Data Sheet 10_Transcriptomic and experimental identification of immune- and telomere-relat

Data Sheet 10 contains transcriptomic data for pelvic organ prolapse (POP) research, identifying 864 differentially expressed genes. The dataset, authored by Rong Ma and last updated in May 2026, includes results from differential expression analysis, machine learning, and validation leading to the identification of two key biomarkers, CCNL1 and NAMPT.

Use Cases

• Biomarker validation for pelvic organ prolapse based on identified key genes CCNL1 and NAMPT.
• Pathway enrichment analysis based on results such as ubiquitin-mediated proteolysis from GSEA.
• Drug target prediction based on the 14 potential therapeutic compounds identified.
• Diagnostic model development based on the reported nomogram with an AUC of 0.847.
• Molecular regulatory network analysis based on the predicted 21 key nodes and 28 interactions.

Strengths

• Includes results from a multi-method analysis combining differential expression (864 genes), machine learning, and experimental validation via RT-qPCR.
• Identifies two specific key biomarkers (CCNL1 and NAMPT) with reported statistical significance (p < 0.05).
• Provides diagnostic model performance metrics, including a nomogram AUC of 0.847 and a strong positive correlation (r = 0.78).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 3.5 KB, indicating limited scope, likely containing summary results rather than raw transcriptomic data.

Provenance

Source

figshare, author Rong Ma.

Collection Method

Analysis of public-database transcriptome data using immune infiltration, WGCNA, differential expression analysis, and machine learning.

Freshness

Last updated 2026-05-22 06:10:49; freshness should be verified.