Constructing a Prognostic Model of Uterine Corpus Endometrial Carcinoma and Predicting Drug-Sensitivity Responses Using Programmed Cell Death-Related Pathways

Abstract

Background: Uterine Corpus Endometrial Carcinoma (UCEC) is the most common type of cancer that develops in the uterus, specifically originating from the endometrium, the inner lining of the uterus. Programmed cell death (PCD) is a highly regulated process that eliminates damaged, aged, or unwanted cells in the body. Dysregulation of PCD pathways can contribute to the formation and progression of various cancers, including UCEC.

Methods: Fourteen PCD pathways (autophagy-dependent cell death, alkaliptosis, apoptosis, cuproptosis, entotic cell death, ferroptosis, immunogenic cell death, lysosome-dependent cell death, MPT-driven necrosis, necroptosis, netotic cell death, oxeiptosis, parthanatos, and pyroptosis) were involved in building a prognostic signature. The model was trained and tested using data from the TCGA-UCEC and validated with the GSE119041 dataset.

Results: A 12-gene PCD signature (DRAM1, ELAPOR1, MAPT, TRIM58, UCHL1, CDKN2A, CYFIP2, AKT2, LINC00618, TTPA, TRIM46, and NOS2) was established and validated in an independent dataset. UCEC patients with a high PCD score (PCDS) exhibited worse prognosis. Furthermore, PCDS was found to be associated with immune related cells and key tumor microenvironment components through multiple methods. It was observed that UCEC patients with a high PCD score may not benefit from immunotherapy, but some chemo drugs like Bortezomib may be useful.

Conclusion: In conclusion, a novel PCD model was established by comprehensively analyzing diverse cell death patterns. This model accurately predicts the clinical prognosis and drug sensitivity of UCEC. The findings suggest that the PCD signature can serve as a valuable tool in assessing prognosis and guiding treatment decisions for UCEC patients.