Colon cancer patient immunogene prognosis model system based on K-ras and TP53 co-mutation

Abstract

The invention relates to an immune gene prognosis model system based on K-ras and TP53 co-mutation colon cancer patients and a construction method of the immune gene prognosis model system. The prognosis model system comprises the following seven genes: DUOX2, BST2, CCL28, APOBEC3C, CD79B, GUCA2A and TNFRSF13C, and an evaluation formula of the prognosis model system is as follows: a risk score = (DUOX2 gene expression quantity *-0.004321) + (BST2 gene expression quantity * + 0.00168) + (CCL28 gene expression quantity *-0.01796) + (APOBEC3C gene expression quantity * + 0.000145) + (CD79B gene expression quantity * + 0.05418) + (GUCA2A gene expression quantity *-0.00528) + (TNFRSF13C gene expression quantity * + 0.12786). According to the invention, by analyzing the difference between genes of a K-ras and TP53 co-mutation patient and a co-wild type patient and combining with gene intersection in an immunological database, a more reliable related gene capable of better reflecting an immune microenvironment is obtained, a prognosis model is constructed based on the gene, and meanwhile, the prognosis model has a good application prospect. The value of the model in the tumor immune microenvironment is evaluated by analyzing the risk score in the model and immune cell infiltration in the tumor microenvironment, and a method is provided for clinical related prognosis analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of gene prognostic model construction, and in particular to a method for constructing an immune gene prognostic model for colon cancer patients based on K-ras and TP53 co-mutations, and an immune gene prognostic model system for colon cancer patients constructed by the method. Background Art

[0002] The incidence of colon cancer in my country has increased year by year, and the current mortality rate has climbed to the fourth place among malignant tumors. Gene mutation plays an important role in the development of colon cancer. TP53 is the most common tumor suppressor gene, and usually more than half of the patients with colorectal cancer will have this gene mutation. K-ras gene is also one of the common mutation genes in colon cancer. Studies have reported that K-ras gene mutation is associated with the occurrence, development and drug resistance of colon cancer. TP53 and K-ras gene mutations play an important role in the tumor immune microenvironment. Activation of K-ras gene mutations has many effects on the tumor immune microenvironment, such as the activation and recruitment of macrophages, polarization of M1 macrophages to M2 macrophages, etc. TP53 can upregulate PD-L1 expression and tumor mutation load in the tumor microenvironment.

[0003] Immune-related genes play an important role in the development and prognosis of colon cancer. Many previous studies have focused on the role of immune-related genes in colon cancer and established relevant prognostic models. However, the current prognostic model is mainly constructed by screening immune-related genes from colon cancer tissues and related paracancerous tissues. This screening method is too single and cannot reflect the tumor immune microenvironment of different colon cancer patients. The differentially screened genes are simply prognosis-related genes and cannot fully reflect the role of immune-related genes in colon cancer. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the existing method for constructing an immune gene prognostic model for colon cancer patients, the present invention proposes a new method for constructing an immune gene prognostic model for colon cancer patients based on K-ras and TP53 co-mutations and an immune gene prognostic model system for colon cancer patients constructed by this method.

[0005] Since the co-mutation of K-ras and TP53 has a positive effect on the tumor immune microenvironment, K-ras mutation can change the immune microenvironment and enhance the infiltration of immune macrophages, and TP53 mutation can enhance the tumor mutation load and affect the expression of multiple immune-related genes, the differential immune genes screened by the co-mutation patients and non-mutation patients in the present invention can better reflect the patient's immune microenvironment and prognosis. In other words, the differential genes screened out based on colorectal cancer tissues under different immune microenvironment states can better reflect the relationship between immune status and prognosis.

[0006] Specifically, the present invention provides an immune gene prognostic model system for patients with colon cancer with co-mutation of K-ras and TP53. The prognostic model system includes the following 7 genes: DUOX2, BST2, CCL28, APOBEC3C, CD79B, GUCA2A and TNFRSF13C. The evaluation formula of the prognostic model system is as follows:

[0007] Risk score = (DUOX2 gene expression level *-0.004321) + (BST2 gene expression level *+0.00168) + (CCL28 gene expression level *-0.01796) + (APOBEC3C gene expression level *+0.01145) + (CD79B gene expression level *+0.05418) + (GUCA2A gene expression level *-0.00528) + (TNFRSF13C gene expression level *+0.12786).

