Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis
Open Access
- 5 June 2013
- journal article
- research article
- Published by Oxford University Press (OUP) in Carcinogenesis: Integrative Cancer Research
- Vol. 34 (10), 2300-2308
- https://doi.org/10.1093/carcin/bgt208
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/ ).Keywords
This publication has 55 references indexed in Scilit:
- BreastMark: An Integrated Approach to Mining Publicly Available Transcriptomic Datasets Relating to Breast Cancer OutcomeBreast Cancer Research, 2013
- Interactions between immunity, proliferation and molecular subtype in breast cancer prognosisGenome Biology, 2013
- Comprehensive molecular portraits of human breast tumoursNature, 2012
- The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroupsNature, 2012
- The Pan-ErbB Negative Regulator Lrig1 Is an Intestinal Stem Cell Marker that Functions as a Tumor SuppressorCell, 2012
- An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patientsBreast Cancer Research and Treatment, 2009
- Genes that mediate breast cancer metastasis to the brainNature, 2009
- Functional organization of the transcriptome in human brainNature Neuroscience, 2008
- A Multigene Assay to Predict Recurrence of Tamoxifen-Treated, Node-Negative Breast CancerThe New England Journal of Medicine, 2004
- Gene expression profiling predicts clinical outcome of breast cancerNature, 2002