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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Bioinformatics Analysis to Identify Intersection Genes, Associated Pathways and Therapeutic Drugs between COVID-19 and Oral Candidiasis

Author(s): Liuqing Yang, Nan Yang, Handan Huang, Jinling Yu, Xin Sui, Lu Tao, Ying Gao and Zhihui Liu*

Volume 26, Issue 8, 2023

Published on: 03 November, 2022

Page: [1533 - 1546] Pages: 14

DOI: 10.2174/1386207325666221007111239

Price: $65

Abstract

Background: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has a serious threat to human health. Oral candidiasis (OC) may be one of the causes of morbidity in severe COVID-19 patients. However, there is currently no treatment for oral candidiasis and COVID-19 (OC/COVID-19). The purpose of this study was to use text mining and data analysis to investigate the target genes for treatment and explore potential therapeutic drugs for OC/COVID-19.

Methods: We used the text mining tool pubmed2ensembl to detect genes associated with OC, and the dataset GSE164805 was used for the data analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on two intersection genes using the Database of Annotation, Visualization and Integrated Discovery (DAVID) platform. The protein-protein interaction (PPI) networks were constructed by STRING software, and gene module analysis was performed using Molecular Complex Detection (MCODE), a plug-in in Cytoscape. The most significant genes were selected as hub genes and their functions and pathways were analyzed using Metascape. We revealed the upstream pathway activity of the hub genes. The drug-gene interaction database (DGIdb) and the traditional Chinese medicines integrated database (TCMID) were used to discover potential drugs for the treatment of OC/COVID-19.

Results: The analysis indicated that there were 2869 differentially expressed genes (DEGs) in GSE164805. We identified 161 unique genes associated with oral candidiasis through text mining. A total of 20 intersection genes were identified as the therapeutic targets for OC/COVID-19. Based on the bioinformatics analysis, nine genes (TNF, IL1B, IFNG, CSF2, ELANE, CCL2, MMP9, CXCR4, and IL1A) were identified as hub genes that were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, AGE-RAGE signaling pathway in diabetic complications and NOD-like receptor signaling pathway. We identified four of the nine genes that target five existing drugs, including BKT140, mavorixafor, sivelestat, canakinumab, and rilonacept. Furthermore, twenty herb ingredients were also screened as potential drugs.

Conclusion: In this study, TNF, IL1B, IFNG, CSF2, ELANE, CCL2, MMP9, CXCR4, and IL1A were potentially key genes involved in the treatment of OC/COVID-19. Taken together five drugs and twenty herb ingredients were identified as potential therapeutic agents for OC/COVID-19 treatment and management.

Keywords: Text mining, bioinformatics, drug discovery, oral candidiasis, COVID-19, differentially expressed genes (DEGs).

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