Genetic and epigenetic factors of arterial hypertension: a bibliometric- and in-silico-based analyses

Introduction: Arterial hypertension (AH) is a pervasive global health concern with multifaceted origins encompassing both genetic and environmental components. Previous research has firmly established the association between AH and diverse genetic factors. Consequently, scientists have conducted extensive genetic investigations in recent years to unravel the intricate pathophysiology of AH.Methods: In this study, we conducted a comprehensive bibliometric analysis employing VOSviewer software to identify the most noteworthy genetic factors that have been the focal point of numerous investigations within the AH field in recent years. Our analysis revealed genes and microRNAs intricately linked to AH, underscoring their pivotal roles in this condition. Additionally, we performed molecular docking analyses to ascertain microRNAs with the highest binding affinity to these identified genes. Furthermore, we constructed a network to elucidate the in-silico-based functional interactions between the identified microRNAs and genes, shedding light on their potential roles in AH pathogenesis.Results: Notably, this pioneering in silico examination of genetic factors associated with AH promises novel insights into our understanding of this complex condition. Our findings prominently highlight miR-7110-5p, miR-7110-3p, miR-663, miR-328-3p, and miR-140-5p as microRNAs exhibiting a remarkable affinity for target genes. These microRNAs hold promise as valuable diagnostic and therapeutic factors, offering new avenues for the diagnosis and treatment of AH in the foreseeable future.Conclusion: In summary, this research underscores the critical importance of genetic factors in AH and, through in silico analyses, identifies specific microRNAs with significant potential for further investigation and clinical applications in AH management.

Automated summary

This study reveals the critical role of genetic factors in arterial hypertension through in silico analysis, while identifying specific microRNAs with potential clinical applications.