E.PAGE

E.PAGE predicts associations between environmental factors and gene expression to identify biological pathways and processes dysregulated by exposures such as cigarette smoking, diet, infections, and toxic chemicals.

Key Features:

• Comprehensive Database: A curated database linking 25,789 genes to four environmental factors (cigarette smoking, diet, infections, toxic chemicals) compiled from an initial screen of over 90,018 articles and 237 selected datasets.
• Gene Module Annotation: Contains 522 manually annotated gene modules that characterize gene–environment interaction patterns.
• Statistical Analysis Package: Provides statistical testing of differentially expressed genes across conditions including type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, cobalt exposure, and smoking to detect enriched processes.
• Pathway Enrichment: Performs pathway and process enrichment to identify molecular pathways significantly influenced by environmental exposures.

Scientific Applications:

• Gene–environment interaction studies: Supports analyses of how exposures such as cigarette smoking, diet, infections, and toxic chemicals alter gene expression and downstream biological processes.
• Disease-focused differential expression analysis: Enables enrichment analysis of DE gene lists from conditions including type 1 diabetes, rheumatoid arthritis, small cell lung cancer, COVID-19, and exposures like cobalt and smoking.
• Mechanistic pathway identification: Facilitates identification of molecular pathways and dysregulated processes linking environmental exposures to cancer, autoimmune, and infectious diseases.

Methodology:

Data retrieval from GEO and the Molecular Signature Database was followed by title and abstract screening against predefined criteria to select 237 datasets from over 90,018 articles, with subsequent database curation, validation, and manual annotation of 522 gene modules.