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ORIGINAL RESEARCH article
Front. Oncol.
Sec. Pharmacology of Anti-Cancer Drugs
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1592426
This article is part of the Research TopicDecoding Tumor Drug Resistance: Machine Learning’s Role from Molecules to TreatmentView all 10 articles
Development of CSOARG: A Single-Cell and Multi-Omics-Based Machine Learning Model for Ovarian Cancer Prognosis and Drug Response Prediction
Provisionally accepted- Jinshan Hospital, Fudan University, Shanghai, China
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Objective: Ovarian cancer is the most deadly gynaecological malignancy. This study aims to generate a predictive model for prognosis and therapeutic responses in ovarian cancer using defined specific genes.The cellular senescence-associated gene sets and the ovarian aging-删除了: tumor associated gene sets from the TCGA and GEO databases were analyzed using Cox regression with LASSO approach and employed to construct a prognostic model of Cellular Senescence and Ovarian Aging-Related Genes (CSOARG). Immunology analysis, functional enrichment, single-cell analysis, and therapeutic responses of ovarian cancer were conducted using the data from public databases. A machine learning model based on the expression levels of prognostic genes combined with clinical features was developed to predict the five-year overall survival. Patients with high-and low-risk scores were separated by the median risk score. Defined genes were verified by qRT-PCR and Western blot. The cellular behavior was evaluated by CCK-8, migration, and wound-healing assays.Results: After a series of calculations, an 8-gene CSOARG model was generated.CSOARG was correlated with genomic instability that harbored homologous recombination deficiency. The area under the curve (AUC) for 5-year overall survival was 0.68. Patients in the high-risk score group had a higher IC50 of chemotherapeutic and targeted therapeutical agents, worse responses to chemotherapy and immunotherapy, and exhibited a poor prognosis. A hub gene WNK1 was validated and acted as an oncogene affecting ovarian cancer cell viability and migration.These findings demonstrate that a novel CSOARG model can effectively predict the prognosis and therapeutical responses of patients with ovarian cancer, which may assist clinicians in implementing better practices.
Keywords: drug sensitivity, Gene signature, Immunotherapy, ovarian tumor, prognosis, senescence
Received: 12 Mar 2025; Accepted: 21 Apr 2025.
Copyright: © 2025 Chen, Guan, Zhang, LI, Fang, Guan, Lu and Xu. 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: Guoxiong Xu, Jinshan Hospital, Fudan University, Shanghai, China
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