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Article Open Access

Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes

  • Authors:
    • Yinling Feng
    • Xuefeng Wang
  • View Affiliations / Copyright

    Affiliations: Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, Sichuan 400016, P.R. China
    Copyright: © Feng et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 1252-1262
    |
    Published online on: January 16, 2017
       https://doi.org/10.3892/mmr.2017.6124
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Abstract

In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co‑expression networks and clinical information was adopted, using weighted gene co‑expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co‑pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution‑based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD‑associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.
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Copy and paste a formatted citation
Spandidos Publications style
Feng Y and Wang X: Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes. Mol Med Rep 15: 1252-1262, 2017.
APA
Feng, Y., & Wang, X. (2017). Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes. Molecular Medicine Reports, 15, 1252-1262. https://doi.org/10.3892/mmr.2017.6124
MLA
Feng, Y., Wang, X."Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes". Molecular Medicine Reports 15.3 (2017): 1252-1262.
Chicago
Feng, Y., Wang, X."Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes". Molecular Medicine Reports 15, no. 3 (2017): 1252-1262. https://doi.org/10.3892/mmr.2017.6124
Copy and paste a formatted citation
x
Spandidos Publications style
Feng Y and Wang X: Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes. Mol Med Rep 15: 1252-1262, 2017.
APA
Feng, Y., & Wang, X. (2017). Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes. Molecular Medicine Reports, 15, 1252-1262. https://doi.org/10.3892/mmr.2017.6124
MLA
Feng, Y., Wang, X."Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes". Molecular Medicine Reports 15.3 (2017): 1252-1262.
Chicago
Feng, Y., Wang, X."Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes". Molecular Medicine Reports 15, no. 3 (2017): 1252-1262. https://doi.org/10.3892/mmr.2017.6124
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