Synergistic Profiling of Programmed Cell Death and Immune Responses Identifies a Novel Prognostic Index for Cervical Cancer

High Resolution Image Download MS PowerPoint Slide Cervical cancer (CC) is one of the leading malignancies impacting women worldwide, with a large number of cases occurring in developing countries. However, there is a need for optimal predictive models that can precisely forecast the prognosis and guide treatment selection. Programmed cell death (PCD) and immune responses (IRs) are pivotal in understanding disease progression, diagnosis, therapeutic decision-making, and risk stratification, making them promising prognostic markers. In this study, machine learning approaches were applied to identify key prognostic genes related to PCD and IR, which led to the development of three prognostic models: a PCD index, an IR index, and a combined immune–cell death index (ICDI). The PCD and IR indices showed a positive correlation in risk scores, and all three models demonstrated comparable prognostic significance. Given the biological relevance of the immune and cell death pathways, we further investigated the ICDI derived from the key genes FADD, MUC4, CLNK, and CD8B. This analysis included validation against clinical parameters, nomograms, and exploration of immune infiltration. Profiling of drug susceptibility revealed that the high-risk cohort of patients showed resistance to rapamycin and idelalisib but were sensitive to thapsigargin and linsitinib. Overall, the ICDI shows strong potential as a prognostic marker for forecasting clinical outcomes and could facilitate personalized treatment strategies for CC patients.