ArticleJournal of inflammation research2024
Identification and Validation of Senescence-Related Signature by Combining Single Cell and Bulk Transcriptome Data Analysis to Predict the Prognosis and Identify the Key Gene CAV1 in Pancreatic Cancer.
Article in Journal of inflammation research, 2024. 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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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Article
- Cellular senescence in cancer: from mechanism paradoxes to precision therapeutics.Molecular cancer · 2025Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The role of cellular senescence in the tumor microenvironment of pancreatic cancer (PC) remains unclear, particularly regarding its impact on prognosis and immunotherapy outcomes. Methods: We utilized single-cell sequencing datasets (GSE155698 and GSE154778) for pancreatic cancer from the Gene Expression Omnibus (GEO) database and bulk RNA-seq data from the University of California, Santa Cruz (UCSC) and International Cancer Genome Consortium (ICGC) repositories, creating three patient cohorts: The Cancer Genome Atlas (TCGA) cohort, PAAD-AU cohort, and PAAD-CA cohort. Dimensionality reduction cluster analysis processed the single-cell data, while weighted gene co-expression network analysis (WGCNA) and differential expression gene analysis were applied to bulk RNA-seq data. Prognostic models were developed using Cox proportional hazards (COX) and least absolute shrinkage and selection operator (LASSO) regression, with validation through survival analysis, decision curve analysis, and principal component analysis (PCA). Tumor mutation data were analyzed using the "maftools" package, and the immune microenvironment was assessed with TIMER2 data. Results: We developed a senescence-related (SENR) six-gene prognostic signature for PC, which stratifies patients by risk, with high-risk groups showing poorer prognoses. This model also offers predictive insights into tumor mutations and immune microenvironment characteristics. Caveolin-1 (CAV1) emerged as a significant prognostic biomarker, with functional validation showing its role in promoting cancer cell proliferation and migration, highlighting its potential as a therapeutic target. Conclusion: This study provides a novel senescence-related prognostic tool for PC, enhancing patient stratification for prognosis and immunotherapy, and identifies CAV1 as a key gene with clinical significance for targeted interventions.
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