Comprehensive analysis method for searching peripheral nerve infringement DNA methylation site marker of head and neck malignant tumor patient

Abstract

The invention discloses a method for searching a DNA methylation difference site marker for a patient with head and neck malignant tumors, and belongs to the technical field of biology. Comprising the following steps: collecting an original beta matrix and clinical features from TCGA, and dividing the original beta matrix and clinical features into a PNI group and a non-PNI group; analyzing the differential methylation level of DNA in the two groups; annotating the differential methylation sites; carrying out GO and KEGG enrichment analysis on the target genes of the differential sites; carrying out LASSO logistic regression analysis on the difference sites, analyzing the effects of the model under different penalty coefficients through LASSO cross validation, selecting the parameter with the highest accuracy to establish the model, and obtaining the most critical sites; calculating scores of individuals according to coefficients of all DNA methylation sites in the model; calculating the area under the curve; and verifying the accuracy of the model in the test set. The method does not need a specific instrument, is suitable for clinical popularization, can be used for predicting whether the head and neck malignant tumors are subjected to peripheral nerve invasion or not, and is favorable for deeply researching the characteristics of the head and neck malignant tumors.

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 head and neck malignant tumors. Background Art

[0002] Head and neck tumors refer to tumors located above the clavicle, with the upper boundary being the skull base and the posterior boundary being the cervical spine, including the head, face and soft tissues of the neck, otolaryngology, oral cavity, salivary glands and thyroid gland. Generally speaking, tumors of the central nervous system such as the brain and spinal cord and tumors in the eye are not included. From the perspective of clinical departments involved in the diagnosis and treatment of general hospitals, they mainly include tumors of the neck in the otolaryngology, oral and maxillofacial departments and general surgery departments; from the perspective of anatomical function, they are mainly divided into three categories: upper digestive tract and upper respiratory tract tumors, salivary gland tumors and thyroid tumors. In addition, they also include tumors of skin and soft tissue origin such as malignant melanoma, malignant lymphoma and soft tissue sarcoma. There are nearly 65,000 new cases of head and neck tumors in the United States each year. Excluding skin and thyroid, >90% of head and neck malignant tumors are squamous cell (epidermoid) carcinomas, and most of the other types are adenocarcinomas, sarcomas and lymphomas.

[0003] Neuroinvasion generally refers to the direct invasion of malignant tumors or metastasis to nerves through blood vessels, lymph nodes, etc., causing conduction abnormalities in the affected nerves, resulting in abnormal sensation and motor function in the nerve-controlled area, and symptoms such as limb numbness, pain, and movement disorders. As one of the most common malignant tumors, the relationship between head and neck malignant tumors and peripheral nerve invasion has also received attention. Neuroinvasion is very common in head and neck tumors. Currently, 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 patients with head and neck malignant tumors will have peripheral nerve invasion and thus have a poor prognosis.

[0004] DNA methylation refers to the change of genetic expression without changing the DNA sequence. It refers to the chemical modification process in which the bases on the DNA sequence are converted into a methyl group by covalent bonding of S-adenosylmethionine under the action of DNA methyltransferase. DNA methylation is a modification method of eukaryotic cells and the main epigenetic form of mammalian genes. The nucleotide sequence and composition of DNA methylation do not change, but its gene expression has been affected. 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 finding DNA methylation site markers for peripheral nerve invasion in patients with head and neck malignancies, aiming to determine the risk factors for PNI in patients with head and neck malignancies, and to reveal the impact of PNI on the clinical progression of patients with head and neck malignancies, so as to provide a basic theoretical basis for the construction of specific marker risk models and treatments. Summary of the invention

[0005] 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.

[0006] The present invention is achieved through the following technical solutions:

[0007] The present invention first downloads beta matrix data and clinical characteristics from the cancer genome atlas TCGA and divides them into PNI group and non-PNI group; then uses R package ChAMP to analyze sites with significant differences in methylation levels in PNI group and non-PNI group, and uses the adjusted P<0.05 and the absolute value of the difference beta value>0.1 as the threshold for screening genomic DNA methylation sites with significant differences; uses R package ChIPseeker to analyze and detect the locations of sites with significant methylation differences in genomic DNA in PNI group and non-PNI group in the genome and specific genes; uses R package clusterProfiler to perform GO and KEGG enrichment analysis on target genes of genomic DNA sites with significant differences in methylation levels; 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 coefficients of all DNA methylation sites in the model 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.

