Multi-omics Mendelian Randomization Data for Mitochondrial Genes in Endometriosis

A bioinformatics dataset from figshare, authored by Sha Wang and last updated in May 2026, investigates causal relationships between mitochondrial genes and endometriosis. It integrates data from multiple sources, including 1,133 mitochondrial-related genes from MitoCarta3.0 and GWAS data from FinnGen. The analysis identified five key feature genes and their associations with immune cells.

Use Cases

• Identify causal mitochondrial genes for endometriosis based on Mendelian randomization results.
• Validate feature gene expression using immunohistochemical data referenced in the study.
• Analyze correlations between mitochondrial genes and immune cell infiltration based on single-sample gene set enrichment analysis.
• Train machine learning models for endometriosis biomarker discovery using the LASSO, random forest, and Boruta algorithms mentioned.
• Perform functional enrichment analysis on gene sets using the GO/KEGG pathways referenced.

Strengths

• Integrates data from multiple established sources, including MitoCarta3.0, eQTLGen, DECODE, Brisbane, and FinnGen.
• Analysis identified 128 mitochondrial genes with significant causal relationships to endometriosis.
• Validation was performed on two independent gene expression datasets (GSE51981 and GSE7305).

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 small at 71.2 KB, indicating limited raw data scope.

Provenance

Source

figshare, author Sha Wang.

Collection Method

Multi-omics bioinformatics analysis combining eQTL, pQTL, mQTL, and GWAS data, followed by Mendelian randomization and machine learning.

Freshness

Last updated 2026-05-04 05:30:46; freshness should be verified.