Systemic bioinformatics analysis of recurrent aphthous stomatitis gene expression profiles
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Jian Wu1,*, Zheng-Ping Chen2,*, An-Quan Shang3,*, Wei-Wei Wang4,*, Zong-Ning Chen1, Yun-Juan Tao5, Yue Zhou5 and Wan-Xiang Wang1
1Department of Laboratory Medicine, The First People’s Hospital of Yancheng City, Yancheng 224006, Jiangsu, China
2Clinical Medicine School, Jiangsu Vocational College Medicine, Yancheng 224002, Jiangsu, China
3Department of Laboratory Medicine, Tongji hospital of Tongji University, Shanghai 200092, Shanghai, China
4Department of Pathology,The Sixth People’s Hospital of Yancheng City, Yancheng 224005, Jiangsu, China
5Department of Laboratory Medicine, Yancheng TCM Hospital Affiliated To Nanjing University of Chinese Medicine, Yancheng 224001, Jiangsu, China
*These authors have contributed equally to this work
Yun-Juan Tao, email: firstname.lastname@example.org
Yue Zhou, email: email@example.com
Wan-Xiang Wang, email: firstname.lastname@example.org
Keywords: visualization and integrated discovery (DAVID); gene expression omnibus (GEO); limma; immune; RAS
Received: May 11, 2017 Accepted: August 06, 2017 Published: November 10, 2017
Recurrent aphthous stomatitis (RAS) represents the most common chronic oral diseases with the prevalence ranges from 5% to 25% for different populations. Its pathogenesis remains poorly understood, which limits the development of effective drugs and treatment methods. In this study, we conducted systemic bioinformatics analysis of gene expression profiles from the Gene Expression Omnibus (GEO) to identify potential drug targets for RAS. We firstly downloaded the gene microarray datasets with the accession number of GSE37265 from GEO and performed robust multi-array (RMA) normalization with affy R programming package. Secondly, differential expression genes (DEGs) in RAS samples compared with control samples were identified based on limma package. Enriched gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were obtained through the Database for Annotation, Visualization and Integrated Discovery (DAVID). Finally, protein-protein interaction (PPI) network was constructed based on the combination of HPRD and BioGrid databases. What’s more, we identified modules of PPI network through MCODE plugin of Cytoscape for the purpose of screening of valuable targets. As a result, 915 genes were found to be significantly differential expression in RAS samples and biological processes related to immune and inflammatory response were significantly enriched in those genes. Network and module analysis identified FBXO6, ITGA4, VCAM1 and etc as valuable therapeutic targets for RAS. Finally, FBXO6, ITGA4, and VCAM1 were further confirmed by real time RT-PCR and western blot. This study should be helpful for the research and treatment of RAS.
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