Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis

Abstract

Weighted gene coexpression network analysis (WGCNA) is a powerful 'guilt-by-association'-based method to extract coexpressed groups of genes from large heterogeneous messenger RNA expression data sets. We have utilized WGCNA to identify 11 coregulated gene clusters across 2342 breast cancer samples from 13 microarray-based gene expression studies. A number of these transcriptional modules were found to be correlated to clinicopathological variables (e.g. tumor grade), survival endpoints for breast cancer as a whole (disease-free survival, distant disease-free survival and overall survival) and also its molecular subtypes (luminal A, luminal B, HER2+ and basal-like). Examples of findings arising from this work include the identification of a cluster of proliferation-related genes that when upregulated correlated to increased tumor grade and were associated with poor survival in general. The prognostic potential of novel genes, for example, ubiquitin-conjugating enzyme E2S (UBE2S) within this group was confirmed in an independent data set. In addition, gene clusters were also associated with survival for breast cancer molecular subtypes including a cluster of genes that was found to correlate with prognosis exclusively for basal-like breast cancer. The upregulation of several single genes within this coexpression cluster, for example, the potassium channel, subfamily K, member 5 (KCNK5) was associated with poor outcome for the basal-like molecular subtype. We have developed an online database to allow user-friendly access to the coexpression patterns and the survival analysis outputs uncovered in this study (available at http://glados.ucd.ie/Coexpression/).

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Network-based approach to identify prognostic biomarkers for estrogen receptor–positive breast cancer treatment with tamoxifen

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An Integrative Genomic Study Implicates the Postsynaptic Density in the Pathogenesis of Bipolar Disorder

“…We used weighted gene correlation network analysis (WGCNA) , a widely used method that finds modules of highly correlated genes, relates these modules to one another, and tests the influence of sample phenotypes on gene expression correlations. WGCNA has been widely used to identify co-expressed gene networks in various human brain regions (Oldham et al, 2008), animals (Fuller et al, 2007;Langfelder et al, 2012), and in human phenotypes, including schizophrenia (Torkamani et al, 2010), autism (Voineagu et al, 2011), cancer (Clarke et al, 2013), aggressive behavior (Malki et al, 2014), BD (Chen et al, 2013a), and psoriasis (Li et al, 2014). However, aside from one study of a few gene networks (Hong et al, 2013), WGCNA has not yet been applied to the complete brain transcriptome in BD as revealed by RNA-seq.…”