canEvolve

canEvolve enables integrative analysis of gene, microRNA (miRNA), and protein expression profiles, copy number alterations, and protein–protein interaction data from resources such as TCGA and the International Cancer Genome Consortium to support hypothesis generation in cancer genomics.

Key Features:

• Data Integration: canEvolve stores and integrates gene, microRNA (miRNA), and protein expression profiles, copy number alteration data, and protein–protein interaction information across multiple cancer types.
• Differential Expression and Copy Number Analysis: Performs primary analyses for differential gene and miRNA expression and detects gene copy number changes using SNP microarrays.
• Integrative Analysis: Supports integrative analyses linking gene expression, copy number alterations, and miRNA profiles, including gene set enrichment analysis.
• Network Analysis: Stores and enables analysis and visualization of gene co-expression data, inferred gene regulatory networks, and protein–protein interactions.
• Clinical Correlations: Provides correlations between gene expression and clinical outcomes, including univariate survival analysis.
• Dataset Coverage: Aggregates data from 90 cancer genomics studies encompassing over 10,000 patients.

Scientific Applications:

• Oncogenic Regulator Discovery: Identification of novel oncogenic regulators through integrative analysis of expression, copy number, and interaction data.
• Expression and Copy Number Exploration: Exploration of gene and miRNA expression changes alongside copy number variations.
• Network-based Mechanistic Studies: Network-based studies using gene co-expression and inferred regulatory networks to elucidate mechanisms of cancer progression.
• Clinical Correlation and Biomarker Analysis: Correlation analyses linking genomic alterations to clinical outcomes, including survival associations to inform personalized medicine approaches.

Methodology:

Aggregating data from diverse functional genomics platforms and public repositories; implementing analytical techniques for primary, integrative, and network analysis of oncogenomics data; and integrating multi-dimensional genomic datasets to support hypothesis generation.