EBV-GCR

EBV-GCR integrates multi-omic and single-cell datasets to enable analysis of Epstein-Barr virus-associated gastric adenocarcinoma (EBVaGC) biology and direct comparisons with EBV-negative gastric cancers (EBVnGCs).

Key Features:

• Datasets (TCGA and single-cell RNA-sequencing): Integrates The Cancer Genome Atlas (TCGA) transcriptomic, genomic, and epigenomic data together with emerging single-cell RNA-sequencing data specific to EBVaGCs.
• Transcriptomic Analysis: Analyzes cellular gene expression changes associated with EBV infection and EBVaGCs.
• Genomic and Epigenomic Exploration: Examines genomic alterations and differential gene methylation patterns linked to EBVaGCs.
• Single-Cell RNA Sequencing: Supports single-cell RNA-sequencing analyses to resolve intratumoral cellular heterogeneity in EBVaGC samples.
• Clinical Correlations: Enables association analyses between EBV-related molecular changes and patient outcomes for prognostic and therapeutic marker identification.
• Immune Landscape Analysis: Characterizes immune landscape features to investigate how EBV influences tumor immunology.

Scientific Applications:

• Comparative molecular profiling: Compare transcriptomic, genomic, and epigenomic differences between EBVaGCs and EBVnGCs.
• Mechanistic studies of EBV oncogenesis: Investigate EBV-driven host-cell gene reprogramming and pathways contributing to gastric carcinogenesis.
• Epigenetic characterization: Identify differential methylation patterns associated with EBV infection in gastric tumors.
• Prognostic and therapeutic marker discovery: Correlate molecular alterations with clinical outcomes to nominate biomarkers and targets.
• Immune microenvironment analysis: Characterize tumor immune composition and EBV-associated immunological features relevant to immunotherapy research.

Methodology:

Integration of TCGA transcriptomic, genomic, and epigenomic datasets with single-cell RNA-sequencing data, coupled with analyses of gene expression, genomic alterations, differential gene methylation, immune landscape features, and clinical association analyses.