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Article Open Access

Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis

  • Authors:
    • Lin Li
    • Huabao Liu
    • Xiaoyu Hu
    • Yi Huang
    • Yanan Wang
    • Yansha He
    • Qingsong Lei
  • View Affiliations / Copyright

    Affiliations: Department of Liver Disease, Chongqing Traditional Chinese Medicine Hospital, Chongqing 400021, P.R. China, Department of Infectious Disease, Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610072, P.R. China, Department of Infectious Disease, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R. China
    Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 7708-7720
    |
    Published online on: April 5, 2018
       https://doi.org/10.3892/mmr.2018.8852
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Abstract

Due to economic development and lifestyle changes, the incidence of non‑alcoholic fatty liver disease (NAFLD) has gradually increased in recent years. However, the pathogenesis of NAFLD is not yet fully understood. To identify candidate genes that contribute to the development and progression of NAFLD, two microarray datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified and functional enrichment analyses were performed. A protein‑protein interaction network was constructed and modules were extracted using the Search Tool for the Retrieval of Interacting Genes and Cytoscape. The enriched functions and pathways of the DEGs included ‘cellular macromolecule biosynthetic process’, ‘cellular response to chemical stimulus’, ‘extracellular matrix organization’, ‘metabolic pathways’, ‘insulin resistance’ and ‘forkhead box protein O1 signaling pathway’. The DEGs, including type‑1 angiotensin II receptor, formin‑binding protein 1‑like, RNA‑binding protein with serine‑rich domain 1, Ras‑related C3 botulinum toxin substrate 1 and polyubiquitin‑C, were identified using multiple bioinformatics methods and validated in vitro with reverse transcription‑quantitative polymerase chain reaction analysis. In conclusion, five hub genes were identified in the present study, and they may aid in understanding of the molecular mechanisms underlying the development and progression of NAFLD.
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Copy and paste a formatted citation
Spandidos Publications style
Li L, Liu H, Hu X, Huang Y, Wang Y, He Y and Lei Q: Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis. Mol Med Rep 17: 7708-7720, 2018.
APA
Li, L., Liu, H., Hu, X., Huang, Y., Wang, Y., He, Y., & Lei, Q. (2018). Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis. Molecular Medicine Reports, 17, 7708-7720. https://doi.org/10.3892/mmr.2018.8852
MLA
Li, L., Liu, H., Hu, X., Huang, Y., Wang, Y., He, Y., Lei, Q."Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis". Molecular Medicine Reports 17.6 (2018): 7708-7720.
Chicago
Li, L., Liu, H., Hu, X., Huang, Y., Wang, Y., He, Y., Lei, Q."Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis". Molecular Medicine Reports 17, no. 6 (2018): 7708-7720. https://doi.org/10.3892/mmr.2018.8852
Copy and paste a formatted citation
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Spandidos Publications style
Li L, Liu H, Hu X, Huang Y, Wang Y, He Y and Lei Q: Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis. Mol Med Rep 17: 7708-7720, 2018.
APA
Li, L., Liu, H., Hu, X., Huang, Y., Wang, Y., He, Y., & Lei, Q. (2018). Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis. Molecular Medicine Reports, 17, 7708-7720. https://doi.org/10.3892/mmr.2018.8852
MLA
Li, L., Liu, H., Hu, X., Huang, Y., Wang, Y., He, Y., Lei, Q."Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis". Molecular Medicine Reports 17.6 (2018): 7708-7720.
Chicago
Li, L., Liu, H., Hu, X., Huang, Y., Wang, Y., He, Y., Lei, Q."Identification of key genes in non‑alcoholic fatty liver disease progression based on bioinformatics analysis". Molecular Medicine Reports 17, no. 6 (2018): 7708-7720. https://doi.org/10.3892/mmr.2018.8852
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