Integrated machine learning algorithms for prediction of prognosis in ovarian cancer patients based on mitochondrial-related genes.

IF 1.6 4区医学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Computer Methods in Biomechanics and Biomedical Engineering Pub Date : 2026-04-15 DOI:10.1080/10255842.2026.2658118

Ke Xu, Ning Zhang, Haoyu He, Hua Zhang, Yuzhou Gao, Run Miao, Zhihao Xu, Yuchen Zhang, Dongmei Ji

引用次数: 0

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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基于线粒体相关基因预测卵巢癌患者预后的综合机器学习算法。

线粒体功能障碍驱动卵巢癌（OC）进展。本研究在TCGA、ICGC和GEO数据上使用10种机器学习算法构建了基于线粒体相关基因的稳健预后模型（MITO-OC）。我们鉴定了241个差异表达基因，并使用StepCox[forward]和RSF算法构建了最优的MITO-OC模型（C-index=0.73）。该模型准确预测了患者的总体生存期，并与肿瘤免疫浸润密切相关。此外，单细胞和泛癌分析强调，CHCHD2是OC和其他肿瘤的关键成分。MITO-OC提供了高效、个性化的预后工具，揭示了OC临床管理的潜在代谢机制。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Methods in Biomechanics and Biomedical Engineering 工程技术-工程：生物医学

CiteScore

4.10

自引率

6.20%

发文量

179

审稿时长

4-8 weeks

期刊介绍： The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.