ArticleFrontiers in immunology2023
Integrating scRNA and bulk-RNA sequencing develops a cell senescence signature for analyzing tumor heterogeneity in clear cell renal cell carcinoma.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Single cell and multi-omics analysis reveal senescence-related molecular subtypes and a prognostic signature in clear cell renal cell carcinoma.Discover oncology · 2026Article
- Multiomics combined drugs to explore the potential mechanism and treatment of ankylosing spondylitis.Journal of orthopaedic surgery and research · 2026Article
- A Manifold-Based Measure of Transcriptional Entropy for Quantifying Aging in Single Cells.bioRxiv : the preprint server for biology · 2026Article
- Monocyte/macrophage-derived NLRP3 Promotes the Onset and Progression of Ankylosing Spondylitis Via the NOD-like Receptor Pathway.Journal of clinical immunology · 2025Article
- Cellular senescence in cancer: from mechanism paradoxes to precision therapeutics.Molecular cancer · 2025Review
- A machine learning approach identifies cellular senescence on transcriptome data of human cells in vitro.GeroScience · 2025Article
- A transcriptome-based human universal senescence index (hUSI) robustly predicts cellular senescence under various conditions.Nature aging · 2025Article
- Novel molecular hepatocellular carcinoma subtypes and RiskScore utilizing apoptosis-related genes.Scientific reports · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Introduction: Cellular senescence (CS) plays a critical role in cancer development, including clear cell renal cell carcinoma (ccRCC). Traditional RNA sequencing cannot detect precise molecular composition changes within tumors. This study aimed to analyze cellular senescence's biochemical characteristics in ccRCC using single RNA sequencing (ScRNA-seq) and traditional RNA sequencing (Bulk RNA-seq). Methods: Researchers analyzed the biochemical characteristics of cellular senescence in ccRCC using ScRNA-seq and Bulk RNA-seq. They combined these approaches to identify differences between malignant and non-malignant phenotypes in ccRCC across three senescence-related pathways. Genes from these pathways were used to identify molecular subtypes associated with senescence, and a new risk model was constructed. The function of the gene DUSP1 in ccRCC was validated through biological experiments. Results: The combined analysis of ScRNA-seq and Bulk RNA-seq revealed significant differences between malignant and non-malignant phenotypes in ccRCC across three senescence-related pathways. Researchers identified genes from these pathways to identify molecular subtypes associated with senescence, constructing a new risk model. Different subgroups showed significant differences in prognosis level, clinical stage and grade, immune infiltration, immunotherapy, and drug sensitivity. Discussion: Senescence signature markers are practical biomarkers and predictors of molecular typing in ccRCC. Differences in prognosis level, clinical stage and grade, immune infiltration, immunotherapy, and drug sensitivity between different subgroups indicate that this approach could provide valuable insights into senescence-related treatment options and prognostic assessment for patients with ccRCC. The function of the gene DUSP1 in ccRCC was validated through biological experiments, confirming its feasibility as a novel biomarker for ccRCC. These findings suggest that targeted therapies based on senescence-related mechanisms could be an effective treatment option for ccRCC.
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