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

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 s...