Integrative Analysis of Dysfunctional Modules Driven by Genomic Alterations at System Level Across 11 Cancer Types.

Area of Science:

• Computational biology
• Cancer genomics
• Systems biology

Background:

• Identifying driver genes and biological functions in tumorigenesis is complex.
• Integrating multi-omics data presents significant challenges in cancer research.

Purpose of the Study:

• To develop a computational pipeline for identifying driver mutation-differential co-expression (DM-DCE) modules.
• To analyze dysfunctional networks across 11 cancers from The Cancer Genome Atlas (TCGA).
• To uncover cancer-specific and cross-cancer characteristics.

Main Methods:

• Development of a novel computational pipeline: Driver Mutation-Differential Co-Expression (DM-DCE).
• Application of the pipeline to multi-omics data from 11 TCGA cancer types.
• Functional, network, and clinical analyses of identified modules and networks.

Main Results:

• Identification of DM-DCE modules associated with dysfunctional networks in 11 cancers.
• Discovery of common cellular signals and pathways across various cancer types.
• Network analysis revealed conserved interactions, crosstalk between signaling pathways, immunity, and cancer, and identified key prognostic/survival patterns.

Conclusions:

• The study systematically elucidates both cancer-specific and cross-cancer characteristics.
• The developed pipeline provides a robust method for identifying driver mutations and functional modules.
• Findings offer insights into the complex interplay of signaling, immunity, and cancer progression.