[0008] Furthermore, the present invention proposes a method for constructing the above-mentioned immune gene prognostic model system for colon cancer patients based on K-ras and TP53 co-mutation, comprising the following steps:

[0009] S1. Obtain mRNA and mutation data of colon cancer patients, download immune-related gene list and GSE75500 colon cancer data;

[0010] S2. Compare the mRNA and mutation data of colon cancer patients to screen patients with K-ras mutation, TP53 mutation, K-ras and TP53 co-mutation, and K-ras and TP53 wild-type;

[0011] S3. The patients with K-ras and TP53 co-mutation and the patients with K-ras and TP53 wild type were divided into two groups, and the related differentially expressed genes were identified by edgeR package, and the differentially expressed immune-related genes were screened out by comparing the immune-related gene lists;

[0012] S4. Genes associated with prognosis were determined by univariate regression analysis, and multivariate Cox regression analysis was further used to determine the key genes for constructing the prognostic model. The prognostic model was constructed by Lasso-penalized Cox regression analysis.

[0013] S5. Conduct feasibility and accuracy analysis of the prognostic model and verify the predictive effect of the prognostic model system, including survival data analysis using Kaplan-Meier curves and statistical analysis using the log-rank test.

[0014] Furthermore, the method for constructing the immune gene prognostic model system for colon cancer patients of the present invention comprises, in step S1:

[0015] Obtain mRNA and mutation data of colon cancer patients from the TCGA database;

[0016] Download the list of immune-related genes from the Immunology Database and ImmPort Database;

[0017] Download GSE75500 colon cancer data from the GEO database.

[0018] Furthermore, in step S3 of the method for constructing the immune gene prognostic model system for patients with colon cancer of the present invention, the relevant differentially expressed genes are identified by using the edgeR package, and the identification formula is: |Log2 fold change (FC)|>1.0 and P﹤0.05.

[0019] Furthermore, the feasibility and accuracy analysis of the prognostic model described in step S5 of the method for constructing the immune gene prognostic model system for colon cancer patients of the present invention includes:

[0020] S51. Establish validation model with internal and external data: All patients were randomly divided into test set and training set as internal validation data through edgeR package, and the two groups of data were divided into high-risk group and low-risk group according to the median, and the prognostic difference between the two groups of patients was observed; GSE75500 in GEO database was used as external validation data, and the group of patients was also divided into high-risk group and low-risk group according to the median, and the prognostic difference between the two groups of patients was observed;

[0021] S52. KM curve and log-rank were used to evaluate the survival time of patients in high- and low-risk groups, and ROC curve was drawn using R language to determine the specificity and sensitivity of the model;

[0022] S53. Analysis of immune cell infiltration in the tumor microenvironment using the TIMER online database: The immune cell infiltration levels of colon cancer patients were downloaded from the TCGA database, and the association between the risk score and immune cell infiltration in this prognostic model was calculated.

[0023] Furthermore, the present invention also relates to the use of the above-mentioned immune gene prognostic model system for colon cancer patients based on co-mutation of K-ras and TP53 in the preparation of a colon cancer prognosis assessment kit.

[0024] In summary, the method for constructing an immune gene prognostic model for colon cancer patients with co-mutation of K-ras and TP53 in the present invention analyzes the differences in genes between patients with co-mutation of K-ras and TP53 and co-wild-type patients, and combines the gene intersection with the immunology database to obtain more reliable related genes that can better reflect the immune microenvironment, and constructs a prognostic model based on this. At the same time, by analyzing the risk score in the model and the immune cell infiltration in the tumor microenvironment, the value of the model in the tumor immune microenvironment is evaluated, which provides a method for future clinical related prognosis analysis and provides a potential target for immunotherapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the following drawings are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 is the differential gene volcano map, where Figure 1 A is the differentially expressed gene between the two groups of patients. Figure 1 B is the immune differential gene obtained by the intersection of the differential gene list and the immune gene list.

[0027] Figure 2 It is the prognosis-related immune differential genes obtained by univariate Cox regression analysis.

[0028] Figure 3 is the correlation model prognosis, where Figure 3 A is the prognostic KM diagram of all TCGA colon cancer patients. Figure 3 B is the KM diagram of the GSE75500 external validation model. Figure 3 C. Figure 3 D is the KM diagram of the internal validation model of the TCGA database.

[0029] Figure 4 It is the ROC curve diagram of risk model and clinical indicators.

[0030] Figure 5 middle Figure 5 A is the univariate regression analysis diagram of risk model and clinical characteristics. Figure 5 B is the multivariate Cox regression analysis diagram.

