Immunoprognostic Model Construction of m6A-Associated lncRNAs in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a specific subtype of breast cancer. Because of its heterogeneity and the lack of reliable molecular targets for effective targeted therapy, the survival rate of TNBC patients remains low. N6-methyladenosine (m6A) and long-stranded non-coding ribonucleic acid (lncRNA) play a critical role in the prognostic value and immunotherapeutic response of TNBC. Therefore, it is important to identify m6A-associated lncRNAs in TNBC patients. In this study, m6A-associated lncRNAs were analyzed and obtained by co-expression. Univariate Cox, random survival forest (RSF), least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were then performed to construct m6A-associated lncRNA models. Kaplan-Meier (KM) survival analysis, principal component analysis (PCA), functional enrichment analysis, and column line plots were then used to analyze the risk models. Finally, potential immunotherapy profiles and drug sensitivity predictions for the model were also discussed. A risk model containing 3 m6A-associated lncRNAs was identified as an independent predictor of prognosis. By regrouping patients using this model, we can differentiate patients more effectively in terms of their response to immunotherapy. Drug candidates targeting TNBC subtype differentiation were identified using the pRRhetic algorithm to estimate treatment response based on the half-maximal inhibitory concentration (IC50) available in the Genomics of Drug Sensitivity in Cancer (GDSC) database for each sample. The findings suggest that this m6A-based lncRNA risk model may be promising for clinical prediction of prognosis and immunotherapy response in TNBC patients.