Construction and validation of a novel model for predicting prognosis of breast cancer patients based on m7G RNA modification-related genes / 中国现代普通外科进展

ABSTRACT

Objective:The aim of this study was to determine the development and validation of a prognostic prediction model based on genes associated with N7 methylguanosine(m7G)RNA methylation modifications in breast cancer(BC)patients.Methods:We explored the expression patterns of 31 key m7G RNA methylation-related regulators in breast cancer tissues and their prognostic significance.First,we downloaded and analyzed the mRNA gene expression and clinical prognostic data from The Cancer Genome Atlas(TCGA)and Gene Expression Gene Expression(GEO)databases.The prognostic value of each m7G-related gene for survival was assessed by us-ing univariate Cox regression analysis.A prognostic risk model based on m7G methylation-regulat-ed genes was then constructed by applying the least absolute shrinkage and selection operator(LASSO)regression method.Patients were divided into high-risk and low-risk groups according to median risk scores,and the differences in overall survival(OS)between high-and low-risk groups were compared.Then,gene set enrichment analysis(GSEA)analysis was performed to elaborate the main mechanisms of action in the BC high-risk group.Finally,a prognostic nomogram based on m7G was constructed and validated in TCGA and GEO cohorts respectively.Results:A prognostic risk model based on seven m7G methylation-regulated genes was constructed.The overall survival(OS)was significantly lower in the high-risk group than in the low-risk group(P＜0.001).GSEA anal-ysis showed that the"cell cycle"was the most important pathways in the high-risk group.Using risk scores and clinical characteristics of the training set TCGA cohort,prognostic nomogram was constructed,and calibration curves and decision curve analysis(DCA)showed good concordance and predictive efficacy and were validated in the GEO cohort.Conclusion:The prognostic model based on m7G-related genes plays an important prognostic predictive role in breast cancer and can be used to guide the individualized treatment of breast cancer patients.