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. 2018 Oct 4;8:14830. doi: 10.1038/s41598-018-33276-3

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© The Author(s) 2018

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Overall strategy. Co-expression networks were generated from RNA-seq data. Community structure detection was applied to partition the networks into modules (purple, green and maroon nodes). For each patient, genes with non-synonymous mutations were enumerated, then mapped to the network modules, and overrepresentation analysis was performed. Modules with (purple, green and maroon cells), or without (grey cells) significant enrichment for mutations were identified. Disease-specific survival analysis was then conducted.