Research Papers:

Comprehensive analysis of gene expression and DNA methylation datasets identify valuable biomarkers for rheumatoid arthritis progression

Gang Fang, Qing Huai Zhang, Qianqian Tang, Zuling Jiang, Shasha Xing, Jianying Li and Yuzhou Pang _

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Oncotarget. 2018; 9:2977-2983. https://doi.org/10.18632/oncotarget.22918

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Gang Fang1, Qing Huai Zhang1, Qianqian Tang2, Zuling Jiang3, Shasha Xing2, Jianying Li1 and Yuzhou Pang1

1Laboratory of Zhuang Medicine Prescriptions Basis and Application Research, Guangxi University of Chinese Medicine, Nanning, China

2Department of Rheumatism, Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, China

3Department of Zhuang Medicine, The First Affiliated of Guangxi University of Chinese Medicine, Nanning, China

Correspondence to:

Yuzhou Pang, email: [email protected]

Keywords: Biomarker; DNA methylation; GEO; gene expression; therapeutic methods

Received: July 21, 2017     Accepted: November 03, 2017     Published: December 05, 2017


Rheumatoid arthritis (RA) represents a common systemic autoimmune disease which lays chronic and persistent pain on patients. The purpose of our study is to identify novel RA-related genes and biological processes/pathways. All the datasets of this study, including gene expression and DNA methylation datasets of RA and OA samples, were obtained from the free available database, i.e. Gene Expression Omnibus (GEO). We firstly identified the differentially expressed genes (DEGs) between RA and OA samples through the limma package of R programming software followed by the functional enrichment analysis in the Database for Annotation, Visualization and Integrated Discovery (DAVID) for the exploring of potential involved biological processes/pathways of DEGs. For DNA methylation datasets, we used the IMA package for their normalization and identification of differential methylation genes (DMGs) in RA compared with OA samples. Comprehensive analysis of DEGs and DMGs was also conducted for the identification of valuable RA-related biomarkers. As a result, we obtained 394 DEGs and 363 DMGs in RA samples with the thresholds of |log2fold change|> 1 and p-value < 0.05, and |delta beta|> 0.2 and p-value < 0.05 respectively. Functional analysis of DEGs obtained immune and inflammation associated biological processes/pathways. Besides, several valuable biomarkers of RA, including BCL11B, CCDC88C, FCRLA and APOL6, were identified through the integrated analysis of gene expression and DNA methylation datasets. Our study should be helpful for the development of novel drugs and therapeutic methods for RA.

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