Bioinformation study of immune microenvironment characteristics of disulfidptosis-related subtypes in ovarian cancer and prognostic model construction.

Abstract

Objective: Ovarian cancer significantly impacts women's reproductive health and remains challenging to diagnose and treat. Despite advancements in understanding DNA repair mechanisms and identifying novel therapeutic targets, additional strategies are still needed. Recently, a novel form of cell death called disulfidptosis, which is triggered by glucose deprivation, has been linked to treatment resistance and changes in the tumor microenvironment (TME). However, its role in ovarian cancer is not well understood.

Methods: Bioinformatics analysis was performed on RNA-seq data from TCGA and GEO databases to identify disulfidptosis-related genes in ovarian cancer. Differential expression analysis and pathway enrichment were conducted, followed by the development of a prognostic model using LASSO Cox regression, validated with GEO datasets (GSE13876, GSE26712). Clinical samples were analyzed using quantitative polymerase chain reaction (qPCR) and immunohistochemistry (IHC) to validate gene expression.

Results: This study identified disulfidptosis-related gene subtypes in ovarian cancer and demonstrated their influence on the tumor microenvironment (TME), immunotherapy responses, and patient prognosis. Six genes (IFNB1, IGF2, CD40LG, IL1B, IL21, CD38) associated with disulfidptosis were identified and incorporated into a prognostic model. This model predicted patient outcomes and was validated externally. Clinical validation showed its accuracy in predicting progression-free survival and resistance to platinum-based chemotherapy.

Conclusion: Our findings highlight the significant impact of disulfidptosis-related genes on the ovarian cancer tumor microenvironment, providing insights that could support the development of clinical evaluations and personalized treatment strategies.