Polygenic risk score for prediction of radiotherapy efficacy and radiosensitivity in patients with non-metastatic breast cancer

Objective:To construct a novel polygenic risk scoring model,in order to predict the benefits of radiosensitivity in patients with non-metastatic breast cancer(NMBC).Methods:A total of 450 NMBC patients from The Cancer Genome Atlas(TCGA)were enrolled and randomly assigned 6:4(training vs.validation).The empirical Bayes differential analysis was used to perform differential expression analysis,univariate Cox regression and Kaplan-Meier analysis were used to screen for prognosis-related genes.Finally,LASSO regression and stepwise regression were used to select key prognostic-related genes.We constructed a multivariate Cox proportional risk regression model using key genes.The pRRophetic function was used to predict drug sensitivity of radiosensitivity(RS)and radioresistance(RR)groups for adjuvant therapy.Results:Eight genes(AMH,H2BU1,HOXB13,TMEM132A,TMEM270,ODF3L1,RIIAD1 and RIMBP2)were screened to build a polygenic risk scoring model.The region of characteristic(ROC)curves were drawn based on the 3-,5-and 10-year overall survival(OS),with area under curves(AUCs)of 0.816,0.822 and 0.806,respec-tively.RS and RR can be effectively distinguished according to the risk score of 2.004.Gene set enrichment analysis(GSEA)showed that necroptosis was significantly enriched in RS,while complement and coagulation cascade,JAK-STAT and PPAR signaling pathways were significantly enriched in RR.Alternatively,for those radioresistant patients,the chemotherapy drugs that might be more helpful are Cisplatin,Docetaxel,Methotrexate and Vinblastine with higher drug sensitivity.Conclusion:The polygenic risk scoring model showed prediction for the benefit of radiotherapy in NMBC patients,which might be used to guide clinical practice.