Cat-E

Cat-E integrates molecular datasets from OvirusTB, TCGA, DrugBANK, and PubChem to identify cancer-associated genes and potential drug targets through multi-omics analyses.

Key Features:

• Data Integration: Consolidates information on oncolytic viruses, cell lines, gene markers, and clinical studies from OvirusTB, TCGA, DrugBANK, and PubChem.
• Data Upload Capability: Accepts user-supplied datasets for integration with precompiled molecular and clinical data.
• Differential Gene Expression Analysis: Performs differential expression analysis to detect genes with altered expression between conditions or cohorts.
• Metabolic Pathway Exploration and Flux Analysis: Conducts metabolic pathway interrogation and flux analysis to characterize metabolic alterations.
• GO and KEGG Enrichment Analysis: Executes Gene Ontology (GO) and KEGG pathway enrichment analyses to identify overrepresented biological processes and pathways.
• Survival Analysis: Associates molecular features with clinical outcomes using survival analysis methods.
• Immune Signature Analysis: Profiles immune-related signatures to evaluate tumor immune states.
• Single Nucleotide Variation Analysis: Analyzes single nucleotide variations to characterize genomic alterations.
• Dynamic Gene Expression and Gene Regulatory Network Analysis: Evaluates temporal or condition-specific gene expression changes and alterations in gene regulatory networks.
• Protein Structure Prediction: Predicts protein structures to support evaluation of therapeutic targets.
• Implementation: Implemented as an R/Shiny web application for computational analyses and integration.

Scientific Applications:

• Target identification and drug linking: Identifies cancer-associated genes and potential drug targets and links them to compounds in DrugBANK and PubChem for drug repurposing or prioritization.
• Biomarker discovery: Discovers diagnostic and prognostic markers across cancer types, including lung adenocarcinoma (LUAD).
• Pathway and metabolic characterization: Characterizes altered pathways and metabolic fluxes using GO/KEGG enrichment and metabolic analyses.
• Clinical association and outcome analysis: Associates molecular features with clinical variables and survival outcomes using integrated clinical study data.
• Immune and mutation profiling: Profiles immune signatures and single nucleotide variations to inform tumor immunobiology and mutational landscapes.
• Structural support for therapeutic evaluation: Uses protein structure prediction to inform target validation and drug design considerations.

Methodology:

Cat-E integrates molecular datasets from OvirusTB, TCGA, DrugBANK, and PubChem and performs differential gene expression, metabolic pathway exploration and flux analysis, GO and KEGG enrichment, survival analysis, immune signature analysis, single nucleotide variation analysis, dynamic gene expression and gene regulatory network analysis, and protein structure prediction within an R/Shiny computational framework.