the target genes of the genomic DNA methylation sites with significant difference; the methylation of the DNA sites of the differential genomes is analyzed; analyzing and detecting the positions of methylation difference sites in the two groups in a genome and a specific gene; analyzing the effects of the model under different penalty coefficients to obtain the most critical methylated genome DNA sites related to the PNI; calculating an area AUC under the curve; using the same risk score in the test set to verify the accuracy of the model; and statistical analysis. The DNA methylation significant difference site marker prepared by the method can be used for predicting whether peripheral nerve invasion occurs in the colorectal cancer or not, and is beneficial to deep research on the characteristics of the colorectal cancer.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and in particular relates to a comprehensive analysis method for finding DNA methylation site markers for peripheral nerve invasion in patients with colorectal cancer. Background Art

[0002] Colorectal cancer refers to cancer originating from the large intestine epithelium, including colon cancer and rectal cancer. Adenocarcinoma is the most common pathological type, and squamous cell carcinoma is a very rare type. In my country, rectal cancer is the most common, followed by colon cancer (sigmoid colon, cecum, ascending colon, etc.). The incidence of colorectal cancer has been gradually increasing in the past two decades, and at the same time, the age of onset tends to be aging. In developed Western countries, colorectal cancer is the second most common malignant tumor after lung cancer. The most common sites are the rectum and the junction of the rectum and the sigmoid colon. Colorectal cancer in young people should exclude pre-existing ulcerative colitis or familial colorectal cancer.

[0003] In recent years, colorectal cancer has shown a clear trend of younger patients, and surgery is still the main means of colorectal cancer treatment. However, studies have found that the prognosis of colorectal cancer patients is still not very ideal, mainly due to tumor recurrence and metastasis. As the most common biological behavior of malignant tumors, distant invasion has always been an important problem that plagues clinical workers. As one of the most common malignant tumors, the relationship between colorectal cancer and peripheral nerve invasion has also received attention. At present, peripheral nerve invasion as a new biological feature has also attracted more and more attention, but related research has just started, and there is no definitive marker for whether colorectal cancer patients will have peripheral nerve invasion and thus have a poor prognosis. DNA methylation is a form of DNA chemical modification that can change genetic expression without changing the DNA sequence. A large number of studies have shown that DNA methylation can cause changes in chromatin structure, DNA conformation, DNA stability, and the way DNA interacts with proteins, thereby controlling gene expression. Based on this, we used bioinformatics methods to establish a comprehensive analysis method for searching for genomic DNA methylation site markers of peripheral nerve invasion in patients with colorectal cancer, aiming to determine the risk factors for PNI in patients with colorectal cancer and to reveal the impact of PNI on the clinical progression of patients with colorectal cancer, so as to provide a basic theoretical basis for constructing a specific marker risk model and treatment. Summary of the invention

[0004] The present invention relates to the field of biomedical technology, and in particular, aims to find out whether a tumor will cause peripheral nerve invasion and use it in diagnosing whether a genomic DNA methylation site marker will be used.

[0005] The present invention is achieved through the following technical solutions:

[0006] The present invention first downloads the original beta matrix data and clinical characteristics from the cancer genome atlas TCGA, and divides them into a PNI group and a non-PNI group; then uses the R package Champ to analyze the genomic DNA with significant differences in methylation levels in the PNI group and the non-PNI group, and uses P<0.005 and the absolute value of delta_beta>0.1 as the screening of genomic DNA sites with significant differences in methylation levels; uses the R package ChIPseeker to perform GO and KEGG enrichment analysis on the target genes of the genomic DNA sites with significant differences in methylation levels; uses the R package ChIPseeker to analyze and detect the locations of the sites with significant methylation differences in the genomic DNA in the PNI group and the non-PNI group in the genome and specific genes; uses LASSO cross-validation to analyze the effects of the model under different penalty coefficients, selects the parameters with the highest accuracy to establish the model, and obtains the most critical genomic DNA sites with significant differences in methylation levels related to the PNI phenotype; uses the coefficient of each genomic DNA site with significant differences in methylation levels to calculate the risk score of the individual; uses ROC to calculate the area under the curve AUC to evaluate the model discrimination ability; and finally verifies the accuracy of the model in a test set.

