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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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A four-long non-coding RNA signature in predicting breast cancer survival

TL;DR: Findings indicate that lncRNAs may be implicated in breast cancer pathogenesis and may have clinical implications in the selection of high-risk patients for adjuvant therapy.
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LncRNA co-expression network analysis reveals novel biomarkers for pancreatic cancer.

TL;DR: This study applied a commonly used systems biology approach, the weighted gene co-expression network analysis (WGCNA), on lncRNAs to identify early PDAC biomarkers and new therapeutic targets and identified A2M-AS1, LINC01133, Linc00205 and TSPOAP1-as1 as prognostic biomarkers for PDAC.
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Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis.

TL;DR: All real hub genes were overexpressed in pancreatic carcinoma compared with normal tissues on transcriptional and translational level and both functional enrichment analysis and gene set enrichment was performed and both showed the cell cycle played a vital role in PDAC.
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Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

TL;DR: This is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcines model for obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis.
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Individual-level analysis of differential expression of genes and pathways for personalized medicine

TL;DR: A method to detect DE genes in individual disease samples by using the disrupted ordering inindividual disease samples showed excellent performance and was found to be insensitive to experimental batch effects and data normalization.
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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