[0031] Figure 6 is the relationship diagram between the risk model and immune-related cells, where Figure 6 A is CD8+T cell, Figure 6 B is a resting memory CD4 T cell, Figure 6 C is activated memory CD4 T cells, Figure 6 D is plasma cells, Figure 6 E is helper T cell, Figure 6 F is a resting NK cell, Figure 6 G is activated NK cells, Figure 6 H is activated mast cells, Figure 6 J is M2 macrophage, Figure 6 K is M1 macrophage. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The present invention can also be implemented or applied through other different specific implementation methods. The details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. At the same time, it should be understood that the scope of protection of the present invention is not limited to the specific specific implementation methods described below; it should also be understood that the terms used in the embodiments of the present invention are for describing specific specific implementation methods, not for limiting the scope of protection of the present invention.

[0033] Unless otherwise defined, all technical terms used in the present invention have the same meanings as commonly understood by those skilled in the art.

[0034] The methods in the following examples are all conventional methods in the art unless otherwise specified. Example: An immune gene prognostic model system for patients with colon cancer based on co-mutation of K-ras and TP53 and its construction method

[0035] This program includes the following steps:

[0036] 1. Data acquisition

[0037] First, we downloaded the mRNA and mutation data of 387 patients with colon cancer from the TCGA database (https: / / portal.gdc.cancer.gov / ). We compared the survival data of TCGA patients and finally included 363 patients. The list of immune-related genes was downloaded from the Immunology Database and Analysis Portal (ImmPort) database. GSE75500 colon cancer data was downloaded from the GEO database.

[0038] (II) Screening patients with different gene mutations

[0039] Comparing the colon cancer mutation data in the TCGA database, 182 patients had K-ras mutations, 243 patients had TP53 mutations, 71 patients had K-ras and TP53 co-mutations, and 44 patients had both K-ras and TP53 wild-type.

[0040] (III) Screening of differentially immune-related genes

[0041] Co-mutation patients and wild-type patients were divided into two groups. The edgeR package (v3.52) (http: / / bioconductor.org / packages / edgeR / ) was used to identify the relevant differentially expressed genes (|Log2fold change (FC)|>1.0 and P﹤0.05). Further, by comparing the immune-related gene list, 75 differentially expressed immune-related genes were obtained (see Figure 1 ).

[0042] (IV) Determine the key genes for model construction

[0043] Through univariate regression analysis, 14 genes related to prognosis were identified (see Figure 2 ).

[0044] (V) Model construction

[0045] Multivariate Cox regression analysis was used to determine the seven genes for model construction: DUOX2, BST2, CCL28, APOBEC3C, CD79B, GUCA2A, and TNFRSF13C. The prognostic model was constructed by Lasso-penalized Cox regression analysis. The formula used to construct the model was as follows:

[0046] in

[0047] Risk score = (DUOX2 gene expression level * -0.004321) + (BST2 gene expression level * + 0.00168) + (CCL28 gene expression level * -0.01796) + (APOBEC3C gene expression level * + 0.01145) + (CD79B gene expression level * + 0.05418) + (GUCA2A gene expression level * -0.00528) + (TNFRSF13C gene expression level * + 0.12786)

[0048] (VI) Model prognostic analysis

[0049] Survival data were analyzed using Kaplan-Meier curves, and statistical analysis was performed by the log-rank test.

[0050] The following methods are used to verify the feasibility and accuracy of the model:

[0051] (1) Establishment of validation model with internal and external data: All patients were randomly divided into two groups, test set (n = 182) and training set (n = 181), as internal validation data using the edgeR package. The two groups were divided into high-risk group and low-risk group according to the median. We found that the prognosis of patients in the two high-risk groups was significantly worse than that in the low-risk group (p < 0.05, see Figure 3C and 3D); In addition, the GEO database GSE75500 was used as external validation data, and the patients were also divided into high-risk group and low-risk group by the median. The prognosis of the high-risk group was significantly worse than that of the low-risk group (p﹤0.05, see Figure 3 B).

[0052] (2) ROC curve: KM curve and log-rank were used to evaluate the survival time of patients in the high-risk and low-risk groups. R language was used to draw the ROC curve to determine the specificity and sensitivity of the model. The ROC curve of the model was 0.712 (see Figure 4 ).

[0053] Through the above two analyses, both internal and external validation showed model consistency, and the model AUC was 0.712, indicating that the model had good stability.

[0054] To investigate the relationship between risk scores, clinical characteristics of patients and prognosis:

[0055] Clinical characteristics and model analysis: Univariate and multivariate Cox regression analysis was performed to assess whether the model was a risk factor independent of clinical characteristics (see Figure 5 ), among which the clinical characteristics are AJCC stage, age (age), tumor stage (Tstage) and lymph node stage (N stage). Through univariate analysis, we found that age, AJCC stage, tumor stage, lymph node stage and risk score are prognostic factors for patients. Through multivariate analysis, we found that age, AJCC stage, lymph node stage and risk score are independent prognostic factors.

