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

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

Document Type: Research Article

Publication date: December 1, 2018

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  • Combinatorial Chemistry & High Throughput Screening publishes full length original research articles and reviews describing various topics in combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) and/or high throughput screening (e.g. developmental, practical or theoretical). Ancillary subjects of key importance, such as robotics and informatics, will also be covered by the journal. In these respective subject areas, Combinatorial Chemistry & High Throughput Screening is intended to function as the most comprehensive and up-to-date medium available. The journal should be of value to individuals engaged in the process of drug discoveryand development, in the settings of industry, academia or government.
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