Data Sheet 5_Transcriptomic and experimental identification of immune- and telomere-relate

A 2026 computational study by Rong Ma identifies potential biomarkers for pelvic organ prolapse. The dataset contains results from differential expression analysis, machine learning, and validation, pinpointing two key genes. It was published on figshare under a CC-BY-4.0 license.

Use Cases

• Validate identified biomarkers (CCNL1, NAMPT) for pelvic organ prolapse based on the described differential expression and ROC analysis.
• Explore molecular regulatory networks based on the predicted interactions with 21 key nodes and 28 interactions.
• Investigate enriched biological pathways like ubiquitin-mediated proteolysis based on the Gene Set Enrichment Analysis (GSEA) results.
• Screen for potential therapeutic compounds based on the 14 drug candidates predicted in the study.

Strengths

• Identifies two specific biomarker genes (CCNL1 and NAMPT) with reported statistical significance (p < 0.05) and validation via RT-qPCR.
• Reports a diagnostic nomogram with a calculated AUC of 0.847 and a strong positive correlation (r = 0.78, p < 0.001) between key genes.
• Analysis is based on 864 differentially expressed genes identified from public transcriptome databases.

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 (684.0 B), indicating limited scope, likely containing summary results rather than raw expression data.

Provenance

Source

figshare, author Rong Ma.

Collection Method

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

Freshness

Last updated 2026-05-22 06:10:46; freshness should be verified.