Naiqiang Zhu1, Peng Zhang2, Lilong Du3, Jingyi Hou4, Baoshan Xu5. 1. Graduate School of Tianjin Medical University, Tianjin 300070, China; Second Department of Spinal Surgery, The Affiliated Hospital of Chengde Medical College, Chengde 067000, China. 2. Graduate School of Tianjin Medical University, Tianjin 300070, China. 3. Department of Minimally Invasive Spine Surgery, Tianjin Hospital, Tianjin 300070, China. 4. Hebei Key Laboratory of Study and Exploitation of Chinese Medicine, Chengde Medical College, Chengde 067000, China. 5. Department of Minimally Invasive Spine Surgery, Tianjin Hospital, Tianjin 300070, China. Electronic address: xubaoshan99@126.com.
Abstract
BACKGROUND: The underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA). MATERIAL AND METHODS: We obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the "limma" package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples. RESULTS: The preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples. CONCLUSIONS: We identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.
BACKGROUND: The underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA). MATERIAL AND METHODS: We obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the "limma" package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples. RESULTS: The preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples. CONCLUSIONS: We identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.