Comprehensive analysis of intervention and control studies for the computational identification of dengue biomarker genes

Dengue fever, caused by the dengue virus (DENV), presents a significant global health concern, with millions of cases reported annually. Despite significant progress in understanding Dengue fever, effective prognosis and treatment remain elusive due to the complex clinical presentations and limitations in current diagnostic methods. The virus, transmitted primarily by the Aedes aegypti mosquito, exists in four closely related forms, each capable of causing flu-like symptoms ranging from mild febrile illness to severe manifestations such as plasma leakage and hemorrhagic fever. Although advancements in diagnostic techniques have been made, early detection of severe dengue remains difficult due to the complexity of its clinical presentations. This study conducted a comprehensive analysis of differential gene expression in dengue fever patients using multiple microarray datasets from the NCBI GEO database. Through bioinformatics approaches, 163 potential biomarker genes were identified, with some overlapping previously reported biomarkers and others representing novel candidates. Notably, AURKA, BUB1, BUB1B, BUB3, CCNA2, CCNB2, CDC6, CDK1, CENPE, EXO1, NEK2, ZWINT, and STAT1 were among the most significant biomarkers. These genes are involved in critical cellular processes, such as cell cycle regulation and mitotic checkpoint control, which are essential for immune cell function and response. Functional enrichment analysis revealed that the dysregulated genes were predominantly associated with immune response to the virus, cell division, and RNA processing. Key regulatory genes such as AURKA, BUB1, BUB3, and CDK1 are found to be involved in cell cycle regulation and have roles in immune-related pathways, underscoring their importance in the host immune response to Dengue virus infection. This study provides novel insights into the molecular mechanisms underlying Dengue fever pathogenesis, highlighting key regulatory genes such as AURKA and CDK1 that could serve as potential biomarkers for early diagnosis and targets for therapeutic intervention, paving the way for improved management of the disease.