Identification of CD4+ Conventional T Cells-Related lncRNA Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Breast Cancer

BackgroundBreast cancer (BC) is one of the most common malignancies in women, and long non-coding RNAs (lncRNAs) are key regulators of its development. T cells can recognize and kill cancer cells, and CD4(+) T conventional (Tconv) cells are the main orchestrators of cancer immune function. However, research on CD4(+) Tconv-related lncRNAs (CD4TLAs) prognostic signature in patients with BC is still lacking. MethodA TCGA database and a GEO database were used to collect the BC patients. Through LASSO Cox regression analysis CD4TLAs-related prognostic models were further constructed, and risk scores (RS) were generated and developed a nomogram based on CD4TLAs. The accuracy of this model was validated in randomized cohorts and different clinical subgroups. Gene set enrichment analysis (GSEA) was used to explore potential signature-based functions. The role of RS has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy. ResultA prognostic model based on 16 CD4TLAs was identified. High-RS was significantly associated with a poorer prognosis. RS was shown to be an independent prognostic indicator in BC patients. The low-RS group had a significant expression of immune infiltrating cells and significantly enriched immune-related functional pathways. In addition, the results of immunotherapy prediction indicated that patients with low-RS were more sensitive to immunotherapy. ConclusionsOur signature has potential predictive value for BC prognosis and immunotherapy response. The findings of this work have greatly increased our understanding of CD4TLA in BC.