Evidence map›Paper›PMID 40534859›Full record

ArticleFrontiers in immunology2025

Integrated machine learning and single-cell analysis reveal the prognostic and therapeutic potential of SUMOylation-related genes in ovarian cancer.

Zhengrong Deng, Yicong Xu, Peidong Zhang, Yixiang Peng, Jiaxing Tan, Zihang Chen, Yimei Ma

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Zhengrong Deng *Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University) Ministry of Education, West China Second University Hospital of Sichuan University, Chengdu, China.
Yicong Xu *Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University) Ministry of Education, West China Second University Hospital of Sichuan University, Chengdu, China.
Peidong Zhang *State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Yixiang PengDepartment of Computer Science and Technology, Civil Aviation University of China, Tianjin, China.
Jiaxing TanDivision of Nephrology, Department of Medicine, West China Hospital, Sichuan University, Chengdu, China.
Zihang ChenDepartment of Pathology, West China Hospital, Sichuan University, Chengdu, China.
Yimei MaKey Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University) Ministry of Education, West China Second University Hospital of Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Ovarian cancer (OC) exhibits high mortality and chemoresistance rates, underscoring the urgent need for precise prognostic biomarkers and novel therapeutic targets. SUMOylation, crucial in cellular stress responses, is frequently dysregulated in various cancers. This study aims to characterize SUMOylation and its regulators in OC and identify potential biomarkers and therapeutic targets. Methods: In this study, using multi-omics data, we characterized the unique features of SUMOylation in OC and revealed the association between SUMOylation-related genes (SRGs) and OC malignancy. We conducted integrated machine learning and single-cell RNA sequencing data analysis to identify key SRGs and explored their functional characteristics. The prognostic potential of these SRGs was confirmed in ID8 mouse models and in samples from 213 OC patients at West China Second Hospital. Results: An integrated machine learning framework identified 22 prognostic-related SRGs from the TCGA-OV cohort. Further single-cell analysis refined these findings, pinpointing five SRGs as biomarkers closely associated with OC cell function, metabolism and the tumor microenvironment. In cancer cells, the expression of four SRGs ( Discussion: Overall, five SRGs were identified and further evaluated as potential prognostic and therapeutic targets, offering deeper insights into precision oncology for OC.

Indexed as

Biomarkers, TumorMachine LearningOvarian NeoplasmsSingle-Cell AnalysisSumoylationAnimalsFemaleGene Expression Regulation, NeoplasticHumansMicePrognosisTumor MicroenvironmentBiomarkers, Tumorchemoresistanceclinical cohortmachine learningsingle-cell RNA-seqsumoylation

Identifiers

PMID40534859
PMCPMC12174102

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.