ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1577781
This article is part of the Research TopicAdvances in Ovarian Cancer TherapeuticsView all articles
Integrated machine learning and single-cell analysis reveal the prognostic and therapeutic potential of SUMOylation-related genes in ovarian cancer
Provisionally accepted
- 1Sichuan University, Chengdu, China
- 2West China Second University Hospital, Sichuan University, Chengdu, Sichuan Province, China
- 3State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
- 4Civil Aviation University of China, Tianjin, China
- 5West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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Ovarian cancer (OC) has high mortality and chemoresistance rates, necessitating novel prognostic signatures and molecular biomarkers. SUMOylation, crucial in cellular stress responses, alters in various cancers. In this study, using multi-omics data, we characterized the unique features of SUMOylation in OC and revealed the close association between SUMOylation-related genes (SRGs) and OC malignancy. Integrated machine learning identified 22 prognostic SRGs based on the TCGA-OV cohort. Further single-cell analysis refined these findings, pinpointing five SRGs as novel biomarkers closely associated with OC function, metabolism and the tumor microenvironment. In cancer cells, SUMOylation levels regulate the expression of four SRGs (PI3, AUP1, CD200 and GNAS), which are closely associated with the activities of epigenetic regulation process and epithelial mesenchymal signaling. Notably, we discovered that AUP1 overexpression is a risk factor for chemoresistance of OC. In tumor microenvironment, CD8 + cytotoxic T cell with high CCDC80 (another SRG) expression exhibit inhibited cytotoxicity activities. Overall, five SRGs were identified and validated as novel prognostic and therapeutic targets, providing valuable insights for precision medicine of OC.
Keywords: Sumoylation, machine learning, single-cell RNA-seq, Clinical cohort, chemoresistance
Received: 16 Feb 2025; Accepted: 29 Apr 2025.
Copyright: © 2025 DENG, Xu, Zhang, Peng, Tan, Chen and Ma. 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: Yimei Ma, West China Second University Hospital, Sichuan University, Chengdu, 610041, Sichuan Province, China
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