Bioinformatics-Driven Identification of Genetic Biomarkers and Therapeutic Targets in Dengue Virus Infection

Abstract

The Dengue fever virus (DENV) poses a significant and escalating worldwide health risk. Nevertheless, dengue fever's exact cause and development have yet to be understood entirely. This study used bioinformatics methods to detect probable biomarkers associated with dengue infection and clarify the underlying mechanisms. The study showed that in the GSE51808 and GSE176079 datasets, the behavior of 555 genes in the dengue-infected samples differed notably from that in the normal samples. In comparison, 812 genes showed distinct patterns in another set of samples. The identification of differentially expressed genes (DEGs) that were upregulated was made through the process, and it was found that GSE51808 had 174 upregulated genes while GSE176079 had 71 upregulated genes. In addition, gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment studies, as well as protein-protein interaction (PPI) network analysis, were executed to gain a deeper insight into the roles of DEGs. Additionally, this study identified the top ten hub genes, including SLC4A1, EPB42, TMOD1, DMTN, ALAS2, SNCA, OSBP2, TRIM58, HBQ1, and ANK1. This facilitated the understanding of complex mechanisms through the involvement of specific miRNAs such as hsa-mir-34a-5p and HIF1A acting as transcription factors, which improved the understanding of complex host response to dengue virus. For each identified gene SLC4A1 and SNCA, the additional use of protein-drug interaction analysis on hub genes, followed by validation through molecular docking, yielded two approved drugs. The drugs Atenolol and Metoprolol exhibited interaction scores of 0.921563 and 2.680912, respectively, and binding scores of -6.478 and -6.032 kcal/mol, respectively, with SLC4A1. Ketoconazole and Gentian violet exhibited an interaction score of 0.094067 with SNCA. Additionally, they demonstrated a binding score of -6.2 and -6 kcal/mol, respectively. This study demonstrated the efficacy of bioinformatics analysis techniques in identifying putative genes involved in dengue fever and elucidating their underlying mechanisms. In addition, SLC4A1 SNCA were identified in this study as potential biomarkers linked to DENV infection, thereby presenting intriguing therapeutic targets for dengue fever.