A bioinformatics analysis linked -related gene patterns to benign prostatic hyperplasia and proposed several potential biomarkers.
Evidence
This computational study integrated GEO datasets GSE119195 and GSE7307, performed differential expression, enrichment, protein-interaction, ROC, miRNA, and transcription-factor network analyses, and highlighted hub genes including IGF1, SOX2, ID1, WNT2, LGALS3, and KL.
Caveat
The abstract reports dataset-derived diagnostic and pathway signals without prospective clinical validation or experimental testing of the proposed biomarkers.
Simplified
Benign prostatic hyperplasia (BPH) is a prevalent condition among aging men, characterized by prostate enlargement and urinary symptoms. Despite its common occurrence, the molecular mechanisms, particularly those related to , are not well understood. This study aims to pinpoint and analyze cellular senescence-related (CSRDEGs) in BPH. We integrated data from GEO datasets GSE119195 and GSE7307, addressing batch effects to ensure data reliability. Differential gene expression analysis revealed key CSRDEGs, which were further analyzed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, highlighting their involvement in significant biological processes and pathways such as inflammation, cell cycle regulation, and extracellular matrix organization. Protein-protein interaction networks were constructed, and hub genes, including IGF1, SOX2, ID1, WNT2, LGALS3, and KL, were determined and analyzed for their diagnostic potential through receiver operating characteristic curve analysis. Regulatory network analyses of these hub genes exhibited intricate mRNA-miRNA and mRNA-TF interactions, providing insights into post-transcriptional and transcriptional regulation mechanisms. Our findings underscore the crucial role of cellular senescence in BPH pathogenesis and propose novel biomarkers and therapeutic targets. This study enhances our understanding of BPH at the molecular level, offering a foundation for future research aimed at improving diagnosis and treatment.
Key numbers
20
Number of identified
Intersected from and -related genes.
0.848
Diagnostic accuracy of LGALS3
Assessed through receiver operating characteristic curve analysis.
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