Network based approach to identify interactions between Type 2 diabetes and cancer comorbidities

Area of Science:

• Genomics and Bioinformatics
• Metabolic Diseases
• Oncology

Background:

• Type 2 diabetes (T2D) is a chronic metabolic disease linked to increased cancer risk.
• Comorbidities like T2D and cancer complicate treatment and may influence each other.
• The molecular mechanisms underlying the T2D-cancer relationship are not fully understood.

Purpose of the Study:

• To investigate potential comorbidity relationships between T2D and various cancers at a molecular level.
• To identify common genes, pathways, and Gene Ontology (GO) terms shared between T2D and cancers using bioinformatics.
• To analyze transcriptomic data for uncovering interrelated molecular processes.

Main Methods:

• Utilized bioinformatics to analyze publicly available transcriptomic datasets for T2D and cancers.
• Employed semantic similarity and gene set enrichment analysis.
• Integrated Gene Ontology (GO), gene expression, and biological process data.

Main Results:

• Identified 13 frequently occurring common genes between T2D and cancers.
• Found 4 common GO terms (GO:0000003, GO:0000122, GO:0000165, GO:0000278).
• Highlighted significant associations between T2D and Liver Cancer, Breast Cancer, Colorectal Cancer, and Bladder Cancer.
• Identified common hub proteins (e.g., KIF4A, NUSAP1, CENPF) and 8 key signaling pathways (e.g., AGE-RAGE, TNF, MAPK).

Conclusions:

• The study provides a system for investigating T2D-cancer comorbidity at the molecular level.
• Revealed shared molecular pathways and genes contributing to the association between T2D and specific cancers.
• Enhanced understanding of critical disease pathways linking T2D and cancer.