[0007] Furthermore, the first aspect of the present invention provides a composition of genomic DNA sites with significantly different methylation levels related to whether a tumor will cause peripheral nerve invasion, characterized in that the composition of methylation sites related to peripheral nerve invasion of a tumor is composed of 11 genomic DNA sites with significantly different differentially expressed methylation levels, namely cg00048697, cg24404823, cg20161095, cg26827503, cg12198140, cg16461538, cg22635096, cg26462055, cg13947995, cg12421755, and cg16053651.

[0008] Furthermore, the tumor includes but is not limited to colorectal cancer.

[0009] Furthermore, the methylation level refers to the methylation level of a genomic DNA site with a significant difference in methylation level.

[0010] Furthermore, the method for determining the methylation level includes any one of PCR, gene chip, and second-generation high-throughput sequencing, or a combination of at least two of them.

[0011] Furthermore, this marker can be used for different groups of cancer patients. That is, for a new group, the sample size can be increased, the "prediction score" can be calculated, the threshold can be found, and on this basis, subsequent patients can be scored and whether nerve invasion occurs can be analyzed.

[0012] Furthermore, the detection product includes: probes and primers of a methylated DNA site marker composition for detecting whether the tumor has peripheral nerve invasion.

[0013] Furthermore, the detection product includes: probes and primers for genomic methylated DNA sites related to peripheral nerve invasion of tumors. The probes can bind to the genomic methylated DNA sites respectively through molecular hybridization to generate hybridization signals, and the primers can amplify the methylated DNA sequences related to peripheral nerve invasion with significant differences in methylation levels through PCR-based technology.

[0014] In addition to the primers or probes for the sequences of the methylated DNA sites, the detection product of the present invention may also contain tools or reagents recognized in the art for PCR reaction, gene chip and DNA methylation sequencing.

[0015] The third aspect of the present invention provides a composition of differential methylation modification of DNA associated with peripheral nerve invasion by tumors as described above, and the use of the detection product of differential methylation modification of DNA associated with peripheral nerve invasion by tumors in establishing a risk prediction system for predicting whether a tumor will cause peripheral nerve invasion.

[0016] The fourth aspect of the present invention provides a composition of differentially methylated DNA modified related to peripheral nerve invasion of tumors as described above, and the use of a detection product of the marker composition of differentially methylated DNA modified related to peripheral nerve invasion of tumors in the preparation of a product for predicting peripheral nerve invasion of tumors.

[0017] Furthermore, the method for determining the differential methylation modification of DNA includes any one of PCR, gene chip, and second-generation high-throughput sequencing, or a combination of at least two of them.

[0018] This model can be used for different groups of tumor patients, that is, for a new group, the "prediction score" can be calculated by increasing the sample volume, finding the middle value, and on this basis, scoring and typing the subsequent patients. The present invention protects the application of this formula to detect and calculate different groups and finally determine the value.

[0019] Compared with the prior art, the present invention has at least the following advantages:

[0020] The present invention established a marker model with 11 DNA differential methylation modifications related to peripheral nerve invasion, and divided colorectal cancer patients into PNI group and non-PNI group. GO and KEGG results showed that the target genes of differential DNA differential methylation modification sites were mainly related to synaptic transmission and ion channel activity. ROC curve analysis showed that the AUC was as high as 0.902. The prediction performance has been verified in the test set.

[0021] The analytical method for determining the combination of differential DNA methylation modification markers for peripheral nerve invasion in colorectal cancer proposed in the present invention provides a reference for the clinical treatment of colorectal cancer and can be used as a new type of DNA differential methylation modification biomarker and potential therapeutic target for predicting whether colorectal cancer will cause peripheral nerve invasion.

[0022] Instruction Manual

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a volcano plot of all sites with significant differences in methylation levels obtained by performing genomic DNA methylation dilution analysis on the PNI group and the non-PNI group in the example of the present invention.

