Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability

Jie Zhang, Kewei Lu, Yang Xiang, Muhtadi Islam, Shweta Kotian, Zeina Kais, Cindy Lee, Mansi Arora, Hui wen Liu, Jeffrey D. Parvin, Kun Huang

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

Gene co-expression network analysis is an effective method for predicting gene functions and disease biomarkers. However, few studies have systematically identified co-expressed genes involved in the molecular origin and development of various types of tumors. In this study, we used a network mining algorithm to identify tightly connected gene co-expression networks that are frequently present in microarray datasets from 33 types of cancer which were derived from 16 organs/tissues. We compared the results with networks found in multiple normal tissue types and discovered 18 tightly connected frequent networks in cancers, with highly enriched functions on cancer-related activities. Most networks identified also formed physically interacting networks. In contrast, only 6 networks were found in normal tissues, which were highly enriched for housekeeping functions. The largest cancer network contained many genes with genome stability maintenance functions. We tested 13 selected genes from this network for their involvement in genome maintenance using two cell-based assays. Among them, 10 were shown to be involved in either homology-directed DNA repair or centrosome duplication control including the well- known cancer marker MKI67. Our results suggest that the commonly recognized characteristics of cancers are supported by highly coordinated transcriptomic activities. This study also demonstrated that the co-expression network directed approach provides a powerful tool for understanding cancer physiology, predicting new gene functions, as well as providing new target candidates for cancer therapeutics.

Original languageEnglish (US)
Article numbere1002656
JournalPLoS Computational Biology
Volume8
Issue number8
DOIs
StatePublished - Aug 1 2012
Externally publishedYes

Fingerprint

Genomic Instability
Mining
cancer
Genome
genome
Genes
Cancer
Gene
Gene Expression
neoplasms
gene
Neoplasms
genes
Tissue
Maintenance
centrosomes
Housekeeping
network analysis
Centrosome
Directed Network

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modeling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability. / Zhang, Jie; Lu, Kewei; Xiang, Yang; Islam, Muhtadi; Kotian, Shweta; Kais, Zeina; Lee, Cindy; Arora, Mansi; Liu, Hui wen; Parvin, Jeffrey D.; Huang, Kun.

In: PLoS Computational Biology, Vol. 8, No. 8, e1002656, 01.08.2012.

Research output: Contribution to journalArticle

Zhang, J, Lu, K, Xiang, Y, Islam, M, Kotian, S, Kais, Z, Lee, C, Arora, M, Liu, HW, Parvin, JD & Huang, K 2012, 'Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability', PLoS Computational Biology, vol. 8, no. 8, e1002656. https://doi.org/10.1371/journal.pcbi.1002656
Zhang, Jie ; Lu, Kewei ; Xiang, Yang ; Islam, Muhtadi ; Kotian, Shweta ; Kais, Zeina ; Lee, Cindy ; Arora, Mansi ; Liu, Hui wen ; Parvin, Jeffrey D. ; Huang, Kun. / Weighted Frequent Gene Co-expression Network Mining to Identify Genes Involved in Genome Stability. In: PLoS Computational Biology. 2012 ; Vol. 8, No. 8.
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