Cancer microarray data weighted gene co-expression network analysis identifies a gene module and hub genes shared across nine types of solid cancer

CellR4 2017; 5 (5): e2439

  Topic: Cancer     Category:

Abstract

Objective: Microarray and next-generation sequencing techniques have revealed a series of somatic mutations and differentially expressed genes associated with multiple cancers. The objective of this research was to identify networks of overexpressed genes for nine common solid cancers using a novel combination of systematic genomic analysis and published cancer microarray databases.
Materials and Methods: A total of twelve gene expression microarray datasets containing nine types of common solid cancers were obtained from the Gene Expression Omnibus, which included 104 breast, 117 brain, 32 colon, 108 gastric, 95 liver, 60 lung, 72 pancreatic, 72 renal, 26 prostate, and a total of 330 matching non-cancerous control tissue samples. Differentially expressed genes (DEG) were analyzed between each cancer sample and its matching controls. Weighted gene co-expression network analysis (WGCNA) was used to construct pairwise correlated co-expressed gene networks and to detect overexpressed gene modules for each cancer.
Results: WGCNA of a total of 1,016 cancer genes identified specific hub genes and gene modules for each type of cancer. Gene co-expression networks were constructed. Overexpressed module genes were compared to their matching non-tumor controls. This revealed significantly overexpressed proliferative cell-cycle related gene modules. These gene modules included BIRC5, TPX2, CDK1, and MKI67, which have previously been shown to be associated with cancers.
Conclusions: Genomic analysis revealed overexpressed gene modules in nine different types of solid cancers and a shared network of overexpressed genes common to all types. These shared, overexpressed genes involve cell and proliferation, supporting the idea that different cancers have a shared core molecular pathway. Elucidation of various networks of gene modules among different types of cancers may provide better understanding of molecular mechanisms for different cancers.

To cite this article

Cancer microarray data weighted gene co-expression network analysis identifies a gene module and hub genes shared across nine types of solid cancer

CellR4 2017; 5 (5): e2439

Publication History

Submission date: 14 Sep 2017

Revised on: 19 Sep 2017

Accepted on: 27 Sep 2017

Published online: 02 Oct 2017