ArticleFrontiers in immunology2025
Cellular senescence-related gene signatures in idiopathic pulmonary fibrosis: insights from bioinformatics.
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 4 papers.
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.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Cellular Senescence: Emerging Therapeutic Target for Idiopathic Pulmonary Fibrosis Pathogenic Mechanisms and Therapeutic Strategies.Biomolecules & therapeutics · 2026Review
- Cellular Senescence in Idiopathic Pulmonary Fibrosis: Molecular Mechanisms, Pathogenic Networks, and Emerging Therapeutic Targets.Diseases (Basel, Switzerland) · 2026Review
- Insight into the pathogenesis of interstitial lung diseases and near-to-native lung fibrosis models.European journal of medical research · 2025Review
- Lung organoids as emerging models in idiopathic pulmonary fibrosis research: Current applications, challenges, and future directions.Therapeutic advances in respiratory diseaseReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease characterized by irreversible lung tissue scarring. Cellular senescence (CS) plays a significant role in IPF pathogenesis, yet the specific molecular mechanisms remain unclear. This study aimed to identify key CS-related differentially expressed genes (CS-DEGs) and investigate their potential as diagnostic biomarkers and therapeutic targets for IPF. Methods: Bioinformatics analysis was conducted on the GSE53845 dataset to identify CS-DEGs in IPF. Gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network analysis, and functional enrichment analyses were performed to explore the biological functions and pathways associated with CS-DEGs. External validation of the identified CS-DEGs was performed using two independent datasets, GSE32537 and GSE24206. Immunofluorescence staining on lung tissue samples from IPF patients and normal controls was performed to validate the expression of key CS-DEGs. Results: A total of 122 DEGs were identified, and 8 core CS-DEGs were selected. CDKN2A, VEGFA, SOX2, and FOXO3 were validated as key CS-DEGs, with high diagnostic potential for IPF. Functional enrichment analysis revealed their involvement in critical biological pathways, including cellular senescence, immune response, and fibrosis regulation. Immunofluorescence staining confirmed higher expression of CDKN2A and SOX2, and lower expression of FOXO3 and VEGFA in IPF lung tissues compared to normal controls. Conclusion: This study highlights the significant role of CS-related genes in the pathogenesis of IPF and identifies four key CS-DEGs (CDKN2A, SOX2, FOXO3, and VEGFA) that could serve as potential biomarkers and therapeutic targets for IPF, providing a basis for further research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.