ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1594021
This article is part of the Research TopicAdvances in Ovarian Cancer TherapeuticsView all 4 articles
A Novel Mitochondrial Autophagy and Aging-Related Gene Signature for Predicting Ovarian Cancer
Provisionally accepted
- 1Lanzhou University, Lanzhou, China
- 2Department of Gynecology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China
- 3Cuiying Biomedical Research Center, Lanzhou University Second Hospital, Chengguan, Gansu Province, China
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Ovarian cancer (OC) is a highly malignant gynecologic tumor with a poor prognosis. In recent years, mitochondrial autophagy and aging (MiAg) have been recognized as crucial pathophysiological mechanisms leading to tumorigenesis. However, the expression of MiAg-related genes in OC and their correlation with prognosis remain unclear. In this study, we used multiple machine learning methods to identify 52 MiAg genes that were differentially expressed between OC and normal ovarian tissues. Based on these 52 differentially expressed genes (DEGs), 379 OC patients were classified into three subtypes by consensus clustering analysis. Subsequently, we evaluated the prognostic value of MiAg-related genes in relation to survival in 375 OC patients with complete survival information, and developed a MiAg prognostic score model. By applying Cox and LASSO regression methods, a five-gene signature was constructed, and the 375 OC patients in the TCGA cohort were categorized into low-risk and high-risk group based on the median risk score. Meanwhile, we categorized 174 OC patients from the Gene Expression Omnibus (GEO) database into high-and low-risk groups using the median risk score of the TCGA cohort to validate the MiAg scoring model. Furthermore, we analyzed these data with unifactorial and multifactorial analyses, functional enrichment analysis, gene mutation analysis, immune infiltration, drug susceptibility analysis, cell line analysis, and immunohistochemistry data from the HPA database. In conclusion, the MiAgscore predicted patient survival, and lower MiAgscore values were associated with a better survival advantage. A comprehensive assessment of mitochondrial autophagy and cellular senescence alterations in OC could help advance disease target development and provide more effective personalized treatment strategies for OC patients.
Keywords: ovarian cancer, Mitochondrial autophagy, cellular senescence, machine learning, prognosis
Received: 15 Mar 2025; Accepted: 15 May 2025.
Copyright: © 2025 Zhang, Jin, Cao, Wang, Zhao, Qin and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Tiansheng Qin, Department of Gynecology, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China
Hongwen Zhu, Cuiying Biomedical Research Center, Lanzhou University Second Hospital, Chengguan, Gansu Province, China
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