Sarcopenia Six-Gene Diagnostic Signature and Causal Analysis

A six-gene diagnostic signature for sarcopenia was developed using 113 machine learning algorithms and validated via Mendelian randomization. The dataset includes results from the integration of four transcriptomic datasets, identifying 318 differentially expressed genes and 109 candidate biomarkers. Author Yaoqi Wu published the findings on figshare under a CC-BY-4.0 license in March 2026.

Use Cases

• Validate the six-gene diagnostic signature (FOXO1, ZBTB16, HOXB2, LYVE1, MGP, CYP26B1) for sarcopenia based on the described machine learning model.
• Investigate the causal role of CYP26B1 gene expression in sarcopenia risk using the provided Mendelian randomization results.
• Analyze correlations between sarcopenia-related genes and immune cell infiltration based on the CIBERSORT analysis mentioned.
• Explore candidate biomarkers related to immune regulation, muscle cytoskeleton, and retinol metabolism from the 109 identified genes.

Strengths

• Diagnostic model achieved high predictive accuracy with an AUC greater than 0.80.
• Analysis integrated four transcriptomic datasets and 113 machine learning algorithms.
• Causal relationship for CYP26B1 was confirmed via Mendelian randomization and in vitro qPCR validation.

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 small at 44.8 KB, indicating limited scope.

Provenance

Source

figshare, author Yaoqi Wu.

Collection Method

Integrated bioinformatics analysis of four transcriptomic datasets, WGCNA, differential expression, machine learning, and Mendelian randomization.

Freshness

Last updated 2026-03-18 07:37:14.