Evidence map›Paper›PMID 42228240›Full record

ArticleGeroScience2026

The role of cellular senescence in immune-metabolic features and prognosis of ovarian cancer: an integrated analysis based on single-cell sequencing and multi-omics data.

Yuan Li, Mengying Bai, Ziqiong Zhou, Wenbo Wu, Haifeng Wu, Shuyi Ling, Liping Wang, Yuehui Zheng

Abstract read
In one paragraph

Article in GeroScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Yuan Li *The Fourth Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, Guangdong, China.
Mengying Bai *The Fourth Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, Guangdong, China.
Ziqiong Zhou *The Fourth Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, Guangdong, China.
Wenbo WuThe Fourth Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, Guangdong, China.
Haifeng WuThe Fourth Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, Guangdong, China.
Shuyi LingThe Fourth Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, Guangdong, China. ling_shuyi@163.com.
Liping WangReproductive Medicine Centre, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital, Shenzhen, China. wlilyu@hotmail.com.
Yuehui ZhengThe Fourth Clinical Medical College of Guangzhou, University of Traditional Chinese Medicine, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, 518033, Guangdong, China. yuehuizheng@163.com.

Funding

2024 Guangzhou University of Chinese Medicine "Jie-bang Gua-shuai" Graduate Innovation Capacity Enhancement Program A3-0317-24-429-015Basic Research Scheme of Shenzhen Science and Technology Innovation Commission JCYJ20230807094815031)Basic Research Scheme of Shenzhen Science and Technology Innovation Commission (JCYJ20240813152305008National Nature Science Foundation of China No. 82474232Scientific Research Project of Shenzhen Association of Chinese Medicine NO.2024077F
6 · The paper itself

Abstract

Ovarian cancer is characterized by a high recurrence rate, and platinum resistance often contributes to poor patient outcomes. Cellular senescence can influence the tumor microenvironment, metabolic status, and immune regulation. However, the role of senescence-associated genes (SAGs) in the heterogeneity, prognosis, and treatment-related features of ovarian cancer remains to be further clarified. This study integrated data from TCGA, GEO, and single-cell RNA sequencing to systematically analyze the expression patterns, single-cell distribution, molecular subtypes, and associations of SAGs with prognosis and the immune microenvironment in ovarian cancer. Consensus clustering was used to identify senescence-related molecular subtypes, and an SAG-related prognostic risk model was constructed and validated. Associations between the risk score and platinum response, tumor mutational burden (TMB), immune cell infiltration, and immune checkpoint expression were further evaluated. SAGs were differentially expressed between ovarian cancer and normal ovarian tissues and were mainly enriched in cell cycle, DNA damage response, and immune-related pathways. Single-cell analysis showed that senescence signals were mainly distributed in cancer-associated fibroblasts, malignant cells, and selected immune cell populations. According to SAG-based consensus clustering, patients were classified into three molecular subtypes with differences in metabolic activity, DNA repair, immune microenvironment, and genomic stability, although no significant differences in OS were observed among subtypes. An eight-gene senescence-related risk model showed prognostic relevance in the TCGA cohort, and additional analyses in two GEO cohorts supported the prognostic value of the selected SAGs. The high-risk group was associated with greater TMB, platinum resistance-related features, increased immune and stromal scores, reduced tumor purity, a lower proportion of CD8⁺ T cells, and higher expression of regulatory T cells, macrophages, and immune checkpoints, suggesting a link with an immunosuppressive tumor microenvironment. The current findings suggest that senescence-associated molecular features are associated with the biological heterogeneity, prognostic stratification, platinum response, and status of the immune microenvironment in ovarian cancer. The SAG-related risk model may provide clues for prognostic assessment and future studies on treatment-response stratification in ovarian cancer, but further experimental and clinical validation is required.

Indexed as

Cellular SenescenceOvarian NeoplasmsFemaleHumansMultiomicsPrognosisSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisTumor MicroenvironmentCellular senescenceImmune infiltrationOvarian cancerPrognostic modelSingle-cell RNA sequencingTumor microenvironment

Identifiers

PMID42228240
PMCPMC13638873

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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 OpenQuestion graph.