مقالات پذیرفته شده در هفتمین کنگره بین المللی زیست پزشکی
Predicting Glioblastoma-Associated MicroRNAs Through Bioinformatics Analysis
Predicting Glioblastoma-Associated MicroRNAs Through Bioinformatics Analysis
Negar Zamani Alavijeh,1,*
1. Department of biology,Higher institute Naghsh-jahan, Isfahan, Iran
Introduction: Predicting various microRNAs associated with glioblastoma through bioinformatic approaches is a critical research area in cancer biology and bioinformatics. Glioblastoma, a highly aggressive form of brain
cancer, presents significant challenges in terms of diagnosis and treatment. MicroRNAs (miRNAs) are short RNA molecules that play crucial roles in gene regulation and have been implicated in the development and progression of glioblastoma. This research aims to leverage the power of bioinformatics tools and techniques to analyze genomic and transcriptomic data. These dysregulated miRNAs may serve as biomarkers for early diagnosis or therapeutic targets for this deadly disease. Recent advancements in high-throughput sequencing technologies have generated vast amounts of data, making bioinformatic analysis an indispensable tool for uncovering the intricate molecular mechanisms underlying glioblastoma. This approach not only enhances our understanding of the disease but also offers new avenues for the development of personalized treatment strategies. The integration of multidisciplinary knowledge from genetics, bioinformatics, and oncology is crucial for making strides in glioblastoma research.
Methods: The expression profiles of glioblastoma-related genes were assessed in the GSE100675 dataset using the GEO2R package from the Gene Expression Omnibus (GEO).To pinpoint the most significant genes contributing to glioblastoma onset, we conducted differential expression analysis and further examined these genes in the DAVID database.In parallel, we identified microRNAs associated with these critical genes using the miRWalk database.The Human microRNA Disease Database (HMDD) was instrumental in revealing the microRNAs implicated in glioblastoma pathogenesis.
Results: The analyses revealed the significant role of the myelin transcription factor 1-like (MYT1L) gene in glioblastoma.Utilizing the miRWalk database, we identified a set of MYT1L-related microRNAs, including hsa-miR-21-3p, hsa-miR-10a-5p, hsa-miR-15a-5p, and hsa-miR-19b-3p.
Conclusion: Using bioinformatics to predict microRNAs associated with glioblastoma holds promise for improving our understanding of this aggressive brain cancer. These microRNAs could serve as vital diagnostic tools and potential therapeutic targets. The interdisciplinary approach of genetics, bioinformatics, and oncology is pivotal in advancing glioblastoma research. This innovative use of computational techniques offers hope for more effective treatments and better outcomes in the battle against glioblastoma.