Myocardial Ischemia-Reperfusion Injury: Lactylation-Related Gene Biomarkers and Subtypes

A 2026 study by Jie Bai identified seven lactylation-related genes as diagnostic biomarkers for myocardial ischemia-reperfusion injury. The dataset likely contains results from gene expression analyses, consensus clustering, and machine learning model development. The data is shared under a CC-BY-4.0 license on figshare.

Use Cases

• Training diagnostic machine learning models based on identified lactylation-related gene expression patterns.
• Analyzing immune cell infiltration in myocardial injury based on CIBERSORT results mentioned in the description.
• Investigating molecular subtypes of myocardial ischemia-reperfusion injury based on consensus clustering results.
• Validating potential therapeutic targets based on gene enrichment analysis results for cellular division and signaling pathways.

Strengths

• Identifies seven specific lactylation-related genes (G6pd, Tkt, Eif2s2, Pabpc1, Itgb2, Cenpf, Runx2) with biomarker potential.
• Four key genes (G6pd, Itgb2, Pabpc1, Runx2) were validated via cell-based assays, supporting their diagnostic relevance.
• Analysis includes multiple methodologies: differential gene expression, coexpression network analysis, and machine learning.

Limitations

• Dataset size is 306.0 B, indicating a very limited scope, likely containing summary results rather than raw expression data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale analysis.

Provenance

Source

Gene expression data sourced from the Gene Expression Omnibus database; lactylation-related genes from GeneCards.

Collection Method

Analysis involved differential gene expression, weighted gene coexpression network analysis, consensus clustering, and machine learning model development.

Freshness

Last updated 2026-04-21 05:34:15.