[0008] Furthermore, the first aspect of the present invention provides a composition of DNA methylation sites 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 10 DNA methylation sites with significantly different methylation levels, namely cg12751042, cg06550986, cg17833066, cg16386396, cg01312482, cg24494491, cg15876464, cg11784887, cg11203512, and cg02583618.

[0009] Furthermore, the tumor includes but is not limited to head and neck malignant tumors.

[0010] Furthermore, the methylation level refers to the methylation level of a genomic DNA site having a significant difference in methylation level.

[0011] Furthermore, the method for determining the methylation level includes any one of PCR, methylation chip, and second-generation high-throughput sequencing, or a combination of at least two of them.

[0012] 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.

[0013] Furthermore, the detection product includes: probes and primers for detecting a DNA methylation site marker composition related to whether the tumor has peripheral nerve invasion.

[0014] Furthermore, the detection product includes: probes and primers for DNA methylation sites related to peripheral nerve invasion of tumors. The probes can bind to the DNA methylation sites respectively through molecular hybridization to generate hybridization signals, and the primers can amplify the peripheral nerve invasion-related methylated DNA sequences with significant differences in methylation levels through PCR-based technology.

[0015] 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, methylation chip and DNA methylation sequencing.

[0016] The third aspect of the present invention provides a composition of differential methylation modification of DNA related to peripheral nerve invasion by tumors as described above, and the use of the detection product of differential methylation modification of DNA related to peripheral nerve invasion by tumors in establishing a risk prediction system for predicting whether a tumor will cause peripheral nerve invasion.

[0017] 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.

[0018] Furthermore, the method for determining the differential methylation modification of DNA includes any one of PCR, methylation chip, and second-generation high-throughput sequencing, or a combination of at least two of them.

[0019] 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.

[0020] Compared with the prior art, the present invention has at least the following advantages:

[0021] The present invention established a marker model with 10 DNA differential methylation modifications related to peripheral nerve invasion, and divided patients with head and neck malignant tumors 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 0.8. The prediction performance has been verified in the test set.

[0022] The analytical method for determining the combination of differential DNA methylation modification markers for peripheral nerve invasion of head and neck malignant tumors proposed in the present invention provides a reference for the clinical treatment of head and neck malignant tumors, and can serve as a new type of DNA differential methylation modification biomarker and potential therapeutic target for predicting whether head and neck malignant tumors will cause peripheral nerve invasion.

[0023] Instruction Manual

[0024] 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.

[0025] Figure 1 It is a volcano plot of all sites with significant differences in methylation levels obtained by analyzing DNA methylation sites in the PNI group and the non-PNI group in the embodiment of the present invention.

[0026] Figure 2This is a heat map of all sites with significant differences in methylation levels obtained by analyzing DNA methylation sites in the PNI group and the non-PNI group in the embodiment of the present invention.

[0027] Figure 3 This is a GO enrichment pathway diagram of target genes at sites with significant differences in methylation levels obtained by analyzing DNA methylation sites in the PNI group and the non-PNI group in the embodiment of the present invention.

[0028] Figure 4 This is a KEGG enrichment pathway diagram of target genes at sites with significant differences in methylation levels obtained by analyzing DNA methylation sites in the PNI group and the non-PNI group in the embodiment of the present invention.

[0029] Figure 5 These are the 10 key sites with significant methylation modification obtained by performing DNA methylation LASSO logistic regression analysis on the PNI group and the non-PNI group in the example of the present invention.

[0030] Figure 6 This 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.

[0031] Figure 7 This is a schematic diagram of error rates corresponding to different lambda parameters selected during the process of establishing a logistic regression model in an embodiment of the present invention.

[0032] 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.

[0033] 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.

[0034] 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

[0035] 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.

