ArticleTranslational cancer research2025
The development and validation of a novel programmed cell death-related signature based on machine learning analysis of 15 programmed cell death patterns for predicting prognosis and therapeutic response in high-grade serous ovarian carcinoma.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: High-grade serous ovarian carcinoma (HGSOC) is the most prevalent and aggressive histological type of ovarian cancer. Predicting the prognosis of HGSOC remains challenging. Dysfunction of programmed cell death (PCD) is confirmed to be involved in the development and progression of ovarian cancer. The study aims to develop a PCD-related prognostic model to predict prognosis and treatment response in HGSOC patients. Methods: Through mining The Cancer Genome Atlas of Ovarian Serous Carcinoma (TCGA-OV) dataset, we characterized the molecular patterns of HGSOC based on prognostic PCD-related genes via consensus cluster analysis and established a prognostic signature using least absolute shrinkage and selection operator (LASSO) Cox and multivariate step Akaike information criterion (AIC) analyses. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were used to evaluate its performance. The robustness of the signature was validated by GSE32062, GSE9891 [Gene Expression Omnibus (GEO)] and the International Cancer Genome Consortium (ICGC) database-Australia (ICGC-AU) datasets. The relationship between different molecular patterns and risk groups was visualized via a Sankey diagram. Differences in clinical features, pathways, TIME, and chemotherapy sensitivity were analyzed between the different risk groups. Results: A total of 26 PCD-related genes with prognostic value were identified. Through the unsupervised clustering approach, three distinct molecular patterns (C1, C2, and C3) were discerned. K-M and TIME analysis indicated C2 was an immune-active subtype with favorable prognosis. Subsequently, a seven-gene prognostic signature was constructed. K-M analysis and time-dependent ROC curves demonstrated the excellent prognostic value of the signature [area under the curve (AUC) Conclusions: We established a novel PCD-related prognostic signature that can be used to effectively predict HGSOC prognosis and treatment response.
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