Construction of Prognostic Signature of Breast Cancer Based on N7-Methylguanosine-Related LncRNAs and Prediction of Immune Response

Abstract Background: N7-Methylguanosine (m7G) plays an important role in the occurrence and development of tumors, and long non-coding RNA (LncRNA) is a prognostic factor for tumors. However, the predictive value of m7G-related LncRNAs in breast cancer (BC) has not been reported. The purpose of this study is to construct a predictive signature based on m7G-related long non-coding RNAs (LncRNAs) to predict the prognosis of patients with breast cancer.Methods: We obtained the RNA-seq data and corresponding clinical data of breast cancer patients from the Cancer Genome Atlas (TCGA) database. We used co-expression network analysis, least absolute shrinkage selection operator (LASSO) regression analysis, univariate Cox regression analysis, and multivariate Cox regression analysis to screen N7-Methylguanosine-Related LncRNAs signature and construct a prognostic model. Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) curve were used to evaluate the signature. Principal component analysis (PCA) and nomogram were used to verify the value of the prognostic signature. Subsequently, we used gene set enrichment analysis (GSEA) for functional analysis, single-sample gene set enrichment analysis (ssGSEA) to explore the relationship between predictive characteristics and tumor immune microenvironment in high-risk and low-risk groups, and compared the drug sensitivity between the two risk groups.Results: We constructed a signature consisting of nine m7G-related LncRNAs (LINC01871, AP003469.4, Z68871.1, AC245297.3, EGOT, TFAP2A-AS1, AL136531.1, SEMA3B-AS1, AL606834.2), which are independently related to the overall survival time (OS) of BC patients. The area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year survival rates was 0.715, 0.724, and 0.726. Kaplan-Meier analysis showed that BC patients in the high-risk group had a poorer prognosis than patients in the low-risk group. Multiple regression analysis showed that risk score was a significant independent prognostic factor for BC patients, and was significantly better than clinicopathological features. SsGSEA analysis showed that the predictive characteristics were significantly correlated with the immune status of BC patients. The immune function of low-risk BC patients was more active, and low-risk patients were more sensitive to PD1/L1 immunotherapy.Conclusions: This prognostic signature based on m7G-related LncRNAs can independently predict the prognosis and immunotherapy response of patients with BC and can be used to guide the individualized treatment of breast cancer patients.