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Comb Chem High Throughput Screen. 2018;21(10):771-783. doi: 10.2174/1386207322666190122110726.

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

Wang Y1,2,3, Liu Z2,3, Lian B3, Liu L2,3, Xie L3.

Author information

1
Department of Biomedical Engineering, School of Fundamental Sciences, China Medical University, Shenyang 110012, Liaoning Province, China.
2
Shanghai Public Health Clinical Center and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
3
Shanghai Center for Bioinformation Technology, Shanghai Academy of Science and Technology, Shanghai 201203, China.

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; Driver Mutation to Differential Co-expression; cancer corresponding; cellular signals; diagnosis; network analysis.

[Indexed for MEDLINE]

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