Establishment Of Prognostic Risk Model Of M6A-Related LncRNA In Breast Cancer And Analysis Of Predictive Value

Background:Breast cancer(BC)is a highly complex and heterogeneous malignant tumor.In 2020,approximately 2.3 million new cases of breast cancer have been reported worldwide,and female BC becomes the leading cause of cancer incidence worldwide.Studies have shown that abnormal m6 A modification may promote or inhibit cancer during the development and progression of BC.Regulatory proteins involved in m6 A modification have potential as prognostic biomarkers and therapeutic targets in BC.Abnormal expression of long non-coding RNAs(lncRNAs)is also present in BC and affects the proliferation,death and drug resistance of cancer cells by regulating gene expression and protein function.Studies have confirmed that m6A-related lnc RNA is closely related to the occurrence and development of a variety of tumors,affecting the prognosis of patients and immunotherapy response,but their potential role and prognostic value in BC have not been fully understood.In this paper,we constructed a prognostic model of m6A-related lnc RNA in breast cancer by bioinformatics analysis and analyzed the prognostic value and clinical application of this model in BC.Methods:Transcriptome data and clinical data of BC samples were obtained from the Cancer Genome Atlas(TCGA),and the data were further processed to select m6A-related lnc RNA by co-expression analysis.Then,all patients were randomly divided into training and testing sets,and the best m6A-related lncRNAs were screened by univariate Cox regression analysis,least absolute shrinkage and selection operator(LASSO),and multivariate Cox regression analysis.Based on these lncRNAs,we constructed a prognostic risk model in the training set and calculated the risk scores of patients in the training set,and divided patients into high-risk and low-risk groups according to the median value of the risk scores.Next,the predictive accuracy of the prognostic risk model was assessed by Kaplan-Meier survival analysis,receiver operating characteristic curve(ROC),nomogram,and principal component analysis.Univariate and multivariate Cox regression analyses were used to determine the independence of the prognostic risk model.Immune function correlation analysis was used to explore potential immune function across risk groups.Differences in tumor mutation burden(TMB)for each sample in the high and low-risk groups were analyzed by using a risk model.Tumor Immune Dysfunction and Exclusion(TIDE)score is used to predict the efficacy of immunotherapy in breast cancer patients.Finally,potential sensitive drugs for this model were further predicted.Results:We screened a total of 16 m6A-related lncRNAs of significance and constructed a prognostic model in the training set.K-M survival analysis showed that BC patients in the high-risk group had a significantly worse prognosis than those in the low-risk group(P<0.05).Cox regression analysis showed that the prognostic risk model could be used as an independent risk factor for the prognosis of BC patients(P<0.001).ROC curves showed that the area under the curve(AUC)of 1-year,3-year,and 5-year overall survival rates in BC patients was 0.743,0.755,and 0.722,respectively,and,compared with clinical characteristics such as gender and TNM stage,the AUC of the risk score was as high as 0.743 and the C-index was the highest,all indicating the high predictive accuracy of the prognostic risk model.The nomogram suggests that this model has effective predictive value and can better guide clinical practice.TMB differed between the high and low-risk groups,with patients in the high TMB and high-risk score groups having the worst survival.Immuno-efficacy analysis showed that the high-risk group received immunotherapy better.In addition,the eight potential drugs selected according to the risk model may guide the clinical medication of breast cancer patients.Conclusion:1.Prognostic risk model is constructed from 16 m6A-related lncRNAs associated with BC prognosis,which has high predictive accuracy and can be used to assess the prognosis of BC patients and is an effective prognostic potential marker for BC.2.Prognostic risk model may predict the efficacy of immunotherapy in BC patients and provide a theoretical basis for individualized treatment of BC.Potential drugs selected based on this model may lead to new treatment options for BC patients.