生物信息学分析鉴定可能与临床相关的NDRG1基因变异

Borré Gb, Pimenta At, Chmieleski Gs, Moyses Gr, Souza Scb, Rabi Lt, Peres Kc, Teixeira Es, Bufalo Ne, Ward Ls
{"title":"生物信息学分析鉴定可能与临床相关的NDRG1基因变异","authors":"Borré Gb, Pimenta At, Chmieleski Gs, Moyses Gr, Souza Scb, Rabi Lt, Peres Kc, Teixeira Es, Bufalo Ne, Ward Ls","doi":"10.26502/jbsb.5107043","DOIUrl":null,"url":null,"abstract":"Background: The search of single nucleotide variants that might have the capacity to alter genetic information and influence in regular cellular pathways, enhancing expansion, mitosis and evasion capacity to neoplasm cells, is central in understanding the molecular nature of distinct cellular growth abnormalities and is critical because it might expose new possibilities for therapeutic targets. The expression of NDRG1 protein, encoded by NDRG1 gene, has already been correlated with tumor progression and evasion, but information on different types of neoplasm is still contentious. Objective: To explore probable correlations of susceptibleness, progression and clinical characteristics between NDRG1 gene polymorphisms (SNPs) and patients that developed thyroid tumors. Methods: SNPs were obtained from the NCBI dbSNP. The encoded protein primary sequences were got from the UniProt database. We employed the three FASTA primary sequences to analyze the amino acid changes. The bioinformatics tools used were: PredictSNP1.0 (which encompasses: PANTHER, SNAP, PolyPhen-1, PhD-SNP, nsSNPAnalyze, SIFT, PredictSNP, PolyPhen-2, MAPP,); I-Mutant2.0; MUpro; PROVEAN; Haploview and SNPs3D). Results: The NCB database reports 319 missense SNPs in the NDRG1 gene. The SIFT tool predicted that 51 nsSNPs of 109 (which means 46.78%) were deleterious; the SNAP tool predicted nearly 30%; PolyPhen-2, 53 (48.62%); 52 (47.70%) derived from PhD-SNP; PolyPhen-1 indicated 38 nsSNPs (approximately 35%); and MAPP showed 47 (which is 43%). Finally, the PredictSNP toll contemplated 13 (approximately 12%) nsSNPs deleterious by all integrated tools, including rs201348291 and rs15132213, whose scores were the most significant, thus indicating a higher possibility that these SNPs are correlated and influence the pathophysiology of thyroid neoplasm. Conclusions: We demonstrated that NDRG1 rs201348291 and rs151322132 may be involved in thyroid cancer emergence and deserve further validation and evaluation of their clinical applicability in determining the risk of thyroid nodules malignancy and thyroid cancer prognostic.","PeriodicalId":73617,"journal":{"name":"Journal of bioinformatics and systems biology : Open access","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bioinformatics Analysis Identifies NDRG1 Gene Variants that may be Clinically Relevant\",\"authors\":\"Borré Gb, Pimenta At, Chmieleski Gs, Moyses Gr, Souza Scb, Rabi Lt, Peres Kc, Teixeira Es, Bufalo Ne, Ward Ls\",\"doi\":\"10.26502/jbsb.5107043\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: The search of single nucleotide variants that might have the capacity to alter genetic information and influence in regular cellular pathways, enhancing expansion, mitosis and evasion capacity to neoplasm cells, is central in understanding the molecular nature of distinct cellular growth abnormalities and is critical because it might expose new possibilities for therapeutic targets. The expression of NDRG1 protein, encoded by NDRG1 gene, has already been correlated with tumor progression and evasion, but information on different types of neoplasm is still contentious. Objective: To explore probable correlations of susceptibleness, progression and clinical characteristics between NDRG1 gene polymorphisms (SNPs) and patients that developed thyroid tumors. Methods: SNPs were obtained from the NCBI dbSNP. The encoded protein primary sequences were got from the UniProt database. We employed the three FASTA primary sequences to analyze the amino acid changes. The bioinformatics tools used were: PredictSNP1.0 (which encompasses: PANTHER, SNAP, PolyPhen-1, PhD-SNP, nsSNPAnalyze, SIFT, PredictSNP, PolyPhen-2, MAPP,); I-Mutant2.0; MUpro; PROVEAN; Haploview and SNPs3D). Results: The NCB database reports 319 missense SNPs in the NDRG1 gene. The SIFT tool predicted that 51 nsSNPs of 109 (which means 46.78%) were deleterious; the SNAP tool predicted nearly 30%; PolyPhen-2, 53 (48.62%); 52 (47.70%) derived from PhD-SNP; PolyPhen-1 indicated 38 nsSNPs (approximately 35%); and MAPP showed 47 (which is 43%). Finally, the PredictSNP toll contemplated 13 (approximately 12%) nsSNPs deleterious by all integrated tools, including rs201348291 and rs15132213, whose scores were the most significant, thus indicating a higher possibility that these SNPs are correlated and influence the pathophysiology of thyroid neoplasm. Conclusions: We demonstrated that NDRG1 rs201348291 and rs151322132 may be involved in thyroid cancer emergence and deserve further validation and evaluation of their clinical applicability in determining the risk of thyroid nodules malignancy and thyroid cancer prognostic.