Coronary Heart Disease Molecular Typing via Gene Co-Expression Networks

2021 differentially expressed genes were screened from GEO, NCBI-gene, and OMIM databases to study Coronary Heart Disease (CHD). Researchers from Sun Yat-sen University performed weighted gene co-expression network analysis (WGCNA), identifying 6 modules and building a multi-factor regulatory network. The work defined five core genes (FTH1, HCAR3, RGS2, S100A9, TYROBP) and classified CHD samples into 5 clusters, suggesting potential diagnostic and therapeutic targets.

Use Cases

• Identifying molecular subtypes of Coronary Heart Disease based on the 5 defined gene clusters.
• Validating potential diagnostic biomarkers like FTH1, S100A9, and TYROBP using gene expression data.
• Building multi-factor regulatory networks for CHD by integrating differentially expressed genes and miRNAs.
• Performing weighted gene co-expression network analysis (WGCNA) on candidate gene sets.

Strengths

• Analysis is based on 2021 differentially expressed genes sourced from established databases (GEO, NCBI-gene, OMIM).
• Identified 6 distinct WGCNA modules and 5 molecular clusters for CHD classification.
• Validated findings at the mRNA level in blood samples from CHD patients.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

GEO, NCBI-gene, and OMIM databases.

Collection Method

Computational screening of differentially expressed genes, followed by weighted gene co-expression network analysis (WGCNA).