Gut Microbiota and Septic Cardiomyopathy Biomarkers from Transcriptome and MR Analysis

STAT3 and SLC5A1 were identified as candidate biomarkers linking gut microbiota to septic cardiomyopathy. The dataset contains results from Mendelian randomization analysis of 5 gut microbiota genera, 22 metabolites, and 461 targets, integrated with 166 differentially expressed genes. This 54.5 KB Excel file was authored by Yuxia Tao and last updated in May 2026.

Use Cases

• Validating the causal relationship between gut microbiota and septic cardiomyopathy based on Mendelian randomization results.
• Investigating the role of specific metabolites like propylene glycol in disease pathology based on the described binding activity.
• Analyzing immune cell involvement, such as MDSCs and activated dendritic cells, in septic cardiomyopathy with the identified biomarkers.
• Exploring regulatory networks involving transcription factors (e.g., FOXC1) and microRNAs (e.g., miR-3120-3p) linked to the biomarkers STAT3 and SLC5A1.

Strengths

• Results are validated with clinical samples measuring IL-6, cTnI, and propylene glycol concentrations.
• Analysis integrates multiple methodologies: Mendelian randomization, differential expression, machine learning, and molecular docking.
• The dataset is openly shared under a CC-BY-4.0 license.

Limitations

• The dataset is very small at 54.5 KB, indicating limited scope or summary-level results.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale modeling.

Provenance

Source

figshare

Collection Method

Integrated analysis of public GWAS data, differential expression analysis, and machine learning algorithms.

Freshness

Last updated 2026-05-04 09:32:37; freshness should be verified.