Abstract
Aim and Objective: Integrating multi-omics data to identify driver genes and key biological functions for tumorigenesis remains a major challenge.
Method: A new computational pipeline was developed to identify the Driver Mutation-Differential Co-Expression (DM-DCE) modules based on dysfunctional networks across 11 TCGA cancers.
Results: Functional analyses provided insight into the properties of various cancers, and found common cellular signals / pathways of cancers. Furthermore, the corresponding network analysis identified conservations or interactions across different types of cancers, thus the crosstalk between the key signaling pathways, immunity and cancers was found. Clinical analysis also identified key prognostic / survival patterns.
Conclusion: Taken together, our study sheds light on both cancer-specific and cross-cancer characteristics systematically.
Keywords: Cancer, network analysis, cancer corresponding, Driver Mutation to Differential Co-expression, diagnosis, cellular signals.
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