Six-Gene Diagnostic Signature for Sarcopenia from Integrated Bioinformatics Analysis

A six-gene diagnostic signature (FOXO1, ZBTB16, HOXB2, LYVE1, MGP, CYP26B1) was identified for sarcopenia using 113 machine learning algorithms and four transcriptomic datasets. The model achieved high predictive accuracy (AUC >0.80), and Mendelian randomization confirmed a causal role for CYP26B1. The dataset, created by Yaoqi Wu and shared on figshare, was last updated on March 18, 2026.

Use Cases

• Validate the six-gene diagnostic signature (FOXO1, ZBTB16, HOXB2, LYVE1, MGP, CYP26B1) for sarcopenia risk prediction.
• Investigate the causal relationship between CYP26B1 gene expression and sarcopenia risk using Mendelian randomization results.
• Analyze correlations between sarcopenia-related genes and immune cell infiltration patterns from CIBERSORT analysis.
• Explore the roles of immune regulation, muscle cytoskeleton, and retinol metabolism pathways in sarcopenia pathogenesis.

Strengths

• Diagnostic model built using 113 machine learning algorithms, achieving an AUC greater than 0.80.
• Analysis integrates four transcriptomic datasets, 318 differentially expressed genes, and 109 candidate biomarkers.
• Findings are supported by in vitro validation using C2C12 cells and qPCR experiments.

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 9.9 KB, indicating limited scope.

Provenance

Source

figshare, author Yaoqi Wu.

Collection Method

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

Freshness

Last updated 2026-03-18 07:37:15; freshness should be verified.