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通过生物信息学筛选与结直肠癌转移相关的基因及在结直肠癌转移研究中的应用

Screening genes related to colorectal cancer metastasis through bioinformatics and its application in colorectal cancer metastasis
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摘要 目的:通过生物信息学筛选与结直肠癌转移相关的关键基因,研究关键基因与结直肠癌生存预后的关系,并评估其作为生物标志物的预测价值。方法:从Gene Expression Omnibus(GEO)数据库筛选出GSE68468和GSE81558的表达谱,分析结直肠癌原发性肿瘤与转移性肿瘤之间差异表达的基因(Differentially expressed genes,DEGs)。构建了DEGs的蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络找出核心基因等。结果:本研究纳入两个GEO数据集(GSE68468和GSE81558),筛选出95个DEGs,其中上调的基因76个,下调的基因19个。从PPI网络中筛选出26个与结直肠癌远处转移相关的核心基因。并进一步分析出TTR,AHOP,APOC3,PLG与总生存率(overall survival,OS)有显著相关性。结论:通过生物信息学方法筛选与结直肠癌转移密切相关的核心基因,为探索新的干预治疗措施提供理论依据。 Objective:To screen key genes associated with colorectal cancer metastasis by bioinformatics to study the relationship between key genes and survival prognosis of colorectal cancer,and to evaluate their predictive value as biomarkers.Methods:The Expression profiles of GSE68468 and GSE81558 were screened from the Gene Expression Omnibus(GEO)database to analyze the Differentially expressed genes(DEGs)between primary colorectal cancer tumors and metastatic tumors.The protein-protein interaction(PPI)network of DEGs was constructed to find the core genes.Results:In this study,two GEO data sets(GSE68468 and GSE81558)were included,and 95 DEGs were selected,including 76 up-regulated genes and 19 down-regulated genes.26 core genes associated with distant metastasis of colorectal cancer were screened from PPI network.It was further analyzed that TTR,AHOP,APOC3,and PLG were significantly correlated with overall survival(OS).Conclusions:Bioinformatics methods are used to screen for core genes that are closely related to colorectal cancer metastasis,providing a theoretical basis for exploring new interventions.
作者 吴骁 高振远 苏方 张甜甜 王俊斌 WU Xiao;GAO Zhen-yuan;SU Fang;ZHANG Tian-tian;WANG Jun-bin(The First Affiliated Hospital of Bengbu Medical College,Bengbu,Anhui 233000)
出处 《赣南医学院学报》 2020年第4期366-372,共7页 JOURNAL OF GANNAN MEDICAL UNIVERSITY
基金 蚌埠医学院自然科学重点项目(编号:BYKY2019081ZD)。
关键词 转移性结直肠癌 生物信息学 核心基因 metastasis colorectal cancer bioinformatics HUB gene
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