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, shared on figshare, contains results from gene expression analysis, coexpression network analysis, and machine learning model development. It includes findings validated via cell-based assays and immune infiltration evaluation.

Use Cases

• Identifying diagnostic biomarkers for myocardial ischemia-reperfusion injury based on lactylation-related gene expression.
• Classifying molecular subtypes of myocardial injury using consensus clustering results.
• Developing machine learning models for early diagnosis based on identified gene signatures.
• Analyzing immune cell infiltration patterns associated with lactylation modifications in cardiac injury.

Strengths

• Identifies seven specific lactylation-related genes (G6pd, Tkt, Eif2s2, Pabpc1, Itgb2, Cenpf, Runx2) with biomarker potential.
• Includes validation from in vitro hypoxia-reoxygenation models for four key genes.
• Results are derived from analysis of gene expression datasets from the Gene Expression Omnibus database.

Limitations

• Dataset size is 1.3 KB, indicating a very limited scope, likely containing summary results rather than raw data.
• Row count and column-level documentation are unknown, limiting suitability assessment.
• Description metadata is limited; actual data structure and field semantics must be inferred after download.

Provenance

Source

figshare, author Jie Bai.

Collection Method

Analysis of MIRI gene expression datasets from the Gene Expression Omnibus using differential expression, coexpression network analysis, and machine learning.

Freshness

Last updated 2026-04-21 05:34:14; freshness should be verified.