Biostatistical Analysis of Microarray Data to Decipher Viral Pathogenesis
DOI:
https://doi.org/10.6000/1929-6029.2024.13.37Keywords:
Viral infection, Diagnosis, Treatment, statistical modeling, data visualizationAbstract
Background: Zika virus, Kunjin virus, Yellow Fever virus, & Sindbis virus belong to Flaviviridae family and are involved in derailing various biological pathways which are not yet elucidated.
Aim: Understanding the gene as well as miRNA interplay which plays a vital role in pathogenesis in the diagnosis and prognosis of the disease is of utmost significance.
Materials and Methods: By leveraging microarray data from the Gene Expression Omnibus GSE232504 dataset, we meticulously examined the differentially expressed genes & micro RNAs (miRNAs) induced by viral infections.
Results: Our analysis revealed 60 statistically significant and differentially expressed genes (DEGs) out of a total of 18,725, with SESN2 (SESTRIN 2) and GADD45A (Growth Arrest and DNA Damage-Inducible Alpha) standing out as highly significant players in the host cell response to these viruses. hsa-miR-148b-3p, hsa-miR-148a-3p, hsa-miR-607 & hsa-miR-5582-3p were the highly expressed micro RNAs (miRNAs). Through functional enrichment analyses, we unveiled significant pathways, including Type 1 Diabetes Mellitus and NF-kappa B Signaling, shedding light on the potential mechanisms underlying these virus-host cell interactions. Furthermore, our PPI (protein-protein interaction) network analysis highlighted key hub genes, while our exploration of miRNA-gene targeting relationships offered valuable insights into post-transcriptional regulation.
Conclusion: This study provides a robust foundation for understanding the molecular intricacies of virus-host cell interactions, offering potential targets for further experimental validation and paving the way for innovative therapeutic approaches in combatting viral infections and associated diseases.
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