A Novel Immune-Related LncRNA Pair Model to Predict the Prognosis of Triple-Negative Breast Cancer

Abstract

Abstract Background Breast cancer (BC) is the most prevalent cancer type and is the principal cause of cancer-related death in women. Anti-PD-1/PD-L1 immunotherapy has shown promising activity in metastatic TNBC, but the potential factors affecting its efficacy have not been elucidated. Immune-related long noncoding RNAs (irlncRNAs) have been reported to be involved in immune escape to influence the carcinogenic process through the PD-1/PD-L1 signaling pathway. Therefore, exploring the potential regulatory mechanism of irlncRNAs in PD-1/PD-L1 immunotherapy in TNBC is of great importance. Methods In the current study, we retrieved transcriptome profiling data from The Cancer Genome Atlas (TCGA) and identified differentially expressed irlncRNA (DEirlncRNA) pairs. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct a risk assessment model. Results Receiver operating characteristic (ROC) curve analysis indicated that the risk model may serve as a potential prediction factor in TNBC patients. Clinical stage and risk score proved to be independent prognostic predictors by univariate and multivariate Cox regression analyses. Subsequently, we investigated the correlation between the risk model and tumor-infiltrating immune cells and immune checkpoints. Finally, we identified USP30-AS1 through the StarBase and MEM databases, predicted the potential target genes of USP30-AS1, and then discovered that these target genes were closely associated with immune responses. Conclusion Although the current study did not investigate the specific mechanism by in vivo and in vitro experiments, we constructed a risk assessment model by immune-related lncRNA pairs regardless of expression levels, which has the potential to predict the efficacy of anti-PD-1/PD-L1 immunotherapy and provide a novel strategy for TNBC treatment.