\",\"PeriodicalId\":73617,\"journal\":{\"name\":\"Journal of bioinformatics and systems biology : Open access\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of bioinformatics and systems biology : Open access\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.26502/jbsb.5107043\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of bioinformatics and systems biology : Open access","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.26502/jbsb.5107043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

背景:寻找可能有能力改变遗传信息和影响常规细胞途径、增强肿瘤细胞扩张、有丝分裂和逃避能力的单核苷酸变异,是理解不同细胞生长异常的分子本质的核心,也是至关重要的,因为它可能为治疗靶点提供新的可能性。由NDRG1基因编码的NDRG1蛋白的表达已经与肿瘤的进展和逃避相关,但关于不同类型肿瘤的信息仍然存在争议。目的:探讨NDRG1基因多态性(SNPs)与甲状腺肿瘤患者的易感性、进展及临床特征的可能相关性。方法:从NCBI dbSNP中提取snp。编码的蛋白一级序列从UniProt数据库中获取。我们使用三个FASTA初级序列分析氨基酸变化。使用的生物信息学工具有:PredictSNP1.0(包括:PANTHER、SNAP、polyphen1、PhD-SNP、nsSNPAnalyze、SIFT、PredictSNP、polyphen2、MAPP、);I-Mutant2.0;MUpro;PROVEAN;Haploview和SNPs3D)。结果:NCB数据库报告了NDRG1基因的319个错义snp。SIFT工具预测109个nsSNPs中有51个是有害的(46.78%);SNAP工具预测近30%;polyphen2, 53 (48.62%);52个(47.70%)来自PhD-SNP;polyphen1检测到38个nssnp(约35%);MAPP显示47(43%)。最后,PredictSNP toll通过所有综合工具预测了13个(约12%)有害的nssnp,包括rs201348291和rs15132213,其评分最显著,因此表明这些snp更有可能相关并影响甲状腺肿瘤的病理生理。结论:我们证明NDRG1 rs201348291和rs151322132可能参与甲状腺癌的发生,值得进一步验证和评估其在确定甲状腺结节恶性风险和甲状腺癌预后方面的临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Bioinformatics Analysis Identifies NDRG1 Gene Variants that may be Clinically Relevant
Background: The search of single nucleotide variants that might have the capacity to alter genetic information and influence in regular cellular pathways, enhancing expansion, mitosis and evasion capacity to neoplasm cells, is central in understanding the molecular nature of distinct cellular growth abnormalities and is critical because it might expose new possibilities for therapeutic targets. The expression of NDRG1 protein, encoded by NDRG1 gene, has already been correlated with tumor progression and evasion, but information on different types of neoplasm is still contentious. Objective: To explore probable correlations of susceptibleness, progression and clinical characteristics between NDRG1 gene polymorphisms (SNPs) and patients that developed thyroid tumors. Methods: SNPs were obtained from the NCBI dbSNP. The encoded protein primary sequences were got from the UniProt database. We employed the three FASTA primary sequences to analyze the amino acid changes. The bioinformatics tools used were: PredictSNP1.0 (which encompasses: PANTHER, SNAP, PolyPhen-1, PhD-SNP, nsSNPAnalyze, SIFT, PredictSNP, PolyPhen-2, MAPP,); I-Mutant2.0; MUpro; PROVEAN; Haploview and SNPs3D). Results: The NCB database reports 319 missense SNPs in the NDRG1 gene. The SIFT tool predicted that 51 nsSNPs of 109 (which means 46.78%) were deleterious; the SNAP tool predicted nearly 30%; PolyPhen-2, 53 (48.62%); 52 (47.70%) derived from PhD-SNP; PolyPhen-1 indicated 38 nsSNPs (approximately 35%); and MAPP showed 47 (which is 43%). Finally, the PredictSNP toll contemplated 13 (approximately 12%) nsSNPs deleterious by all integrated tools, including rs201348291 and rs15132213, whose scores were the most significant, thus indicating a higher possibility that these SNPs are correlated and influence the pathophysiology of thyroid neoplasm. Conclusions: We demonstrated that NDRG1 rs201348291 and rs151322132 may be involved in thyroid cancer emergence and deserve further validation and evaluation of their clinical applicability in determining the risk of thyroid nodules malignancy and thyroid cancer prognostic.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Linear Regression of Sampling Distributions of the Mean. Transcriptional and Translational Regulation of Differentially Expressed Genes in Yucatan Miniswine Brain Tissues following Traumatic Brain Injury. The Growing Liberality Observed in Primary Animal and Plant Cultures is Common to the Social Amoeba. Role of Transcription Factors and MicroRNAs in Regulating Fibroblast Reprogramming in Wound Healing. CDKs Functional Analysis in Low Proliferating Early-Stage Pancreatic Ductal Adenocarcinoma.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1