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

Abstract

Background: Breast 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. Method: A 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. Result: A 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. Conclusions: Our 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.