The Construction Of A Prognostic Model For Breast Cancer Based On MAPK Pathway-related Genes And The Analysis Of Its Clinical Significance

Objectives:To construct a prognostic model of breast cancer based on mitogen activated protein kinase(MAPK)signaling pathway-related genes by bioinformatics analysis technology,verify the predictive ability of the model and explore the immune correlation of the model.Methods:The gene expression data and clinical data of breast cancer were obtained from The Cancer Genome Atlas(TCGA)database and Gene Expression Omnibus(GEO)database,and81 MAPK signaling pathway-related genes(MRGs)were obtained from the Genome Set Enrichment Analysis(GSEA)website.LASSO analysis and univariate Cox regression analysis were used to screen the differentially expressed MRGs with prognostic value to construct a prognostic model of breast cancer.According to the median risk score of TCGA samples obtained by the model,the samples were divided into high-risk group and low-risk group.Kaplan-Meier(K-M)survival curve and receiver operating characteristic curve(ROC)were drawn using TCGA and GEO data to verify the reliability of the prognostic model.TCGA samples were used for univariate and multivariate independent prognostic analysis.Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)enrichment analysis were performed on the differentially expressed genes between the high-risk group and lowrisk group in TCGA samples.Single sample gene set enrichment analysis(ss GSEA)was used to quantify the infiltration scores of 14 immune cells and the activities of 13 immune-related pathways.Results:A total of 1144 gene expression samples were obtained from TCGA database,including111 normal tissue samples and 1033 tumor tissue samples,and we obtained 81 genes were related to MAPK signaling pathway from GSEA,of which 57 genes were differentially expressed between normal samples and tumor samples.Univariate Cox regression analysis showed that 13 of 57 MRGs were associated with overall survival(OS)(P <0.05).These genes included DAXX,HRAS,JUN,MAP2K6,MAP3K11,MAPK10,MAPKAPK3,MAX,NFKBIA,PAK1,RAC1,RPS6KA1 and TGFB1,among which MAPK10 and RAC1 were high-risk genes(hazard ratio,HR>1),the remaining 11 genes were low-risk genes(HR<1).Protein interaction network and correlation analysis suggested that multiple genes had a coexpression relationship.The 13-gene breast cancer prognostic model was constructed and the samples were divided into high-risk group and low-risk group.The high-risk patients had a shorter survival time than the low-risk patients(P<0.01).The ROC curve showed that the model had a certain predictive ability for the survival time of breast cancer patients.GSE20685(N=327)and GSE42568(N=121)obtained in the GEO database were used for validation,and the consistent results were obtained.Univariate and multivariate Cox analysis showed that the risk score could be used as an independent predictor(HR>1,P <0.01).GO analysis found that the differentially expressed genes were enriched in the activation and proliferation of immune cells,immune response and cell-cell adhesion.KEGG analysis also showed that the differentially expressed genes were enriched in immune-related functions.Compared with the high-risk group,the 12 immune cells had stronger infiltration and 13 immune-related pathways showed stronger activity in the low-risk group measured by ss GSEA,the difference was statistically significant(P<0.05).However,there was no statistical difference in macrophages and Th2 cells in the immune cells between the two groups.Conclusions:(1)A 13-gene MAPK signaling pathway-related breast cancer prognostic model was constructed based on TCGA database.(2)Patients in the high-risk group identified by the model had worse prognosis than those in the low-risk group.(3)Compared with the high-risk group,the infiltrative degree of immune cells and the activity of immune-related pathways were stronger in the low-risk group.