Prediction of prognosis and treatment effect of cervical cancer by immunogenic cell death-related genes

Background Immunogenic cell death (ICD) is a potential mechanism that mediates adaptive immune responses during anticancer therapy. However, expression patterns and clinical implications of ICD related genes in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) remain unclear. This study aimed to investigate the associations of 34 ICD related genes with immune characteristics, prognosis, and immune checkpoint blocking (ICB) efficacy in CESC and to predict drug responses across different risk subgroups. Methods Ribonucleic acid sequencing (RNA-seq) transcriptomic profiles, standard normalized variate (SNV) data, and corresponding clinicopathological information were retrieved from The Cancer Genome Atlas (TCGA). Consensus clustering was applied to identify two ICD related molecular subtypes. A prognostic signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression and validated using immunohistochemistry (IHC) and receiver operating characteristic (ROC) curve analyses. Immunotherapy responses were predicted based on the risk score of the established model. Results The ICD-high subtype (C2) was associated with favorable clinical outcomes and enhanced immune activation. The prognostic signature comprised six genes: ATG5, FOXP3, IFNG, IL1B, PDIA3, and TNF. IHC confirmed higher expression of ATG5 and PDIA3 in cervical cancer (CC) tissues than in normal cervical tissues, and ROC curves verified the predictive accuracy. Furthermore, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) scores suggested that low risk patients exhibited better responses to anti programmed cell death protein-1 (PD-1) immunotherapy. Conclusions This study characterized the expression profiles of ICD related genes and established a novel ICD based classification system for CESC. Comprehensive analyses of the tumor immune microenvironment further validated the capacity of this signature in predicting prognosis and immunotherapy benefits, which may help identify CC patients who are most likely to benefit from immunotherapy.