1st International and 10th National Iranian Conference on Bioinformatics
Home Accepted Papers
Identification of Potential key Genes and Pathways in Prostate Cancer Using Bioinformatic Analysis
Paper ID : 1246-ICB10
Authors:
Yasaman khamineh, Mahmood Talkhabi *
گروه آموزشی علوم جانوری و زیست شناسی دریا،دانشکده علوم و فناوری زیستی،دانشگاه شهید بهشتی ،تهران،ایران
Abstract:
Prostate cancer (PCa) is the world's second most frequent malignancy that threatens men's health. There are still significant challenges in the treatment of prostate cancer. Its mortality accounts for around 10% of all tumor-related deaths, and it is increasing year by year. The precise molecular pathways are still unknown, prompting urgent study and experience. Therefore, we used bioinformatics analysis to identify potential biomarkers and efficient pathways for PCa early detection. The GSE103512 dataset, which has been downloaded from the Gene Expression Omnibus database (GEO), was normalized using the Transcriptome analysis console (TAC). The genes with adjusted p-value (FDR)< 0.05 and -2<|[log FC]|<2 were identified as differentially expressed genes (DEGs) between 7 normal prostate tissue and 50 PCa samples. Protein-protein interaction (PPI) and visualization were constructed using string, Cytoscape, and Gephi, respectively. We examined these through KEGG pathway enrichment analysis to determine which biological processes, molecular activities may be linked to the overlapping DEGs. According to the findings,822 DEGs (585 up-regulated and 237 down-regulated) were discovered. V-myc avian myelocytomatosis viral oncogene homolog (MYC), SRC proto-oncogene, non-receptor tyrosine kinase (SRC), and cadherin 1, type 1 (CDH1) are three overexpressed genes enriched in the KEGG pathway of peroxisome proliferator-activated receptors (PPARs) and bladder cancer, while caveolin 1 (CAV1), jun proto-oncogene (JUN), and elastin (ELN) are three low expressed genes enriched in the KEGG pathway of protein digestion and absorption and focal adhesion pathways. DEGs in prostate cancer were significantly enriched in the following GO terms (most significant) under the biological process's group: ‘extracellular matrix organization’(GO:0030198), ‘aortic valve morphogenesis’(GO:0003180), ‘fatty-acyl-CoA biosynthetic process’(GO:0046949), and ‘polyamine metabolic process’(GO:0006595). Taken together, we discovered new potential key genes in PCa that might function as robust biomarkers and provide important information for future molecularly targeted therapeutics.
Keywords:
Key words: prostate cancer; differentially expressed genes; pathway; bioinformatics; biomarker
Status : Paper Accepted (Poster Presentation)