LinkeDomics

LinkeDomics integrates and analyzes multi-omics cancer datasets from TCGA (32 cancer types) and CPTAC mass spectrometry proteomics to enable gene and protein expression profiling, correlation and survival analyses, and GO/KEGG pathway enrichment.

Key Features:

• Multi-Omics Integration: Integrates genomics, transcriptomics, and proteomics data to support cross-omic analyses.
• TCGA and CPTAC Data Access: Provides access to TCGA datasets covering 32 cancer types and CPTAC mass spectrometry proteomics for TCGA cancer types including breast, colorectal, and ovarian tumors.
• Gene and Protein Expression Analysis: Analyzes high-throughput sequencing data and proteomics measurements to evaluate gene and protein expression patterns.
• Correlation Testing: Performs correlation analyses to identify relationships between gene expressions and other molecular or clinical features.
• Survival Association Analysis: Associates molecular features with clinical parameters such as patient survival time.
• Functional Enrichment: Supports Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.
• Biomarker and Target Discovery: Enables identification of potential biomarkers and therapeutic targets from integrated multi-omics data.
• Experimental Validation Integration: Supports integration of findings with experimental validation methods such as quantitative real-time PCR (q-PCR).

Scientific Applications:

• Biomarker and Therapeutic Target Identification: Facilitates discovery of candidate biomarkers and therapeutic targets across multiple cancer types.
• MAGE-A Subtype Analysis in ESCC: Has been used to study expression and functional implications of Melanoma-associated antigen-A (MAGE-A) subtypes in esophageal squamous-cell carcinoma (ESCC) using TCGA-ESCA data and survival correlations.
• Immune Response and Pathway Investigation: Elucidates roles of genes and proteins in immune response regulation and cytokine-related pathways via pathway enrichment.
• Cross-Omic Mechanistic Insight: Enables holistic exploration of molecular alterations across genomics, transcriptomics, and proteomics to inform cancer biology.

Methodology:

Integration and analysis of TCGA multi-omics and CPTAC mass spectrometry proteomics data, analysis of high-throughput sequencing data for gene expression, correlation testing, GO and KEGG pathway enrichment, and association of molecular features with patient survival time.