Multi-omics Analysis of Abdominal Aortic Aneurysm Biomarkers

Supplementary data from an integrated multi-omics study identifies candidate biomarkers for abdominal aortic aneurysm (AAA). The analysis integrates genetic, transcriptomic, and proteomic evidence from public microarray datasets using Mendelian randomization and machine learning. Four genes—PLAU, CD58, PCYOX1, and THBS4—were prioritized, with PLAU showing strong colocalization and discriminatory accuracy.

Use Cases

• Prioritizing candidate genes for AAA based on integrated eQTL and pQTL Mendelian randomization evidence.
• Training machine learning models to classify AAA using differentially expressed gene signatures.
• Validating biomarker discriminatory performance via receiver operating characteristic curve analysis.
• Investigating shared genetic architecture for AAA susceptibility using Bayesian colocalization methods.
• Conducting gene set enrichment and immune-infiltration profiling from transcriptomic data.

Strengths

• Analysis integrates multiple public datasets (GSE47472, GSE57691, GSE7084) for validation.
• Methodology combines differential expression (551 genes), Mendelian randomization (267 eQTL, 129 pQTL genes), and multiple machine learning approaches.
• Key findings, such as the AUC for PLAU (0.854 and 0.944), are supported by experimental validation in a murine model.

Limitations

• Column names and row counts are not provided, limiting understanding of the data's structure.
• The dataset consists of supplementary document files (DOCX, XLSX), suggesting the primary analysis results are embedded within reports rather than raw structured data tables.
• The last updated date (2026-05-29) appears to be in the future relative to the current date, indicating a potential metadata error.

Provenance

Source

Author: Ming Xie. Data derived from public microarray datasets GSE47472, GSE57691, and GSE7084.

Collection Method

Integrated analysis of transcriptomic data following batch correction, combined with eQTL/pQTL summary statistics via two-sample Mendelian randomization and machine learning refinement.

Freshness

Last updated: 2026-05-29