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

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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/).

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Weighted Correlation Gene Network Analysis Reveals New Potential Mechanisms and Biomarkers in Non-obstructive Azoospermia.

TL;DR: In this article, a weighted gene coexpression network analysis algorithm generated three related functional modules, which correlated closely with clinical parameters derived from histopathological subtypes of NOA, with the resulting data enabled the construction of a protein-protein interaction network of these three modules, with CDK1, CDC20, CCNB1,CCNB2 and MAD2L1 identified as hub genes.
Journal ArticleDOI

A transcriptional co-expression network-based approach to identify prognostic biomarkers in gastric carcinoma.

TL;DR: A transcriptional co-expression network-based approach was used to identify prognostic biomarkers in gastric carcinoma and may have potential for use in personalized therapies, however, large-scale randomized controlled clinical trials and replication experiments are needed before these key biomarkers can be applied clinically.
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Bioinformatics analysis of the prognostic value of Tripartite Motif 28 in breast cancer.

TL;DR: The results of the present study indicate that TRIM28 provides a survival advantage to patients with BC and is a novel prognostic biomarker, in addition to being a therapeutic target for the treatment of BC.
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Identifying Long Non-coding RNA of Prostate Cancer Associated With Radioresponse by Comprehensive Bioinformatics Analysis.

TL;DR: This study is the first to screen long non-coding RNAs (lncRNAs) associated with radioresponse in PCa by The Cancer Genome Atlas and identifies its potential target PCGs for further basic and clinical research.
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Machine learning identifies 10 feature miRNAs for lung squamous cell carcinoma

TL;DR: Ten feature miRNAs were identified which could be used as a feature group to separate the cancer tissues from the adjacent tissues ultimately, and cross-validation of the obtained data showed that it can achieve extremely high accuracy and recall rate.
References
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Journal ArticleDOI

Molecular portraits of human breast tumours

TL;DR: Variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals were characterized using complementary DNA microarrays representing 8,102 human genes, providing a distinctive molecular portrait of each tumour.
Journal ArticleDOI

WGCNA: an R package for weighted correlation network analysis.

TL;DR: The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software.
Journal ArticleDOI

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Journal ArticleDOI

Gene expression profiling predicts clinical outcome of breast cancer

TL;DR: DNA microarray analysis on primary breast tumours of 117 young patients is used and supervised classification is applied to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis, providing a strategy to select patients who would benefit from adjuvant therapy.
Journal ArticleDOI

Comprehensive molecular portraits of human breast tumours

Daniel C. Koboldt, +355 more
- 04 Oct 2012 - 
TL;DR: The ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.
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