[0056] (3) Analysis of immune cell infiltration in the tumor microenvironment using the TIMER online database: The TIMER database can be easily used to determine the relationship between immune cell infiltration and other parameters. We downloaded the immune cell infiltration levels of TCGA colon cancer patients and calculated the association between risk score and immune cell infiltration (see Figure 6 ), among which the risk score was positively correlated with M1 macrophages, M2 macrophages, activated NK cells, resting NK cells, activated CD4 memory T lymphocytes, CD8 T lymphocytes and helper T lymphocytes, and negatively correlated with activated mast cells, plasma cells and resting CD4 memory T lymphocytes.

[0057] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any technician familiar with the profession can make some changes or modifications to the technical contents disclosed above without departing from the scope of the technical solution of the present invention to obtain equivalent embodiments of equivalent changes. However, any simple modification, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. An immune gene prognostic model system for patients with colon cancer based on co-mutation of K-ras and TP53, characterized in that: The prognostic model system includes the following 7 genes: DUOX2, BST2, CCL28, APOBEC3C, CD79B, GUCA2A and TNFRSF13C. The evaluation formula of this prognostic model system is as follows: Risk score = (DUOX2 gene expression level *-0.004321) + (BST2 gene expression level *+0.00168) + (CCL28 gene expression level *-0.01796) + (APOBEC3C gene expression level *+0.01145) + (CD79B gene expression level *+0.05418) + (GUCA2A gene expression level *-0.00528) + (TNFRSF13C gene expression level *+0.12786).

2. The method for constructing an immune gene prognostic model system for colon cancer patients based on K-ras and TP53 co-mutation according to claim 1, characterized in that: The construction method comprises: S1. Obtain mRNA and mutation data of colon cancer patients, download immune-related gene list and GSE75500 colon cancer data; S2. Compare the mRNA and mutation data of colon cancer patients to screen patients with K-ras mutation, TP53 mutation, K-ras and TP53 co-mutation, and K-ras and TP53 wild-type; S3. The patients with K-ras and TP53 co-mutation and the patients with K-ras and TP53 wild type were divided into two groups, and the related differentially expressed genes were identified by edgeR package, and the differentially expressed immune-related genes were screened out by comparing the immune-related gene lists; S4. Genes associated with prognosis were determined by univariate regression analysis, and multivariate Cox regression analysis was further used to determine the key genes for constructing the prognostic model. The prognostic model was constructed by Lasso-penalized Cox regression analysis. S5. Conduct feasibility and accuracy analysis of the prognostic model and verify the predictive effect of the prognostic model system, including survival data analysis using Kaplan-Meier curves and statistical analysis using the log-rank test.

3. The method for constructing the immune gene prognosis model system for colon cancer patients according to claim 2, characterized in that: Step S1 includes: Obtain mRNA and mutation data of colon cancer patients from the TCGA database; Download the list of immune-related genes from the Immunology Database and ImmPort Database; Download GSE75500 colon cancer data from the GEO database.

4. The method for constructing the immune gene prognosis model system for colon cancer patients according to claim 2, characterized in that: The edgeR package was used to identify the relevant differentially expressed genes as described in step S3, and the identification formula was: |Log2 fold change (FC)|>1.0 and P<0.

05.

5. The method for constructing the immune gene prognosis model system for colon cancer patients according to claim 2, characterized in that: The feasibility and accuracy analysis of the prognostic model described in step S5 includes: S51. Establish validation model with internal and external data: All patients were randomly divided into test set and training set as internal validation data through edgeR package, and the two groups of data were divided into high-risk group and low-risk group according to the median, and the prognostic difference between the two groups of patients was observed; GSE75500 in GEO database was used as external validation data, and the group of patients was also divided into high-risk group and low-risk group according to the median, and the prognostic difference between the two groups of patients was observed; S52. KM curve and log-rank were used to evaluate the survival time of patients in high- and low-risk groups, and ROC curve was drawn using R language to determine the specificity and sensitivity of the model; S53. Analysis of immune cell infiltration in the tumor microenvironment using the TIMER online database: The immune cell infiltration levels of colon cancer patients were downloaded from the TCGA database, and the association between the risk score and immune cell infiltration in this prognostic model was calculated.

6. Use of the immune gene prognostic model system for colon cancer patients based on co-mutation of K-ras and TP53 according to claim 1 in the preparation of a colon cancer prognosis evaluation kit.

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