[0036] The technical solution of the present invention is:

[0037] A comprehensive analytical approach to identify markers of peripheral nerve invasion in patients with head and neck malignancies, including:

[0038] Step S1: Data collection and preprocessing

[0039] Data of patients with head and neck malignancies, including survival information and DNA methylation levels, were collected from the inclusion and exclusion criteria of The Cancer Genome Atlas TCGA (https: / / portal.gdc.cancer.gov / ) database.

[0040] Inclusion criteria: (1) samples were marked with PNI or non-PNI; (2) the patient's pathological diagnosis was head and neck malignant tumor; (3) complete clinical data.

[0041] Exclusion criteria: (1) non-primary head and neck malignant tumors; (2) patients with missing survival information; (3) patients with recurrent head and neck malignant tumors.

[0042] Step S2: Differential analysis of DNA methylation levels

[0043] 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 the difference beta>0.1 and the corrected P<0.05 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.

[0044] In order to investigate the potential biological processes and enriched pathways of target genes at sites with significant differences in DNA methylation, the gene ontology GO and KEGG enrichment analysis was performed using the “clusterProfiler” R package, and the criterion for statistical significance was a P value less than 0.05.

[0045] 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

[0046] A combination of diagnostic markers for sites with significant differences in DNA methylation for determining whether head and neck malignant tumors have peripheral nerve invasion is a risk assessment model established by 10 key sites.

[0047] A model was established in the TCGA cohort for predicting whether head and neck malignancies 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 in the PNI group and 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 10 independent site markers for judging significant differences in DNA methylation related to peripheral nerve invasion were obtained, namely cg12751042, cg06550986, cg17833066, cg16386396, cg01312482, cg24494491, cg15876464, cg11784887, cg11203512, and cg02583618.

[0048] 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 head and neck malignant tumors, with an AUC of 0.8.

[0049] 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, and the AUC reached 0.75.

[0050] 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.

[0051] 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 DNA methylation site markers for peripheral nerve invasion in patients with head and neck malignant tumors, characterized in that: include: Patients with head and neck malignancies were divided into peripheral nerve invasion group (PNI group) and non-peripheral nerve invasion group (non-PNI group) based on clinical information from the TCGA database; The methylation levels of the PNI group and the non-PNI group were analyzed, and a total of 10 DNA methylation site markers associated with peripheral nerve invasion in patients with head and neck malignant tumors were obtained after comprehensive analysis.

2. The method for constructing a comprehensive analysis method for finding DNA methylation site markers for peripheral nerve invasion in patients with head and neck malignant tumors according to claim 1, characterized in that: The 10 neuroinvasion-related DNA methylation sites are cg12751042, cg06550986, cg17833066, cg17833066, cg01312482, cg24494491, cg15876464, cg11784887, cg11203512, and cg02583618.

3. According to claim 1, a comprehensive analysis method for finding DNA methylation site markers of peripheral nerve invasion in patients with head and neck malignant tumors is constructed, characterized in that: The following steps are involved: Step 1: Collect the original beta 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 DNA methylation sites between the PNI group and the non-PNI group, with the adjusted p < 0.05 and the absolute value of the difference beta value > 0.1 as the threshold for screening differential DNA methylation sites; Step 3: Use the R package ChIPseeker to annotate the above differentially methylated sites and determine the genes and regions where they are located; perform GO and KEGG enrichment analysis on the target genes of DNA sites with significant differences in methylation levels; Step 4: Perform LASSO logistic regression analysis on the differential DNA methylation sites, analyze the effect of the model under different penalty coefficients through LASSO cross-validation, select the parameters with the highest accuracy to establish the model, and obtain the most critical DNA methylation sites related to PNI; Step 5: Calculate the individual score based on the coefficients of all DNA methylation sites in the model; 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: Perform statistical analysis and draw graphs. For detailed graph results, please see the "Instruction Manual Attachments" section.

4. Furthermore, the tumor includes but is not limited to head and neck malignant tumors.

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, methylation 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 on this basis, the methylation levels of the DNA methylation site markers of subsequent patients can be analyzed and scored.

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