探索缺氧诱导因子的相互作用:通过生物信息学分析揭示疾病的遗传联系

Demet Kivanc Izgi, S. Oguz
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摘要

目的:缺氧诱导因子(Hypoxia-inducible factor, HIF)是一种在细胞感知和适应氧水平变化的能力中起重要作用的转录因子。HIF1α基因位于染色体14q23.2区域,由15个外显子和14个内含子组成。它是代谢过程的转录调节剂,如血管生成和红细胞生成,是免疫反应所必需的。材料和方法:本研究利用多种生物信息学数据库工具研究了HIF1α的功能及其与其他基因和疾病的关系。使用GENEMANIA/GeneCard数据库检测HIF基因与其他基因的关系,miRDB数据库显示靶mirna, STRING数据库检测蛋白-蛋白相互作用,GWAS数据库显示其与疾病的关系。此外,使用UniProt数据库确定其表达的器官和组织。结果:生物信息学分析结果显著,发现189个mirna靶向HIF1α,并与10个基因密切相互作用,其中鉴定出STAT3、MDM2、TP53、SMAD3、VHL等重要基因。HIF-1 α基因利用的最主要途径是HIF-1信号通路。与EPO、PLIN2、BNIP3蛋白和ENO1酶也建立了共表达关系。此外,我们还确定HIF1α在肾脏和肝脏静脉周围区域的表达水平最高。此外,hif - α与肾细胞癌和膀胱癌等疾病密切相关。结论:在生物信息学工具的帮助下,确定与HIF1α、其他基因和表观遗传因素相关的途径,可以使实验研究具有更大的队列和更广阔的视角。因此,它可能有助于我们了解该基因如何影响疾病和异常,并加速靶向治疗的研究。
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Exploring the Interplay of Hypoxia-Inducible Factors: Unveiling Genetic Connections to Diseases Through Bioinformatics Analysis
Objective: Hypoxia-inducible factor (HIF) is a transcription factor that is effective in the ability of cells to sense and adapt to changes in oxygen levels. HIF1α gene is located in the 14q23.2 chromosome region and consists of 15 exons and 14 introns. It is a transcriptional regulator of metabolic processes such as angiogenesis and erythropoiesis and is required for immunological responses. Material and Methods: Our study examined the function of HIF1α and its relations with other genes and diseases using various bioinformatics database tools. GENEMANIA/GeneCard databases were used to detect the relationship of HIF gene with other genes, miRDB to show target miRNAs, STRING to detect protein-protein interaction, and GWAS databases to show its relationship with diseases. In addition, organs and tissues in which it is expressed were determined using the UniProt database. Results: The bioinformatic analysis yielded significant results, revealing that 189 miRNAs target HIF1α and exhibits close interactions with 10 genes, among which important genes like STAT3, MDM2, TP53, SMAD3, and VHL were identified. The most predominant pathway utilized by the HIF1α gene was determined to be the HIF-1 signaling pathway. A co-expression relationship was also established with proteins EPO, PLIN2, BNIP3, and the enzyme ENO1. Furthermore, it was ascertained that HIF1α exhibits the highest expression levels in the kidney and the perivenous region of the liver. Moreover, close associations have been established between HIF1α and diseases such as renal cell carcinoma and bladder cancer. Conclusion: Identifying the pathways associated with HIF1α, other genes, and epigenetic factors with the help of Bioinformatics Tools may enable experimental studies to be carried out with large cohorts and using a broad perspective. Thus, it may contribute to our understanding of how this gene affects diseases and anomalies and to accelerate the studies of targeted therapeutic treatment.
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