[0025] Figure 2 This is a heat map of all sites with significant differences in methylation levels obtained by performing genomic DNA methylation dilution analysis on the PNI group and the non-PNI group in the example of the present invention.

[0026] Figure 3 This is a GO enrichment pathway diagram of target genes at sites with significant differences in methylation levels obtained by performing genomic DNA methylation dilution analysis on the PNI group and the non-PNI group in the embodiment of the present invention.

[0027] Figure 4 This is a KEGG enrichment pathway diagram of target genes at sites with significant differences in methylation levels obtained by performing genomic DNA methylation dilution analysis on the PNI group and the non-PNI group in the embodiment of the present invention.

[0028] Figure 5 These are 11 key sites with significant methylation modification obtained by performing genomic DNA methylation analysis on the PNI group and the non-PNI group in the example of the present invention.

[0029] Figure 6This is a schematic diagram of the change in regression coefficient of each DNA methylation site when different values ​​of the lambda parameter are selected during the establishment of the logistic regression model in an embodiment of the present invention.

[0030] Figure 7 It is a schematic diagram of ten cross-validations for adjusting parameter selection in the LASSO model in the regression model establishment and verification in the embodiment of the present invention.

[0031] Figure 8 Schematic diagram of the ROC curve used to predict classification accuracy in the establishment and verification of the regression model in the embodiment of the present invention.

[0032] Fig. 9 This is a location map of all sites with significant methylation modification in genes obtained by performing genomic DNA methylation analysis on the PNI group and the non-PNI group in the example of the present invention.

[0033] Fig.10 This is a location map of all sites with significant methylation modification in the genome obtained by performing genomic DNA methylation analysis on the PNI group and the non-PNI group in the example of the present invention. DETAILED DESCRIPTION

[0034] The embodiments of the present invention will be described in detail below in conjunction with examples. However, those skilled in the art will understand that the following examples are only used to illustrate the present invention and should not be construed as limiting the scope of the present invention. The specific conditions not specified in the examples are carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used without indicating the manufacturer are all conventional products that can be purchased commercially.

[0035] The technical solution of the present invention is:

[0036] A comprehensive analysis method for finding DNA methylation site markers for peripheral nerve invasion in patients with colorectal cancer, comprising:

[0037] Step S1: Data collection and preprocessing

[0038] Data on colorectal cancer patients, including somatic mutations, survival information, and DNA methylation levels, were collected from The Cancer Genome Atlas (TCGA) database with inclusion and exclusion criteria.

[0039] Inclusion criteria: (1) samples were marked as PNI or non-PNI; (2) the patient's pathological diagnosis was colorectal cancer; (3) complete clinical data.

[0040] Exclusion criteria: (1) non-primary colorectal cancer; (2) patients lacking mutation information and survival information; (3) patients with recurrent colorectal cancer.

[0041] Step S2: Differential analysis of DNA methylation levels

[0042] The difference analysis of DNA methylation levels was performed based on the PNI group and the non-PNI group. Specifically, the Champ package in the R software was used to analyze the sites with significant differences in methylation on the genomic DNA. The absolute value of delta_beta>0.1 and P<0.005 were used as the standard. The visualization of the site target genes with significant differences in methylation on the differentially expressed genomic DNA was achieved by drawing volcano maps and heat maps using the "ggplot2", "Cairo" and "pheatmap" packages in the R software.

[0043] In order to investigate the potential biological processes and enriched pathways of target genes at sites with significant differences in DNA methylation, gene ontology GO and KEGG enrichment analysis was performed using the “clusterprofiler” R package, and the statistical significance standard was a P value less than 0.005.

[0044] Step S3: Construction and evaluation of a risk scoring model for a combination of site markers with significant differences in DNA methylation associated with peripheral nerve invasion

[0045] A combination of diagnostic markers with significant differences in DNA methylation for determining whether colorectal cancer has peripheral nerve invasion is a risk assessment model established by 11 key sites.

