Incorporating gene co-expression network in identification of cancer prognosis markers

Shuangge Ma, Mingyu Shi, Yang Li, Danhui Yi, Ben Chang Shia

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

Background: Extensive biomedical studies have shown that clinical and environmental risk factors may not have sufficient predictive power for cancer prognosis. The development of high-throughput profiling technologies makes it possible to survey the whole genome and search for genomic markers with predictive power. Many existing studies assume the interchangeability of gene effects and ignore the coordination among them.Results: We adopt the weighted co-expression network to describe the interplay among genes. Although there are several different ways of defining gene networks, the weighted co-expression network may be preferred because of its computational simplicity, satisfactory empirical performance, and because it does not demand additional biological experiments. For cancer prognosis studies with gene expression measurements, we propose a new marker selection method that can properly incorporate the network connectivity of genes. We analyze six prognosis studies on breast cancer and lymphoma. We find that the proposed approach can identify genes that are significantly different from those using alternatives. We search published literature and find that genes identified using the proposed approach are biologically meaningful. In addition, they have better prediction performance and reproducibility than genes identified using alternatives.Conclusions: The network contains important information on the functionality of genes. Incorporating the network structure can improve cancer marker identification.

Original languageEnglish
Article number271
JournalBMC Bioinformatics
Volume11
DOIs
Publication statusPublished - May 20 2010
Externally publishedYes

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Prognosis
Cancer
Genes
Gene
Gene Expression
Neoplasms
Gene Regulatory Networks
Gene Networks
Network Connectivity
Alternatives
Environmental Factors
Reproducibility
Performance Prediction
Risk Factors
Profiling
Breast Cancer
Lymphoma
Network Structure
High Throughput
Genomics

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Incorporating gene co-expression network in identification of cancer prognosis markers. / Ma, Shuangge; Shi, Mingyu; Li, Yang; Yi, Danhui; Shia, Ben Chang.

In: BMC Bioinformatics, Vol. 11, 271, 20.05.2010.

Research output: Contribution to journalArticle

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