864 differentially expressed genes, including 833 upregulated and 31 downregulated, were identified from public-database transcriptome data for pelvic organ prolapse (POP). The study, authored by Rong Ma and last updated in May 2026, used machine learning to pinpoint CCNL1 and NAMPT as key biomarkers, validated by RT-qPCR. It provides a computational foundation for understanding POP pathogenesis and identifying therapeutic targets.
Use Cases
- Biomarker validation for pelvic organ prolapse based on the identified key genes CCNL1 and NAMPT.
- Pathway enrichment analysis based on findings such as ubiquitin-mediated proteolysis from GSEA.
- Drug target prediction based on the 14 potential therapeutic compounds identified.
- Diagnostic model development based on the constructed nomogram with an AUC of 0.847.
- Molecular regulatory network analysis based on the predicted interactions with 21 key nodes and 28 interactions.
Strengths
- Includes validation of key biomarkers (CCNL1 and NAMPT) via RT-qPCR, as stated in the results.
- Analysis includes a diagnostic nomogram with a reported AUC of 0.847.
- Dataset is openly licensed under CC-BY-4.0, facilitating reuse.
Limitations
- Row count is unknown, which may limit suitability assessment.
- Column-level documentation is absent; field semantics must be inferred after download.
- The dataset is very small at 12.2 KB, indicating limited scope.
Provenance
- Source
- figshare, authored by Rong Ma.
- Collection Method
- Analysis of public-database transcriptome data using differential expression analysis, immune infiltration, WGCNA, and machine learning.
- Freshness
- Last updated 2026-05-22 06:10:44; freshness should be verified.