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

Identification of Key Genes in Association with Progression and Prognosis in Cervical Squamous Cell Carcinoma.

TL;DR: Five genes of significant prognostic value-DSG2, ITM2A, CENPM, RIBC2, and MEIS2-were identified by prognostic signature analysis and used to construct a risk linear model and suggested DSG2 might be a key gene in CESC prognosis.
Journal ArticleDOI

Differences of immune disorders between Alzheimer's disease and breast cancer based on transcriptional regulation.

TL;DR: From the above findings, the opposing cellular behaviors demonstrate an invaluable opportunity to gain insights into the pathogenesis of these two types of diseases and to aid in developing new treatments.
Journal ArticleDOI

Molecular profiling of mucinous epithelial ovarian cancer by weighted gene co-expression network analysis

TL;DR: The transcriptome profiling by weighted gene co-expression network analysis (WGCNA) has been proposed as an effective method to improve the efficacy of early diagnosis of mucinous epithelial ovarian cancer (mEOC) and proposed an applicable framework of molecular characteristics for patients with mEOC.
Journal ArticleDOI

ITLNI identified by comprehensive bioinformatic analysis as a hub candidate biological target in human epithelial ovarian cancer.

TL;DR: It is speculated that ITLN1 was a tumor suppressor which could be used as a potential biomarker for treating EOC, Gene Expression Omnibus, prognosis and quantitative real-time PCR to validate hub gene expression in clinical tissue samples.
Journal ArticleDOI

High-efficient screening method for identification of key genes in breast cancer through microarray and bioinformatics

TL;DR: Key pathways and genes involved in breast cancer which are potential molecular targets for breast cancer treatment and diagnosis are identified and developed a new method for screening key genes with abnormal expression based on bioinformatics.
References
More filters
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.
Related Papers (5)