[0046] A model was established in the TCGA cohort for predicting whether colorectal cancer would have peripheral nerve invasion by using site markers with significant differences in DNA methylation related to peripheral nerve invasion. The R package ChIPseeker was used to detect the locations of sites with significant differences in methylation in genomic DNA between the PNI group and the non-PNI group in the genome and specific genes; LASSO analysis was used to screen and predict sites with significant differences in methylation on related DNA, and 11 independent site markers for judging significant differences in DNA methylation related to peripheral nerve invasion were obtained, namely cg00048697, cg24404823, cg20161095, cg26827503, cg12198140, cg16461538, cg22635096, cg26462055, cg13947995, cg12421755, and cg16053651.

[0047] The ROC curve results showed that the site markers with significant differences in DNA methylation related to peripheral nerve invasion can accurately predict peripheral nerve invasion of colorectal cancer, with an AUC of 0.908.

[0048] In order to verify the universal applicability of the above peripheral nerve invasion-related miRNA markers, the predefined test set was used as the validation set. The ROC curve showed that the model had good consistency between the predicted probability and the actual probability, with an AUC of 0.877.

[0049] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0050] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A comprehensive analysis method for finding markers of genomic DNA methylation sites in peripheral nerve invasion in patients with colorectal cancer, characterized by: Colorectal cancer patients were divided into peripheral nerve invasion group (PNI group) and non-peripheral nerve invasion group (non-PNI group) based on the annotations during sequencing from the TCGA database; The genomic DNA of the PNI group and the non-PNI group was analyzed for methylation levels, and after comprehensive analysis, 11 genomic DNA methylation site markers associated with peripheral nerve invasion in colorectal cancer patients were obtained.

2. The method of constructing a comprehensive analysis method for finding markers of genomic DNA methylation sites of peripheral nerve invasion in patients with colorectal cancer according to claim 1, characterized in that: The 11 types of neuroinvasion-related genomic DNA methylation sites are cg00048697, cg24404823, cg20161095, cg26827503, cg12198140, cg16461538, cg22635096, cg26462055, cg13947995, cg12421755, and cg16053651.

3. According to claim 1, a comprehensive analysis method for finding markers of genomic DNA methylation sites in peripheral nerve invasion in patients with colorectal cancer is constructed, characterized in that: The following steps are involved: Step 1: Collect the original β matrix data and clinical characteristics from a certain cancer cohort in TCGA, and then divide them into PNI group and non-PNI group; Step 2: The R package Champ was used to analyze the differential methylation levels of genomic DNA in the PNI group and the non-PNI group, with P < 0.005 and the absolute value of delta_beta > 0.1 as the threshold for screening differential genomic DNA methylation sites; GO and KEGG were used to analyze the main enrichment pathways of target genes at genomic DNA sites with significant differences in methylation levels; the least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to analyze the differential methylation of genomic DNA sites; Step 3: Use the R package ChIPseeker to analyze and detect the locations of sites with significant methylation differences in the genome and specific genes in the PNI group and the non-PNI group; Step 4: Analyze the effect of the model under different penalty coefficients through LASSO cross-validation, select the parameter with the highest accuracy to establish the model, and obtain the most critical methylated genomic DNA sites related to PNI; Step 5: Calculate the individual score using the coefficient of each specific methylated genomic DNA site; use ROC to calculate the area under the curve AUC to evaluate the model's discrimination ability; then verify the accuracy of the model in the test set; Step 6: Statistical analysis.

4. Furthermore, the tumor includes but is not limited to colorectal cancer.

5. Furthermore, the DNA methylation level includes but is not limited to the DNA methylation level of the genome.

6. Furthermore, the method for determining the genomic DNA methylation level includes any one of PCR, gene chip, second-generation high-throughput sequencing, etc., or a combination of at least two of them.

7. This model can be used for different groups of cancer patients. That is, for a new group, the sample size can be increased, the "prediction score" can be calculated, the threshold can be found, and subsequent patients can be scored and the methylation level of genomic DNA methylation site markers can be analyzed on this basis.

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