Efferocytosis-related signatures identified via Single-cell analysis and machine learning predict TNBC outcomes and immunotherapy response

Triple-negative breast cancer (TNBC) is characterized by poor prognosis and limited targeted treatment options. Efferocytosis, an essential immune mechanism for the clearance of apoptotic cells, is increasingly recognized as a key contributor to tumor immune evasion. This study aimed to identify key efferocytosis-related genes in TNBC, investigate their impact on the tumor microenvironment and immunotherapy responses, and construct a prognostic model to inform and optimize treatment strategies. RNA sequencing data and clinical information for patients with TNBC were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Machine learning models were employed to derive efferocytosis-related signatures to predict clinical outcomes and immunotherapy responses. Eight efferocytosis-related genes were identified, considered efferocytosis-related gene signatures herein: P2RX1, IFNG, IL1A, CD93, XKR8, SIAH2, F2RL1, and TLR4. Using the individual risk scores derived from this model, patients were stratified into high- and low-risk groups, revealing significant differences in immune infiltration and immuno-therapy response. Our study highlights the predictive significance of efferocytosis in assessing chemotherapy sensitivity, emphasizing the pivotal role of the immune microenvironment in mediating drug resistance. Moreover, we identified potential targets for immunotherapeutic strategies in the treatment of TNBC.