مقالات پذیرفته شده در هفتمین کنگره بین المللی زیست پزشکی
Employing Bioinformatics Analysis to Explore Key Genes and Pathways in Prostate Cancer
Employing Bioinformatics Analysis to Explore Key Genes and Pathways in Prostate Cancer
Hamidreza Ghaderi Jafarbeigloo,1Arash Goodarzi,2Zahra Abpeikar,3Fariba Noori,4Mozhgan Jirehnezhadyan,5Mohsen Safaei,6,*
1. Department of Tissue Engineering, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran 2. Department of Tissue Engineering, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran 3. Department of Tissue Engineering, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran 4. Department of Tissue Engineering, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran 5. Department of Tissue Engineering, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran 6. Department of Tissue Engineering, School of Advanced Technologies in Medicine, Fasa University of Medical Sciences, Fasa, Iran
Introduction: The importance of effective prostate cancer treatment cannot be underestimated; it is vital to preserving the quality of life for those affected by this common form of cancer. Identifying new treatments through modern sciences such as bioinformatics is essential, allowing for personalized care plans tailored to the individual's needs. Additionally, these advances can help to develop preventative measures that can ultimately reduce the risk of developing this type of cancer.
Methods: From NCBI-GEO, we downloaded the Gene expression dataset GSE55945 and proceeded to analyze its Differentially Expressed Genes (DEGs), relevant Gene Ontology (GO) information, and both the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathways and protein-protein interaction (PPI) networks. After this process, 135 DEGs were identified, and their results were subjected to Functional enrichment analysis, KEGG findings, and PPI network assessment.
Results: In total, 7 hub or key genes, including CAV1, MYLK, CACNA1D, CALM1, NOX4, CCK, and AOX1, were identified. Analyzes related to molecular processes showed that most genes with differential expression are involved in the "mechanism of drug metabolism". Also, the results showed that the molecular function of most genes is related to "G-protein dependent", and "inositol phosphate metabolism" processes.
Conclusion: The DEGS, Key genes, and signaling pathways identified in this study may help understand prostate cancer's molecular mechanisms and provide possible targets for diagnosing and treating this disease.