Open Access

Co‑expression network‑based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm

  • Authors:
    • Hong Yue
    • Bo Yang
    • Fang Yang
    • Xiao‑Li Hu
    • Fan‑Bin Kong
  • View Affiliations

  • Published online on: March 3, 2016     https://doi.org/10.3892/etm.2016.3131
  • Pages: 1707-1715
  • Copyright: © Yue et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Recent progress in bioinformatics has facilitated the clarification of biological processes associated with complex diseases. Numerous methods of co‑expression analysis have been proposed for use in the study of pairwise relationships among genes. In the present study, a combined network based on gene pairs was constructed following the conversion and combination of gene pair score values using a novel algorithm across multiple approaches. Three hippocampal expression profiles of patients with Alzheimer's disease (AD) and normal controls were extracted from the ArrayExpress database, and a total of 144 differentially expressed (DE) genes across multiple studies were identified by a rank product (RP) method. Five groups of co‑expression gene pairs and five networks were identified and constructed using four existing methods [weighted gene co‑expression network analysis (WGCNA), empirical Bayesian (EB), differentially co‑expressed genes and links (DCGL), search tool for the retrieval of interacting genes/proteins database (STRING)] and a novel rank‑based algorithm with combined score, respectively. Topological analysis indicated that the co‑expression network constructed by the WGCNA method had the tendency to exhibit small‑world characteristics, and the combined co‑expression network was confirmed to be a scale‑free network. Functional analysis of the co‑expression gene pairs was conducted by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The co‑expression gene pairs were mostly enriched in five pathways, namely proteasome, oxidative phosphorylation, Parkinson's disease, Huntington's disease and AD. This study provides a new perspective to co‑expression analysis. Since different methods of analysis often present varying abilities, the novel combination algorithm may provide a more credible and robust outcome, and could be used to complement to traditional co‑expression analysis.

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May-2016
Volume 11 Issue 5

Print ISSN: 1792-0981
Online ISSN:1792-1015

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Copy and paste a formatted citation
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Spandidos Publications style
Yue H, Yang B, Yang F, Hu XL and Kong FB: Co‑expression network‑based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm. Exp Ther Med 11: 1707-1715, 2016
APA
Yue, H., Yang, B., Yang, F., Hu, X., & Kong, F. (2016). Co‑expression network‑based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm. Experimental and Therapeutic Medicine, 11, 1707-1715. https://doi.org/10.3892/etm.2016.3131
MLA
Yue, H., Yang, B., Yang, F., Hu, X., Kong, F."Co‑expression network‑based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm". Experimental and Therapeutic Medicine 11.5 (2016): 1707-1715.
Chicago
Yue, H., Yang, B., Yang, F., Hu, X., Kong, F."Co‑expression network‑based analysis of hippocampal expression data associated with Alzheimer's disease using a novel algorithm". Experimental and Therapeutic Medicine 11, no. 5 (2016): 1707-1715. https://doi.org/10.3892/etm.2016.3131