C-H Ma1, Q Lv, Y Cao, Q Wang, X-K Zhou, B-W Ye, C-Q Yi. 1. Department of Orthopedic Surgery, Shanghai First People's Hospital, Shanghai Jiao Tong University, Shanghai, China. chengqingyichqy@hotmail.com.
Abstract
BACKGROUND: This study aimed to identify biological markers about osteoarthritis (OA) which is a polygenic disease by investigating the gene expression profiles of the synovium samples from early-stage and end-stage OA patients for the diagnosis and treatment of OA. METHODS: The gene expression profile of GSE32317 was downloaded from Gene Expression Omnibus (GEO) database, including 10 samples from early-stage OA patients and 9 samples from end-stage OA patients. The differentially expressed genes (DEGs) were identified by Significance Analysis of Microarrays. The co-expression network of DEGs was constructed by Pearson correlation test. Then, modules in the constructed co-expression network were selected by MCODE Plugin. What's more, EASE (Expression Analysis Systematic Explorer) was used to define the significant functions and pathways in the identified modules. RESULTS: Total 419 DEGs were identified, among which 112 were up-regulated and 307 down-regulated. We selected 7 statistically significant modules with gene number above 10 and phenotypic correlation test of modules showed that all the modules had significant correlation with OA (p < 0.05). The genes of module 1, module 2 and module 7 were significantly related to immune system functions, protein glycosylation functions, bone, chondrocytes and cartilage functions, respectively. The most significant pathway in module 3 and module 5 was Wnt signal pathway, and in module 4 was Toll-like receptor signal pathway. CONCLUSIONS: DEGs related to immune response, cartilage development, protein glycosylation, muscle development, and DEGs participated in the Wnt signaling pathway and Toll-like receptor (TLR) signaling pathway might be the potential target genes for the OA treatment.
BACKGROUND: This study aimed to identify biological markers about osteoarthritis (OA) which is a polygenic disease by investigating the gene expression profiles of the synovium samples from early-stage and end-stage OA patients for the diagnosis and treatment of OA. METHODS: The gene expression profile of GSE32317 was downloaded from Gene Expression Omnibus (GEO) database, including 10 samples from early-stage OA patients and 9 samples from end-stage OA patients. The differentially expressed genes (DEGs) were identified by Significance Analysis of Microarrays. The co-expression network of DEGs was constructed by Pearson correlation test. Then, modules in the constructed co-expression network were selected by MCODE Plugin. What's more, EASE (Expression Analysis Systematic Explorer) was used to define the significant functions and pathways in the identified modules. RESULTS: Total 419 DEGs were identified, among which 112 were up-regulated and 307 down-regulated. We selected 7 statistically significant modules with gene number above 10 and phenotypic correlation test of modules showed that all the modules had significant correlation with OA (p < 0.05). The genes of module 1, module 2 and module 7 were significantly related to immune system functions, protein glycosylation functions, bone, chondrocytes and cartilage functions, respectively. The most significant pathway in module 3 and module 5 was Wnt signal pathway, and in module 4 was Toll-like receptor signal pathway. CONCLUSIONS: DEGs related to immune response, cartilage development, protein glycosylation, muscle development, and DEGs participated in the Wnt signaling pathway and Toll-like receptor (TLR) signaling pathway might be the potential target genes for the OA treatment.
Authors: Hannah Labinsky; Paul M Panipinto; Kaytlyn A Ly; Deric K Khuat; Bhanupriya Madarampalli; Vineet Mahajan; Jonathan Clabeaux; Kevin MacDonald; Peter J Verdin; Jane H Buckner; Erika H Noss Journal: Arthritis Rheumatol Date: 2020-03-12 Impact factor: 10.995
Authors: S M Iqbal; C Leonard; S C Regmi; D De Rantere; P Tailor; G Ren; H Ishida; Cy Hsu; S Abubacker; D Sj Pang; P T Salo; H J Vogel; D A Hart; C C Waterhouse; G D Jay; T A Schmidt; R J Krawetz Journal: Sci Rep Date: 2016-01-11 Impact factor: 4.379
Authors: Louise H W Kung; Varshini Ravi; Lynn Rowley; Katrina M Bell; Christopher B Little; John F Bateman Journal: Sci Rep Date: 2017-12-18 Impact factor: 4.379