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Construction of lncRNA-miRNA-mRNA regulatory network in severe asthmatic bronchial epithelial cells: A bioinformatics study.
Fan, Mengzhen; Song, Wenjie; Hao, Zheng; Zhang, Jing; Li, Yang; Fu, Jinjie.
Afiliação
  • Fan M; School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Song W; School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Hao Z; Tianjin Key Laboratory of Modern Chinese Medicine Theory Innovation and Transformation, Tianjin, China.
  • Zhang J; School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • Li Y; Tianjin Key Laboratory of Modern Chinese Medicine Theory Innovation and Transformation, Tianjin, China.
  • Fu J; Medical History Documentation Center, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
Medicine (Baltimore) ; 102(35): e34749, 2023 Sep 01.
Article em En | MEDLINE | ID: mdl-37657025
ABSTRACT
Asthma is a chronic respiratory disease caused by environment-host interactions. Bronchial epithelial cells (BECs) are the first line of defense against environmental toxins. However, the mechanisms underlying the role of BECs in severe asthma (SA) are not yet fully understood. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) have been shown to play important roles in the regulation of gene expression in the pathogenesis of SA. In this study, bioinformatics was used for the first time to reveal the lncRNA-miRNA-mRNA regulatory network of BECs in SA. Five mRNA datasets of bronchial brushing samples from patients with SA and healthy controls (HC) were downloaded from the Gene Expression Omnibus (GEO) database. A combination of the Venn diagram and robust rank aggregation (RRA) method was used to identify core differentially expressed genes (DEGs). Protein-protein interaction (PPI) analysis of core DEGs was performed to screen hub genes. The miRDB, miRWalk, and ENCORI databases were used to predict the miRNA-mRNA relationships, and the ENCORI and starBase v2.0 databases were used to predict the upstream lncRNAs of the miRNA-mRNA relationships. Four core DEGs were identified carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5), interleukin-1 receptor type 2 (IL1R2), trefoil factor 3 (TFF3), and vascular endothelial growth factor A (VEGFA). These 4 core DEGs indicated that SA was not significantly associated with sex. Enrichment analysis showed that the MAPK, Rap1, Ras, PI3K-Akt and Calcium signaling pathways may serve as the principal pathways of BECs in SA. A lncRNA-miRNA-mRNA regulatory network of the severe asthmatic bronchial epithelium was constructed. The top 10 competing endogenous RNAs (ceRNAs) were FGD5 antisense RNA 1 (FGD5-AS1), metastasis associated lung adenocarcinoma transcript 1 (MALAT1), X inactive specific transcript (XIST), HLA complex group 18 (HCG18), small nucleolar RNA host gene 16 (SNHG16), has-miR-20b-5p, has-miR-106a-5p, hsa-miR-106b-5p, has-miR-519d-3p and Fms related receptor tyrosine kinase 1 (FLT1). Our study revealed a potential mechanism for the lncRNA-miRNA-mRNA regulatory network in BECs in SA.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Asma / MicroRNAs / RNA Longo não Codificante Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Asma / MicroRNAs / RNA Longo não Codificante Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article