Construction of an immunogenic cell death related gene signature: A new prognostic model for small cell lung cancer

Abstract

Abstract Small cell lung cancer (SCLC) is a highly malignant cancer with poor prognosis. Therefore, more accurate models to identify high-risk patients are essential for facilitating personalized management of SCLC. Immunogenic cell death (ICD) is a process that stimulates robust anti-tumor immune responses and holds promising implications for cancer treatment. However, the expression of ICD-related genes in SCLC and their correlations with prognosis remain unclear. In this study, we employed a series of bioinformatic and machine learning approaches to establish an ICD-related risk score (ICDRS) and classified SCLC patients into low- or high-risk subgroups. Patients in the high-risk subgroup exhibited significantly lower survival probabilities, and the prognostic value of ICDRS was validated in independent cohorts. Furthermore, GSEA and tumor microenvironment (TME) analysis indicated that tumor proliferation and cancer-associated fibroblasts were enriched in the high-risk subgroup, while immune-realted scores were lower in high-risk subgroup. Overall, ICDRS could serve as a useful prognostic biomarker for overall survival (OS) in SCLC.