Identifying key genes in rheumatoid arthritis by weighted gene co‐expression network analysis

• Published in: International journal of rheumatic diseases Vol. 20; no. 8; pp. 971 - 979
• Main Authors: Ma, Chunhui, Lv, Qi, Teng, Songsong, Yu, Yinxian, Niu, Kerun, Yi, Chengqin
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 01.08.2017
• Subjects: Antirheumatic Agents - pharmacology; Arthritis, Rheumatoid - diagnosis; Arthritis, Rheumatoid - drug therapy; Arthritis, Rheumatoid - genetics; Benzophenanthridines - pharmacology; Case-Control Studies; CCL19 protein; CCR1 protein; Cluster Analysis; Computational Biology - methods; Databases, Genetic; differentially expressed genes; DNA microarrays; functional annotation; Gene expression; Gene Expression Profiling - methods; Gene Regulatory Networks; Genetic Markers; Genetic Predisposition to Disease; Homeostasis; Humans; Immune response; Immunosuppressive agents; Inflammation; Isoquinolines - pharmacology; Oligonucleotide Array Sequence Analysis; Papaverine - pharmacology; Rheumatoid arthritis; Sanguinarine; Signal Transduction - drug effects; Signal Transduction - genetics; small‐molecule drugs; Transcriptome; weighted gene co‐expression network analysis; differentially expressed genes; weighted gene co-expression network analysis; rheumatoid arthritis; small-molecule drugs; functional annotation
• ISSN: 1756-1841; 1756-185X; 1756-185X
• DOI: 10.1111/1756-185X.13063

Purpose This study aimed to identify rheumatoid arthritis (RA) related genes based on microarray data using the WGCNA (weighted gene co‐expression network analysis) method. Methods Two gene expression profile datasets GSE55235 (10 RA samples and 10 healthy controls) and GSE77298 (16 RA samples and seven healthy controls) were downloaded from Gene Expression Omnibus database. Characteristic genes were identified using metaDE package. WGCNA was used to find disease‐related networks based on gene expression correlation coefficients, and module significance was defined as the average gene significance of all genes used to assess the correlation between the module and RA status. Genes in the disease‐related gene co‐expression network were subject to functional annotation and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery. Characteristic genes were also mapped to the Connectivity Map to screen small molecules. Results A total of 599 characteristic genes were identified. For each dataset, characteristic genes in the green, red and turquoise modules were most closely associated with RA, with gene numbers of 54, 43 and 79, respectively. These genes were enriched in totally enriched in 17 Gene Ontology terms, mainly related to immune response (CD97, FYB, CXCL1, IKBKE, CCR1, etc.), inflammatory response (CD97, CXCL1, C3AR1, CCR1, LYZ, etc.) and homeostasis (C3AR1, CCR1, PLN, CCL19, PPT1, etc.). Two small‐molecule drugs sanguinarine and papaverine were predicted to have a therapeutic effect against RA. Conclusion Genes related to immune response, inflammatory response and homeostasis presumably have critical roles in RA pathogenesis. Sanguinarine and papaverine have a potential therapeutic effect against RA.