Exploring the Interplay of Hypoxia-Inducible Factors: Unveiling Genetic Connections to Diseases Through Bioinformatics Analysis


Kivanc Izgi D., Oguz S. R.

Medical science and discovery, cilt.10, sa.9, ss.669-672, 2023 (Hakemli Dergi)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 9
  • Basım Tarihi: 2023
  • Doi Numarası: 10.36472/msd.v10i9.1030
  • Dergi Adı: Medical science and discovery
  • Derginin Tarandığı İndeksler: Index Copernicus
  • Sayfa Sayıları: ss.669-672
  • İstanbul Üniversitesi Adresli: Evet

Özet

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.