ArticleScientific reports2025
Unveiling a novel model of cell senescence-related genes for prognostic assessment and immunotherapeutic insights in gastric cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Senescence-associated gene signatures predict survival in lung cancer: a multi-cohort analysis.GeroScience · 2026Article
- The Identification of LA-tumor associated macrophages in immune modulation via amyloid-beta precursor protein/CD74 signal pathway in gastric cancer: a predictive module and machine learning.Frontiers in oncology · 2025Article
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4 authors.
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Abstract
Recent studies have shed light on the dysregulated nature of cell senescence in many cancers, with implications for tumor immunity and prognosis. However, it is still unclear what role cellular senescence plays in stomach adenocarcinoma (STAD). To address this gap, we investigated the impact of cellular senescence on gastric cancer and its potential prognostic and therapeutic significance. The mRNA expression patterns, gene mutations, and clinical information of STAD were obtained from the cancer genome atlas (TCGA) and gene expression omnibus (GEO). Differentially expressed senescence-related genes were identified between gastric cancer tissues and normal tissues, then the prognostic value and functional roles of these genes in immunotherapy were systematically investigated by bioinformatics approaches. To authenticate the dysregulated genes identified within our prognostic signature, we conducted real-time quantitative PCR. Moreover, we verified gene expression patterns in both normal and tumor samples and performed in vitro experiments to modulate gene expression, assessing its impact on cell proliferation and invasion. Leveraging least absolute shrinkage and selection operator (LASSO) regression analysis, we successfully established a prognostic signature based on cell senescence-related genes. This signature categorized patients into high and low-risk groups, with the high-risk group exhibiting decreased overall survival likelihood compared to the low-risk group. Notably, these groups demonstrated distinct tumor microenvironment features and immune cell infiltration. Furthermore, patients in the high-risk group exhibited poorer responses to treatment compared to those in the low-risk group. To facilitate clinical application, we developed a nomogram for STAD prognosis prediction. By employing this cell senescence-related signature, we could accurately predict prognosis in STAD and tailor individualized therapeutic strategies, including chemotherapy and immunotherapy.
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