Identification of Hub Genes and Potential Biomarkers for Childhood Asthma by Utilizing an Established Bioinformatic Analysis Approach.

Autor:	Santri IN; Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia., Irham LM; Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia., Djalilah GN; Medical Faculty Muhammadiyah Surabaya, Surabaya 60115, Indonesia., Perwitasari DA; Faculty of Pharmacy, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia., Wardani Y; Faculty of Public Health, Universitas Ahmad Dahlan, Yogyakarta 55164, Indonesia., Phiri YVA; School of Public Health, College of Public Health, Taipei Medical University, Taipei 11031, Taiwan.; Institute for Health Research and Communication (IHRC), Lilongwe P.O. Box 1958, Malawi., Adikusuma W; Departement of Pharmacy, University of Muhammadiyah Mataram, Mataram 83127, Indonesia.
Jazyk:	angličtina
Zdroj:	Biomedicines [Biomedicines] 2022 Sep 16; Vol. 10 (9). Date of Electronic Publication: 2022 Sep 16.
DOI:	10.3390/biomedicines10092311
Abstrakt:	Childhood asthma represents a heterogeneous disease resulting from the interaction between genetic factors and environmental exposures. Currently, finding reliable biomarkers is necessary for the clinical management of childhood asthma. However, only a few biomarkers are being used in clinical practice in the pediatric population. In the long run, new biomarkers for asthma in children are required and would help direct therapy approaches. This study aims to identify potential childhood asthma biomarkers using a genetic-driven biomarkers approach. Herein, childhood asthma-associated Single Nucleotide Polymorphisms (SNPs) were utilized from the GWAS database to drive and facilitate the biomarker of childhood asthma. We uncovered 466 childhood asthma-associated loci by extending to proximal SNPs based on r2 > 0.8 in Asian populations and utilizing HaploReg version 4.1 to determine 393 childhood asthma risk genes. Next, the functional roles of these genes were subsequently investigated using Gene Ontology (GO) term enrichment analysis, a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and a protein−protein interaction (PPI) network. MCODE and CytoHubba are two Cytoscape plugins utilized to find biomarker genes from functional networks created using childhood asthma risk genes. Intriguingly, 10 hub genes (IL6, IL4, IL2, IL13, PTPRC, IL5, IL33, TBX21, IL2RA, and STAT6) were successfully identified and may have been identified to play a potential role in the pathogenesis of childhood asthma. Among 10 hub genes, we strongly suggest IL6 and IL4 as prospective childhood asthma biomarkers since both of these biomarkers achieved a high systemic score in Cytohubba’s MCC algorithm. In summary, this study offers a valuable genetic-driven biomarker approach to facilitate the potential biomarkers for asthma in children. Competing Interests: The authors declare that no conflicts of interest exist.
Databáze:	MEDLINE
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