BIC

BIC characterizes the transcriptional landscape of bacterial communities within tumor microenvironments by analyzing miRNA sequencing data from The Cancer Genome Atlas (TCGA) to identify bacteria present at tumor sites.

Key Features:

• Data Source and Methodology: Analyzes miRNA sequencing data from The Cancer Genome Atlas (TCGA) across 32 cancer types and aligns unmapped human reads against bacterial reference sequences to infer bacterial presence and abundance.
• Bacterial Abundance and Diversity: Provides relative abundance estimates and measures of bacterial diversity within different cancer types.
• Clinical Associations: Reports associations between specific bacterial communities and clinical outcomes that may serve as diagnostic or prognostic biomarkers.
• Co-expression Networks: Constructs co-expression networks linking bacterial genes to human genes to explore interactive roles in the tumor microenvironment.
• Biological Functions: Annotates biological functions associated with identified bacteria to interpret their potential roles in cancer biology.

Scientific Applications:

• Non-gut tumor microbiome characterization: Facilitates analysis of bacteria present at tumor sites across 32 cancer types to complement gut-focused microbiome resources.
• Microbial oncology research: Enables investigation of bacterial community composition and transcriptional activity in relation to cancer biology.
• Biomarker discovery: Supports identification of bacterial signatures associated with clinical outcomes for potential diagnostic or prognostic use.
• Host–microbe interaction analysis: Integrates bacterial and human gene expression to uncover co-expression relationships and potential interaction mechanisms in the tumor microenvironment.

Methodology:

Processes TCGA miRNA-seq data from 32 cancer types, aligns unmapped human reads to bacterial reference sequences to infer bacterial abundance and diversity, and constructs co-expression networks linking bacterial genes to human genes.