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Updated: Apr 16, 2026
Preparation of Mitochondria from Ovarian Cancer Tissues and Control Ovarian Tissues for Quantitative Proteomics Analysis
Published on: November 18, 2019
Integrated machine learning algorithms for prediction of prognosis in ovarian cancer patients based on
Ke Xu1,2, Ning Zhang1,3, Haoyu He1,2
1Department of Obstetrics and Gynecology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Abstract:
Mitochondrial dysfunction drives ovarian cancer (OC) progression. This study constructed a robust prognostic model (MITO-OC) based on mitochondria-related genes using ten machine-learning algorithms on TCGA, ICGC, and GEO data. We identified 241 differentially expressed genes and built the optimal MITO-OC model using StepCox[forward] and RSF algorithms (C-index=0.73). The model accurately predicts patient overall survival and strongly correlates with tumor immune infiltration. Furthermore, single-cell and pan-cancer analyses highlighted CHCHD2 as a critical component in OC and other tumors. MITO-OC provides a highly effective, personalized prognostic tool and reveals underlying metabolic mechanisms for OC clinical management.
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