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Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types

Author

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  • Yang Yang

    (The University of Texas Health Science Center at Houston, School of Public Health
    The University of Texas MD Anderson Cancer Center)

  • Leng Han

    (The University of Texas MD Anderson Cancer Center)

  • Yuan Yuan

    (The University of Texas MD Anderson Cancer Center
    Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine)

  • Jun Li

    (The University of Texas MD Anderson Cancer Center)

  • Nainan Hei

    (The University of Texas Health Science Center at Houston, School of Public Health)

  • Han Liang

    (The University of Texas MD Anderson Cancer Center
    Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine)

Abstract

Prognostic genes are key molecules informative for cancer prognosis and treatment. Previous studies have focused on the properties of individual prognostic genes, but have lacked a global view of their system-level properties. Here we examined their properties in gene co-expression networks for four cancer types using data from ‘The Cancer Genome Atlas’. We found that prognostic mRNA genes tend not to be hub genes (genes with an extremely high connectivity), and this pattern is unique to the corresponding cancer-type-specific network. In contrast, the prognostic genes are enriched in modules (a group of highly interconnected genes), especially in module genes conserved across different cancer co-expression networks. The target genes of prognostic miRNA genes show similar patterns. We identified the modules enriched in various prognostic genes, some of which show cross-tumour conservation. Given the cancer types surveyed, our study presents a view of emergent properties of prognostic genes.

Suggested Citation

  • Yang Yang & Leng Han & Yuan Yuan & Jun Li & Nainan Hei & Han Liang, 2014. "Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types," Nature Communications, Nature, vol. 5(1), pages 1-9, May.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms4231
    DOI: 10.1038/ncomms4231
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    Cited by:

    1. Dongling Yu & Zuguo Yu, 2022. "HWVoteRank: A Network-Based Voting Approach for Identifying Coding and Non-Coding Cancer Drivers," Mathematics, MDPI, vol. 10(5), pages 1-13, March.
    2. Chihyun Park & JungRim Kim & Jeongwoo Kim & Sanghyun Park, 2018. "Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-15, July.
    3. Benafsh Husain & F Alex Feltus, 2019. "EdgeScaping: Mapping the spatial distribution of pairwise gene expression intensities," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-15, August.

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