Kai Wang1,2, Rujie Zhu1, Ju Li2, Zhongyuan Zhang2, Xin Wen3, Hongwei Chen4, Lingyun Sun5,6. 1. Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Zhongshan Road 321, Nanjing, 210008, People's Republic of China. 2. Department of Rheumatology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, 223001, People's Republic of China. 3. Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People's Republic of China. 4. Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People's Republic of China. chenhw@nju.edu.cn. 5. Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Zhongshan Road 321, Nanjing, 210008, People's Republic of China. lingyunsun@nju.edu.cn. 6. Department of Rheumatology and Immunology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, 210008, People's Republic of China. lingyunsun@nju.edu.cn.
Abstract
OBJECTIVE: Polymyositis (PM) is a chronic autoimmune connective tissue disease whose pathogenic mechanisms are unclear. This study aimed to identify the main genes and functionally enriched pathways involved in PM using weighted gene coexpression network analysis (WGCNA). METHODS: To identify the candidate genes of PM, microarray datasets GSE128470, GSE3112, GSE39454 and GSE125977 were obtained from the Gene Expression Omnibus database. The gene network of GSE128470 was constructed, and WGCNA was used to divide genes into different modules. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were applied to the most PM-related modules. The datasets were used to verify the expression profile and diagnostic capabilities of the hub genes. Additionally, gene set enrichment analysis (GSEA) was carried out. Moreover, gene signatures were then used as a search query to explore the connectivity map (CMap). RESULTS: A weighted gene coexpression network was constructed, and the genes were divided into 66 modules. The enriched functions and candidate pathway modules included interferon-γ, type I interferon, cellular response to interferon-γ, neutrophil activation, neutrophil degranulation, neutrophil-mediated immunity and neutrophil activation involved in the immune response. A total of 22 hub genes were identified. The Mann-Whitney U test was performed on these 22 genes using the three datasets of muscle samples and one dataset of whole blood samples, and two genes significantly differentially expressed in all datasets were obtained: VCAM1 and LY96. Receiver operating characteristic curve analysis determined that VCAM1 and LY96 gene expression can distinguish PM from healthy controls (the area under the curve [AUC] was greater than 0.75). Logistic regression analysis was performed on the combination of LY96 and VCAM1. The accuracy, sensitivity, specificity, and AUC of the combination reached 1.0. GSEA of VCAM1 and LY96 revealed their relation to 'inflammatory response', 'TNF-α signalling via NF-κB', 'complement' and 'myogenesis'. CMap research revealed a few compounds with the potential to counteract the effects of the dysregulated molecular signature in PM. CONCLUSIONS: We used WGCNA to observe all aspects of PM, which helped to elucidate the molecular mechanisms of PM onset and progression and provide candidate targets for the diagnosis and treatment of PM. Key Points • Four microarray datasets were analysed in patients with polymyositis and healthy controls, and VCAM1 and LY96 were significant genes in all datasets. • GSEA of VCAM1 and LY96 revealed that they were mainly related to 'inflammatory response', 'TNF-α signalling via NF-κB', 'complement' and 'myogenesis'. • CMap found a few compounds such as dimethyloxalylglycine and HNMPA-(AM)3 with the potential to counteract the effects of the dysregulated molecular signature in PM.
OBJECTIVE: Polymyositis (PM) is a chronic autoimmune connective tissue disease whose pathogenic mechanisms are unclear. This study aimed to identify the main genes and functionally enriched pathways involved in PM using weighted gene coexpression network analysis (WGCNA). METHODS: To identify the candidate genes of PM, microarray datasets GSE128470, GSE3112, GSE39454 and GSE125977 were obtained from the Gene Expression Omnibus database. The gene network of GSE128470 was constructed, and WGCNA was used to divide genes into different modules. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were applied to the most PM-related modules. The datasets were used to verify the expression profile and diagnostic capabilities of the hub genes. Additionally, gene set enrichment analysis (GSEA) was carried out. Moreover, gene signatures were then used as a search query to explore the connectivity map (CMap). RESULTS: A weighted gene coexpression network was constructed, and the genes were divided into 66 modules. The enriched functions and candidate pathway modules included interferon-γ, type I interferon, cellular response to interferon-γ, neutrophil activation, neutrophil degranulation, neutrophil-mediated immunity and neutrophil activation involved in the immune response. A total of 22 hub genes were identified. The Mann-Whitney U test was performed on these 22 genes using the three datasets of muscle samples and one dataset of whole blood samples, and two genes significantly differentially expressed in all datasets were obtained: VCAM1 and LY96. Receiver operating characteristic curve analysis determined that VCAM1 and LY96 gene expression can distinguish PM from healthy controls (the area under the curve [AUC] was greater than 0.75). Logistic regression analysis was performed on the combination of LY96 and VCAM1. The accuracy, sensitivity, specificity, and AUC of the combination reached 1.0. GSEA of VCAM1 and LY96 revealed their relation to 'inflammatory response', 'TNF-α signalling via NF-κB', 'complement' and 'myogenesis'. CMap research revealed a few compounds with the potential to counteract the effects of the dysregulated molecular signature in PM. CONCLUSIONS: We used WGCNA to observe all aspects of PM, which helped to elucidate the molecular mechanisms of PM onset and progression and provide candidate targets for the diagnosis and treatment of PM. Key Points • Four microarray datasets were analysed in patients with polymyositis and healthy controls, and VCAM1 and LY96 were significant genes in all datasets. • GSEA of VCAM1 and LY96 revealed that they were mainly related to 'inflammatory response', 'TNF-α signalling via NF-κB', 'complement' and 'myogenesis'. • CMap found a few compounds such as dimethyloxalylglycine and HNMPA-(AM)3 with the potential to counteract the effects of the dysregulated molecular signature in PM.
Authors: Anna Tjärnlund; Quan Tang; Cecilia Wick; Maryam Dastmalchi; Herman Mann; Jana Tomasová Studýnková; Radka Chura; Nicola J Gullick; Rosaria Salerno; Johan Rönnelid; Helene Alexanderson; Eva Lindroos; Rohit Aggarwal; Patrick Gordon; Jiri Vencovsky; Ingrid E Lundberg Journal: Ann Rheum Dis Date: 2017-10-09 Impact factor: 19.103
Authors: S Bernatsky; L Joseph; C A Pineau; P Bélisle; J F Boivin; D Banerjee; A E Clarke Journal: Ann Rheum Dis Date: 2008-08-19 Impact factor: 19.103