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Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse
Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse
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Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse
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Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse
Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse

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Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse
Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse
Journal Article

Transcriptomic and experimental identification of immune- and telomere-related genes in pelvic organ prolapse

2026
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Overview
BackgroundPelvic organ prolapse (POP) is a prevalent disease among women, and immune cell and telomere have potential associations with the pathogenesis of POP. The identification and validation of immune cell-related genes (ICRGs) and telomere-related genes (TRGs) in POP are of great significance for elucidating its mechanisms, screening diagnostic key genes, and identifying therapeutic targets.MethodsIn this study, first, ICRGs were obtained based on immune infiltration and WGCNA; key genes related to immune cell and telomere in POP were identified from public-database transcriptome data through differential expression analysis, machine learning, expression level analysis and ROC analysis. Subsequently, a comprehensive analysis including nomogram construction, correlation analysis, GSEA, molecular regulatory network construction, and drug prediction explored the molecular mechanisms of these key genes in POP.ResultsWe identified 864 differentially expressed genes (DEGs), including 833 upregulated and 31 downregulated genes. The intersection of DEGs, telomere-related genes (TRGs), and immune cell-related genes (ICRGs) yielded six candidate genes. Machine learning further pinpointed CCNL1 and NAMPT as key biomarkers, which were significantly upregulated in POP samples (p < 0.05) and validated by RT-qPCR. Subsequently, a comprehensive analysis revealed their diagnostic potential via a nomogram (AUC: 0.847), a strong positive correlation (r = 0.78, p < 0.001), and enrichment in pathways such as ubiquitin-mediated proteolysis (GSEA). Molecular regulatory network construction predicted interactions with 21 key nodes and 28 interactions, and drug prediction identified 14 potential therapeutic compounds. These findings provide a theoretical basis for understanding POP pathogenesis and identifying novel therapeutic targets.ConclusionThis study identifies CCNL1 and NAMPT as novel immune- and telomere-related biomarkers for POP. These findings provide potential targets for diagnostic development and lay a computational foundation for future therapeutic strategies, although experimental validation is required to confirm their causal roles.