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
Patent

Triple drug combination (metformin, simvastatin, digoxin) for targeted treatment of pancreatic cancer

TL;DR: A combination of three well-known and FDA approved compounds has been discovered to significantly suppress the proliferation of pancreatic cancer cells in clinically relevant models as mentioned in this paper, which is a breakthrough in the field of cancer treatment.
DissertationDOI

Identifying biological associations from high-throughput datasets

TL;DR: The combination of the approaches presented in this thesis may advance the current stages of tumor marker identification and identify with high confidence underlying biological associations in different types of large scale datasets.
Posted ContentDOI

KARL: Knowledge Augmented Rule Learning for Informed Biomarker Discovery

TL;DR: There is a critical need for flexible modeling methods that can handle data from diverse sources to facilitate discovery of robust biomarkers that underlie disease regulatory processes.
Posted ContentDOI

Weighted Gene Coexpression Network Analysis-Based Identification of Key Modules and Hub Genes that Regulate the Drought Stress Response in the Rice Drought-Sensitive Line PY6

TL;DR: It is speculated that drought-induced photosynthetic inhibition leads to H2O2 and MDA accumulation, which can then trigger the reprogramming of the rice transcriptome, including the hub genes involved in ROS scavenging, to prevent oxidative stress damage.
Journal ArticleDOI

Gene co-expression network based on part mutual information for gene-to-gene relationship and gene-cancer correlation analysis

TL;DR: Part mutual information (PMI) as mentioned in this paper was used to calculate gene co-expression networks of cancer mRNA transcriptome data and showed that the PMI-based networks with fewer edges could represent the correlation pattern and are robust across biological conditions.
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)