scispace - formally typeset
Open AccessJournal ArticleDOI

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

Reads0
Chats0
TLDR
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 and a cluster of genes was found to correlate with prognosis exclusively for basal-like breast cancer.
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/).

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Prognostic Genes of Breast Cancer Identified by Gene Co-expression Network Analysis.

TL;DR: Wang et al. as discussed by the authors used Weighted gene co-expression network analysis (WGCNA) to construct free-scale gene coexpression networks to explore the associations between gene sets and clinical features, and identify candidate biomarkers.
Journal ArticleDOI

BreastMark : An Integrated Approach to Mining Publicly Available Transcriptomic Datasets Relating to Breast Cancer Outcome

TL;DR: BreastMark is a powerful tool for examining putative gene/miRNA prognostic markers in breast cancer, and can act as a powerful reductionist approach to these more complex gene signatures, eliminating superfluous genes, potentially reducing the cost and complexity of these multi-index assays.
References
More filters
Journal ArticleDOI

A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules

TL;DR: By assembling these links into a gene-coexpression network, this work found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
Journal ArticleDOI

Genes that mediate breast cancer metastasis to the brain

TL;DR: It is shown that breast cancer metastasis to the brain involves mediators of extravasation through non-fenestrated capillaries, complemented by specific enhancers of blood–brain barrier crossing and brain colonization.
Journal ArticleDOI

Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2009

TL;DR: The 11th St Gallen expert consensus meeting on the primary treatment of early breast cancer in March 2009 maintained an emphasis on targeting adjuvant systemic therapies according to subgroups defined by predictive markers, acknowledging the role of risk factors with the caveat that risk per se is not a target.
Journal ArticleDOI

An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival

TL;DR: The p53 signature identified a subset of aggressive tumors absent of sequence mutations in p53 yet exhibiting expression characteristics consistent with p53 deficiency because of attenuated p53 transcript levels, showing the primary importance of p53 functional status in predicting clinical breast cancer behavior.
Related Papers (5)