کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8408450 | 1545070 | 2017 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer
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کلمات کلیدی
Gene moduleROCccRCCTCGARCCeQTLDGMAUC - AUCThe cancer genome atlas - اوتومتر ژنوم سرطانExpression quantitative trait loci - بیان صفات صفات کمیProtein-protein interaction - تعامل پروتئین-پروتئینKEGG یا Kyoto Encyclopedia of Genes and Genomes - دایرة المعارف ژن ها و ژنوم کیوتو Kyoto Encyclopedia of Genes and Genomes - دایره المعارف ژنتیک ژن ها و ژنوم کیوتوRenal cell cancer - سرطان سلول کلیهClear cell renal cell carcinoma - سلول خونی سلولهای کلیهDEG - شماSVM - ماشین بردار پشتیبانیSupport vector machine - ماشین بردار پشتیبانیPathways - مسیرهایarea under curve - منطقه تحت منحنیDifferentially expressed gene - ژن بیان شده متفاوت استreceiver operating characteristic - گیرنده عامل عامل
موضوعات مرتبط
علوم زیستی و بیوفناوری
بیوشیمی، ژنتیک و زیست شناسی مولکولی
بیوتکنولوژی یا زیستفناوری
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چکیده انگلیسی
Clear cell renal cell carcinoma (ccRCC) is the most common and most aggressive form of renal cell cancer (RCC). The incidence of RCC has increased steadily in recent years. The pathogenesis of renal cell cancer remains poorly understood. Many of the tumor suppressor genes, oncogenes, and dysregulated pathways in ccRCC need to be revealed for improvement of the overall clinical outlook of the disease. Here, we developed a systems biology approach to prioritize the somatic mutated genes that lead to dysregulation of pathways in ccRCC. The method integrated multi-layer information to infer causative mutations and disease genes. First, we identified differential gene modules in ccRCC by coupling transcriptome and protein-protein interactions. Each of these modules consisted of interacting genes that were involved in similar biological processes and their combined expression alterations were significantly associated with disease type. Then, subsequent gene module-based eQTL analysis revealed somatic mutated genes that had driven the expression alterations of differential gene modules. Our study yielded a list of candidate disease genes, including several known ccRCC causative genes such as BAP1 and PBRM1, as well as novel genes such as NOD2, RRM1, CSRNP1, SLC4A2, TTLL1 and CNTN1. The differential gene modules and their driver genes revealed by our study provided a new perspective for understanding the molecular mechanisms underlying the disease. Moreover, we validated the results in independent ccRCC patient datasets. Our study provided a new method for prioritizing disease genes and pathways.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational and Structural Biotechnology Journal - Volume 15, 2017, Pages 463-470
Journal: Computational and Structural Biotechnology Journal - Volume 15, 2017, Pages 463-470
نویسندگان
Mary Qu Yang, Dan Li, William Yang, Yifan Zhang, Jun Liu, Weida Tong,