Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis
Colin Clarke,Stephen F. Madden,Padraig Doolan,Sinead T Aherne,Helena Joyce,Lorraine O'Driscoll,William M. Gallagher,Bryan T. Hennessy,Michael Moriarty,John Crown,Susan Kennedy,Martin Clynes +11 more
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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
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Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation
Erik C. B. Johnson,Eric B. Dammer,Duc M. Duong,Lingyan Ping,Maotian Zhou,Luming Yin,Lenora Higginbotham,Andrew Guajardo,Bartholomew White,Juan C. Troncoso,Madhav Thambisetty,Thomas J. Montine,Edward B. Lee,John Q. Trojanowski,Thomas G. Beach,Eric M. Reiman,Vahram Haroutunian,Vahram Haroutunian,Minghui Wang,Eric E. Schadt,Bin Zhang,Dennis W. Dickson,Nilufer Ertekin-Taner,Todd E. Golde,Vladislav A. Petyuk,Philip L. De Jager,David A. Bennett,Thomas S. Wingo,Srikant Rangaraju,Ihab Hajjar,Joshua M. Shulman,James J. Lah,Allan I. Levey,Nicholas T. Seyfried +33 more
TL;DR: Large-scale, comprehensive proteomic profiling of Alzheimer’s disease brain and cerebrospinal fluid reveals disease-associated protein coexpression modules and highlights the importance of glia and energy metabolism in disease pathogenesis.
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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.
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A self-enforcing CD44s/ZEB1 feedback loop maintains EMT and stemness properties in cancer cells.
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BreastMark : An Integrated Approach to Mining Publicly Available Transcriptomic Datasets Relating to Breast Cancer Outcome
Stephen F. Madden,Colin Clarke,Patricia Gaule,Sinead T Aherne,Norma O'Donovan,Martin Clynes,John Crown,William M. Gallagher +7 more
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
Paula D. Bos,Xiang Zhang,Cristina Nadal,Weiping Shu,Roger R. Gomis,Don X. Nguyen,Andy J. Minn,Marc J. van de Vijver,William L. Gerald,John A. Foekens,Joan Massagué,Joan Massagué +11 more
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
The molecular portraits of breast tumors are conserved across microarray platforms
Zhiyuan Hu,Cheng Fan,Daniel S. Oh,James Stephen Marron,Xiaping He,Bahjat F. Qaqish,Chad A. Livasy,Lisa A. Carey,Evangeline R Reynolds,Lynn G. Dressler,Andrew B. Nobel,Joel S. Parker,Matthew G. Ewend,Lynda R. Sawyer,Junyuan Wu,Yudong Liu,Rita Nanda,Maria Tretiakova,Alejandra Ruiz Orrico,Donna Dreher,Juan P. Palazzo,Laurent Perreard,Edward W. Nelson,Mary C. Mone,Heidi Theil Hansen,Michael Mullins,John Quackenbush,Matthew J. Ellis,Olufunmilayo I. Olopade,Philip S. Bernard,Charles M. Perou +30 more
TL;DR: This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation.
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
Lance D. Miller,Johanna Smeds,Joshy George,Vinsensius B. Vega,Liza A. Vergara,Alexander Ploner,Yudi Pawitan,Per Hall,Sigrid Klaar,Edison T. Liu,Jonas Bergh +10 more
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.
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