Bioinformatics analysis to reveal the key genes related to obstructive sleep apnea

• Published in: Sleep & breathing Vol. 23; no. 1; pp. 259 - 267
• Main Authors: Gu, Xiandong, Yang, Wei, Luo, Xuming, Wang, Xiongbiao, Tang, Jihong, Cai, Zhuying
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.03.2019; Springer Nature B.V
• Subjects: Adipose tissue; Adipose Tissue - metabolism; Apnea; Bioinformatics; Computational Biology; Dentistry; Down-Regulation - genetics; Epstein-Barr virus; Epstein-Barr Virus Infections - genetics; Gene expression; Gene Expression Profiling; Gene Regulatory Networks - genetics; Humans; Interleukin 1; Interleukin-10 Receptor beta Subunit - genetics; Internal Medicine; Medicine; Medicine & Public Health; Mitogen-Activated Protein Kinase 10 - genetics; Mitogen-Activated Protein Kinase 9 - genetics; Neurology; Oligonucleotide Array Sequence Analysis; Otorhinolaryngology; Pediatrics; Pneumology/Respiratory System; Protein interaction; Protein Interaction Domains and Motifs - genetics; Reference Values; Respiratory tract; Sleep; Sleep apnea; Sleep Apnea, Obstructive - genetics; Sleep Breathing Physiology and Disorders • Original Article; Sleep disorders; Thioredoxins - genetics; Up-Regulation - genetics; Differentially expressed genes; Protein-protein interaction network; Integrated regulatory network; Enrichment analysis; Obstructive sleep apnea
• ISSN: 1520-9512; 1522-1709
• DOI: 10.1007/s11325-018-1694-7

Purpose Obstructive sleep apnea (OSA) is induced by obstruction of the upper airway, which can raise multiple health risks. This study is designed to reveal the key genes involved in OSA. Methods GSE38792 was extracted from Gene Expression Omnibus database, including ten visceral adipose tissues from OSA patients and eight visceral adipose tissues from normal controls. Differential expression analysis was conducted using limma package, and then the functions of the differentially expressed genes (DEGs) were analyzed using DAVID database, followed by protein-protein interaction (PPI) network, and integrated regulatory network analysis was performed using Cytoscape software. Results A total of 368 DEGs (176 upregulated and 192 downregulated) were identified in OSA samples. Epstein-Barr virus infection (involving IL10RB , MAPK9 , and MAPK10 ) and olfactory transduction were the main pathways separately enriched for the upregulated genes and the downregulated genes. After the PPI network was built, the top ten network nodes (such as TXN) were selected according to node degrees. Two significant PPI network modules were identified. Moreover, the integrated regulatory network was constructed. Conclusion IL10RB , MAPK9 , MAPK10 , and TXN might function in the pathogenesis of OSA.