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与胃癌相关的炎症基因miRNA结合位点内单核苷酸多态性的生物信息学预测

Bioinformatic prediction of SNPs within miRNA binding sites of inflammatory genes associated with gastric cancer.

作者信息

Song Chuan-Qing, Zhang Jun-Hui, Shi Jia-Chen, Cao Xiao-Qin, Song Chun-Hua, Hassan Adil, Wang Peng, Dai Li-Ping, Zhang Jian-Ying, Wang Kai-Juan

机构信息

Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, Henan, China E-mail :

出版信息

Asian Pac J Cancer Prev. 2014;15(2):937-43. doi: 10.7314/apjcp.2014.15.2.937.

Abstract

Polymorphisms in miRNA binding sites have been shown to affect miRNA binding to target genes, resulting in differential mRNA and protein expression and susceptibility to common diseases. Our purpose was to predict SNPs (single nucleotide polymorphisms) within miRNA binding sites of inflammatory genes in relation to gastric cancer. A complete list of SNPs in the 3'UTR regions of all inflammatory genes associated with gastric cancer was obtained from Pubmed. miRNA target prediction databases (MirSNP, Targetscan Human 6.2, PolymiRTS 3.0, miRNASNP 2.0, and Patrocles) were used to predict miRNA target sites. There were 99 SNPs with MAF>0.05 within the miRNA binding sites of 41 genes among 72 inflammation-related genes associated with gastric cancer. NF-κB and JAK-STAT are the two most important signaling pathways. 47 SNPs of 25 genes with 95 miRNAs were predicted. CCL2 and IL1F5 were found to be the shared target genes of hsa-miRNA-624-3p. Bioinformatic methods could identify a set of SNPs within miRNA binding sites of inflammatory genes, and provide data and direction for subsequent functional verification research.

摘要

已证明miRNA结合位点的多态性会影响miRNA与靶基因的结合,从而导致mRNA和蛋白质表达差异以及对常见疾病的易感性。我们的目的是预测与胃癌相关的炎症基因的miRNA结合位点内的单核苷酸多态性(SNP)。从PubMed获得了与胃癌相关的所有炎症基因3'UTR区域内的SNP完整列表。使用miRNA靶标预测数据库(MirSNP、Targetscan Human 6.2、PolymiRTS 3.0、miRNASNP 2.0和Patrocles)来预测miRNA靶位点。在与胃癌相关的72个炎症相关基因中的41个基因的miRNA结合位点内有99个MAF>0.05的SNP。NF-κB和JAK-STAT是两个最重要的信号通路。预测了25个基因与95个miRNA的47个SNP。发现CCL2和IL1F5是hsa-miRNA-624-3p的共同靶基因。生物信息学方法可以识别炎症基因的miRNA结合位点内的一组SNP,并为后续的功能验证研究提供数据和方向。

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