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Co-Expression Network Analysis Reveals Key Genes Related to Ankylosing Spondylitis Arthritis Disease: Computational and Experimental Validation Publisher



Najafzadeh L1 ; Mahmoudi M2 ; Ebadi M1 ; Shasaltaneh MD3 ; Masoudinejad A4
Authors
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Authors Affiliations
  1. 1. Department of Biology, College of Science, Damghan Branch, Islamic Azad University, Damghan, Iran
  2. 2. Rheumatology Research Center, Tehran University of Medical Sciences, Tehran, Iran
  3. 3. Department of Biology, Faculty of Sciences, University of Zanjan, Zanjan, Iran
  4. 4. Laboratory of Systems Biology and Bioinformatics, Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran

Source: Iranian Journal of Biotechnology Published:2021


Abstract

Background: Ankylosing spondylitis (AS) is a type of arthritis which can cause inflammation in the vertebrae and joints between the spine and pelvis. However, our understanding of the exact genetic mechanisms of AS is still far from being clear. Objective: To study and find the mechanisms and possible biomarkers related to AS by surveying inter-gene correlations of networks. Materials and Methods: A weighted gene co-expression network was constructed among genes identified by microarray analysis, gene co-expression network analysis, and network clustering. Then receiver operating characteristic (ROC) curves were conducted to identify a significant module with the genes implicated in the AS pathogenesis. Real-time PCR was performed to validate the results of microarray analysis. Results: In the significant module obtained from the network analysis there were eight AS related genes (LSM3, MRPS11, NSMCE2, PSMA4, UBL5, RPL17, MRPL22 and RPS17) which have been reported in previous studies as hub genes. Further, in this module, eight significant enriched pathways were found with adjusted p-values < 0.001 consisting of oxidative phosphorylation, ribosome, nonalcoholic fatty liver disease, Alzheimer's, Huntington's, and Parkinson's diseases, spliceosome, and cardiac muscle contraction pathways which have been linked to AS. Furthermore, we identified nine AS related genes (UQCRB, UQCRH, UQCRHL, UQCRQ, COX7B, COX5B, COX6C, COX6A1 and COX7C) in these pathways which can play essential roles in controlling mitochondrial activity and pathogenesis of autoimmune diseases. Real-time PCR results showed that three genes including UQCRH, MRPS11, and NSMCE2 in AS patients were significantly differentially expressed compared with normal controls. Conclusions: The results of the present study may contribute to understanding of AS molecular pathogenesis, thereby aiding the early prognosis, diagnosis, and effective therapies of the disease. © 2021 The Author(s).
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