چاپ صفحه |  صفحه نخست سامانه |  نویسندگان |  اطلاعات تفضیلی |  دانلود مقاله | دانشگاه علوم پزشکی تبریز |
| نویسنده ثبت کننده مقاله | نرجس صدیقی |
| مرحله جاری مقاله | تایید نهایی |
| دانشکده/مرکز مربوطه | کمیته تحقیقات دانشجویی |
| کد مقاله | 86021 |
| عنوان فارسی مقاله | شناسایی بیومارکرهای بالقوه مرتبط با سرطان تخمدان با استفاده از تجزیه و تحلیل بیوانفورماتیک |
| عنوان لاتین مقاله | Identification of Potential Biomarkers Associated with Ovarian Cancer Using An Integrated Bioinformatics Analysis |
| نوع ارائه | پوستر |
| عنوان کنگره / همایش | the 20th International Congress of Stem Cell Biology & Technology and the 25th International Congress of Reproductive Biomedicine |
| نوع کنگره / همایش | بین المللی |
| کشور محل برگزاری کنگره/ همایش | Iran (Islamic Republic) |
| شهر محل برگزاری کنگره/ همایش | تهران |
| سال انتشار/ ارائه شمسی | 1403 |
| سال انتشار/ارائه میلادی | |
| تاریخ شمسی شروع و خاتمه کنگره/همایش | 1403/06/07 الی 1403/06/09 |
| آدرس لینک مقاله/ همایش در شبکه اینترنت | https://royancongress.com/preCongress |
| آدرس علمی (Affiliation) نویسنده متقاضی | Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran |
| نویسنده | نفر چندم مقاله |
|---|---|
| نرجس صدیقی | اول |
| مهدی طالبی | دوم |
| عنوان | متن |
|---|---|
| کلمات کلیدی | Bioinformatic analysis; Ovarian cancer; Biomarker |
| خلاصه مقاله | Introduction: Ovarian cancer(OC) is an extremely deadly gynecological cancer. women are often diagnosed at an advanced stage due to the lack of available biomarkers. This study aims to analyze the potential hub-genes associated with the survival of these patients. Method: At first, GEPIA2 was used to examine the TCGA OV dataset in order to identify all DEGs linked with OV among high throughput RNA-Seq data. GEPIA2 is an online program that uses the Genotype-Tissue projects and the TCGA database. Thereafter, significant genes (p-value ≤ 0.05) were divided into 4 groups including up/down prognostic or protective genes according to the logFC and HR. Also, a Protein-protein interaction(PPI) network of significant genes associated with OV was constructed with STRING at the Cytoscape software for identifying hub-genes associated with OV cancer. Next, we used TISIDB to evaluate the immune system's interaction with hub-genes which play a major role in cancer onset, development, and treatment. Ultimately, enrichment analysis including Gene Ontology(GO) was used to explore the related biological processes, molecular function, cellular components, and Kegg pathway analysis. Result: From 7638 genes obtained from TCGA-RNAseq for OV, 341 genes are prognostic and 241 are protective, divided into down and up categories. According to PPI networks, we identified 7 hub-genes based on their degree and interaction, including CXCR4, CXCL10, RPL23, CXCL9, UBD, GZMB, and TNFSF13B. Also, the result of TISIDB showed that most hub-genes have a strong interaction with the immune system except RPL23 and CXCR4 which have less interaction. Ultimately, the enrichment analysis showed these genes have the strongest significance in the inflammatory response, cytolytic granule, CXCR Chemokine Receptor Binding, Cytokine-cytokine receptor interaction, etc. Conclusion: Our study identified 7 hub-genes that might be involved in the prognostic, diagnosis and survival of OV. Further investigations are warranted to identify the therapeutic potential of these genes. |
| نام فایل | تاریخ درج فایل | اندازه فایل | دانلود |
|---|---|---|---|
| OV.pdf | 1403/09/03 | 289495 | دانلود |
| 1732377920706.jpg | 1403/09/03 | 1689655 | دانلود |
