Yong Qin1, Jia Li2, Yonggang Zhou3, Chengliang Yin4,5,6, Yi Li2, Ming Chen2, Yinqiao Du2, Tiejian Li2, Jinglong Yan1. 1. Department of Orthopedics Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China. 2. Department of Orthopedics Surgery, The First Medical Center, Chinese PLA General Hospital, Beijing, China. 3. Department of Orthopedics Surgery, The Fourth Medical Center, Chinese PLA General Hospital, Beijing, China. 4. Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing, China. 5. National Engineering Laboratory for Medical Big Data Application Technology, Chinese PLA General Hospital, Beijing, China. 6. Faculty of Medicine, Macau University of Science and Technology, Macau, China.
Abstract
OBJECTIVE: Synovial inflammation influences the progression of osteoarthritis (OA). Herein, we aimed to identify potential biomarkers and analyze transcriptional regulatory-immune mechanism of synovitis in OA using weighted gene coexpression network analysis (WGCNA). DESIGN: A data set of OA synovium samples (GSE55235) was analyzed based on WGCNA. The most significant module with OA was identified and function annotation of the module was performed, following which the hub genes of the module were identified using Pearson correlation and a protein-protein interaction network was constructed. A transcriptional regulatory network of hub genes was constructed using the TRRUST database. The immune cell infiltration of OA samples was evaluated using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The hub genes coexpressed in multiple tissues were then screened out using data sets of synovium, cartilage, chondrocyte, subchondral bone, and synovial fluid samples. Finally, transcriptional factors and coexpressed hub genes were validated via experiments. RESULTS: The turquoise module of GSE55235 was identified via WGCNA. Functional annotation analysis showed that "mineral absorption" and "FoxO signaling pathway" were mostly enriched in the module. JUN, EGR1, FOSB, and KLF4 acted as central nodes in protein-protein interaction network and transcription factors to connect several target genes. "Activated B cell," "activated CD4T cell," "eosinophil," "neutrophil," and "type 17 T helper cell" showed high immune infiltration, while FOSB, KLF6, and MYBL2 showed significant negative correlation with type 17 T helper cell. CONCLUSIONS: Our results suggest that the expression level of apolipoprotein D (APOD) was correlated with OA. Furthermore, transcriptional regulatory-immune network was constructed, which may contribute to OA therapy.
OBJECTIVE: Synovial inflammation influences the progression of osteoarthritis (OA). Herein, we aimed to identify potential biomarkers and analyze transcriptional regulatory-immune mechanism of synovitis in OA using weighted gene coexpression network analysis (WGCNA). DESIGN: A data set of OA synovium samples (GSE55235) was analyzed based on WGCNA. The most significant module with OA was identified and function annotation of the module was performed, following which the hub genes of the module were identified using Pearson correlation and a protein-protein interaction network was constructed. A transcriptional regulatory network of hub genes was constructed using the TRRUST database. The immune cell infiltration of OA samples was evaluated using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The hub genes coexpressed in multiple tissues were then screened out using data sets of synovium, cartilage, chondrocyte, subchondral bone, and synovial fluid samples. Finally, transcriptional factors and coexpressed hub genes were validated via experiments. RESULTS: The turquoise module of GSE55235 was identified via WGCNA. Functional annotation analysis showed that "mineral absorption" and "FoxO signaling pathway" were mostly enriched in the module. JUN, EGR1, FOSB, and KLF4 acted as central nodes in protein-protein interaction network and transcription factors to connect several target genes. "Activated B cell," "activated CD4T cell," "eosinophil," "neutrophil," and "type 17 T helper cell" showed high immune infiltration, while FOSB, KLF6, and MYBL2 showed significant negative correlation with type 17 T helper cell. CONCLUSIONS: Our results suggest that the expression level of apolipoprotein D (APOD) was correlated with OA. Furthermore, transcriptional regulatory-immune network was constructed, which may contribute to OA therapy.
Authors: Johanne Martel-Pelletier; Andrew J Barr; Flavia M Cicuttini; Philip G Conaghan; Cyrus Cooper; Mary B Goldring; Steven R Goldring; Graeme Jones; Andrew J Teichtahl; Jean-Pierre Pelletier Journal: Nat Rev Dis Primers Date: 2016-10-13 Impact factor: 52.329