Wang Zhaojun1#, Mo Zhifeng2#, Liang Hongsen1, Zhang Qiwei1, Li Wei1, Yan Dongqing1, Yin Yin1, Fan Haiyang1, Zhang Liang1, Shi Donglei1, Zhang Junhang1*, Li Haifeng3*
1Department of thoracic surgery, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China
2Emergency Department, the Seventh Affiliated Hospital, Sun Yat-sen University, Shen Zhen 518107, China
3Department of Anesthesiology, Guangdong General Hospital, Guangzhou 510080, China
Zhang Jun-hang, E-mail: email@example.com, https://orcid.org/0000-0002-7979-6093
Li Hai-feng, E-mail: firstname.lastname@example.org, https://orcid.org/0000-0001-9111-4377
#Wang Zhaojun and Zhifeng Mo contributed to this study equally.
*Zhang Junhang and Li Haifeng are the corresponding author.
Article History Received 18 April 2021 Accepted 25 May 2021 Published 30 June 2021
Cite this Article Wang Zhaojun, Mo Zhifeng, Liang Hongsen, Zhang Qiwei, Li Wei, Yan Dongqing, Yin Yin, Fan Haiyang, Zhang Liang, Shi Donglei, Zhang Junhang, Li Haifeng. Identification of differentially
expressed genes in asthma by bioinformatics analysis [J].Medical Research, 2021.3(2):28-36, http://dx.doi.org/10.6913/MRHK.202106_3(2).0004
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Objective Asthma is a common inflammatory disease of the airway, and its underlying mechanism is complex. The role of microRNAs (miRNAs) in asthma is unclear. The present study aimed to investigate miRNA-mRNA regulatory networks underlying asthma.
Methods Onemicroarray dataset was downloaded from the Gene Expression Omnibus (GEO) database. Differential expression of miRNAs (DEMs) was identified in bronchial epithelial cells (BECs) isolated from healthy donors and patients with asthma. MiRTarBase, mirDIP, and miRDB were used to predict target genes, followed by protein-protein interaction (PPI) network analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Ontology (GO) analysis; cytoHubba was used to predict the important nodes in the network. The miRNA-hub genes sub-network of interest was determined.
Results This study constructed an asthma-associated miRNA-mRNA network, in which seven key miRNAs and 10 hub genes were identified.
Conclusions The novel miRNAs and hub genes identified in the present study could be potential diagnostic and treatment biomarkers for asthma.
Keywords GEO; miRNA; asthma; bioinformatics