Identification of clusters related to programmed cell death and potential prognostic biomarkers for immunotherapy response in endometrial cancer

Abstract

Abstract Endometrial cancer (EC) is one of the few malignancies with increasing incidence and mortality rates. Targeted therapy and immunotherapy have become pivotal treatment strategies for EC patients. However, the current methods and biomarkers for predicting immunotherapy responses and prognosis are remain limited. Programmed cell death (PCD) pathways play a crucial role in cancer development and progression and may serve as prognostic markers and indicators of drug sensitivity in EC. In our study, we integrated multiple PCD pathways and comprehensive multi-omics datasets from TCGA-EC and GEO databases. By analyzing distinct PCD signatures, we discovered two major EC subgroups with distinctive prognoses, tumor microenvironment (TME) profiles, and responses to immunotherapy. To further investigate the cellular basis of these PCD patterns, single-cell RNA sequencing analysis was conducted to explore tumor heterogeneity in PCD characteristics across EC subpopulations. Further investigation revealed seven key PCD-associated genes (HIF3A, ACTL8, SIRPG, FBN3, ARHGAP30, CD6, and P2RY13) that formed the basis for a novel prognostic scoring system—risk score (RS). Our findings showed that patients with lower risk scores had better survival rates and improved immunotherapy outcomes. Conversely, patients with higher risk scores experienced poor clinical outcomes and reduced immunotherapy efficacy, although alternative therapies such as docetaxel and olaparib demonstrated potential therapeutic benefits. Overall, the RS provides a valuable tool for early prognosis prediction and for identifying patients who may benefit from immunotherapy.

Keywords

• Bioinformatics
• Endometrial Cancer
• Immunotherapy
• Prognosis