结肠癌分子机制评估：一种系统生物学方法。
Assessment of colon cancer molecular mechanism: a system biology approach.
作者信息
Arjmand Babak, Khodadoost Mahmood, Jahani Sherafat Somayeh, Rezaei Tavirani Mostafa, Ahmadi Nayebali, Hamzeloo Moghadam Maryam, Rezaei Tavirani Sina, Khanabadi Binazir, Iranshahi Majid
机构信息
Cell Therapy and Regenerative Medicine Research Center, Endocrinology and Metabolism Molecular-Cellular Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
School of Traditional Medicine Shahid, Beheshti University of Medical Sciences, Tehran, Iran.
出版信息
Gastroenterol Hepatol Bed Bench. 2021 Fall;14(Suppl1):S51-S57.
AIM
The current study aimed to assess and compare colon cancer dysregulated genes from the GEO and STRING databases.
BACKGROUND
Colorectal cancer is known as the third most common kind of cancer and the second most important reason for global cancer-related mortality rates. There have been many studies on the molecular mechanism of colon cancer.
METHODS
From the STRING database, 100 differentially expressed proteins related to colon cancers were retrieved and analyzed by network analysis. The central nodes of the network were assessed by gene ontology. The findings were compared with a GSE from GEO.
RESULTS
Based on data from the STRING database, TP53, EGFR, HRAS, MYC, AKT1, GAPDH, KRAS, ERBB2, PTEN, and VEGFA were identified as central genes. The central nodes were not included in the significant DEGs of the analyzed GSE.
CONCLUSION
A combination of different database sources in system biology investigations provides useful information about the studied diseases.
目的
本研究旨在评估和比较来自基因表达综合数据库（GEO）和搜索工具检索基因邻接关系数据库（STRING）的结肠癌失调基因。
背景
结直肠癌是第三大常见癌症，也是全球癌症相关死亡率的第二大重要原因。关于结肠癌的分子机制已有许多研究。
方法
从STRING数据库中检索出100种与结肠癌相关的差异表达蛋白，并通过网络分析进行分析。通过基因本体论评估网络的中心节点。将研究结果与来自GEO的一个基因表达数据集（GSE）进行比较。
结果
基于STRING数据库的数据，TP53、表皮生长因子受体（EGFR）、哈维鼠肉瘤病毒癌基因同源物（HRAS）、原癌基因MYC、蛋白激酶B1（AKT1）、甘油醛-3-磷酸脱氢酶（GAPDH）、 Kirsten大鼠肉瘤病毒癌基因同源物（KRAS）、原癌基因ERBB2、第10号染色体缺失的磷酸酶及张力蛋白同源物（PTEN）和血管内皮生长因子A（VEGFA）被确定为中心基因。这些中心节点未包含在所分析的GSE的显著差异表达基因中。
结论
在系统生物学研究中结合不同的数据库来源可为所研究的疾病提供有用信息。
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