Bu Fanqin, Zhu Xiaojian, Zhu Jinfeng, Liu Zitao, Wu Ting, Luo Chen, Lin Kang, Huang Jun
Department of Gastrointestinal Surgery, Second Affiliated Hospital of Nanchang University, Nanchang 330006, Jiangxi Province, People's Republic of China.
Jiangxi Medical College of Nanchang University, Nanchang, People's Republic of China.
Cancer Manag Res. 2020 Nov 17;12:11677-11687. doi: 10.2147/CMAR.S279165. eCollection 2020.
Colorectal cancer (CRC) is one of the most lethal malignancies and the incidence of CRC has been on the rise. Herein, we aimed to identify effective biomarkers for early diagnosis and treatment of colorectal cancer via bioinformatic tools.
To identify differentially expressed genes (DEGs) in CRC, we downloaded CRC gene expression data from GSE24514 and GSE110223 datasets in Gene Expression Omnibus (GEO) and employed R to analyze the data. We further performed functional enrichment analysis of the DEGs on the DAVID gene ontology analysis tool. STRING database and Cytoscape visualization tool were employed to construct a PPI (protein-protein interaction) network and establish intensive intervals in the network. Immunohistochemistry, qRT-PCR and Western blotting were performed to identify the expression level of in CRC. In vitro and in vivo experiments were performed to assess the impact of in the pathogenesis of CRC in terms of proliferation, migration and metastasis.
Among the two datasets, 389 DEGs were identified and used to construct a PPI network. These genes were mainly involved in cell proliferation and cell cycle. Among them, 15 genes including were found to be strongly associated with the PPI network. We further performed immunohistochemistry, qRT-PCR and Western blotting to identify that expression was higher in CRC than in paired normal tissues. Moreover, in vitro and in vivo experiments demonstrated could promote the proliferation, invasion and migration of colorectal cancer cells.
could be considered as a potential biomarker for CRC patients.
结直肠癌（CRC）是最致命的恶性肿瘤之一，且其发病率一直在上升。在此，我们旨在通过生物信息学工具鉴定用于结直肠癌早期诊断和治疗的有效生物标志物。
为了鉴定结直肠癌中差异表达基因（DEGs），我们从基因表达综合数据库（GEO）中的GSE24514和GSE110223数据集中下载了结直肠癌基因表达数据，并使用R软件分析这些数据。我们进一步在DAVID基因本体分析工具上对DEGs进行功能富集分析。利用STRING数据库和Cytoscape可视化工具构建蛋白质-蛋白质相互作用（PPI）网络并确定网络中的紧密区间。进行免疫组织化学、qRT-PCR和蛋白质印迹法以鉴定[此处原文缺失具体基因名称]在结直肠癌中的表达水平。进行体外和体内实验以评估[此处原文缺失具体基因名称]在结直肠癌发病机制中对增殖、迁移和转移方面的影响。
在这两个数据集中，鉴定出389个DEGs并用于构建PPI网络。这些基因主要参与细胞增殖和细胞周期。其中，包括[此处原文缺失具体基因名称]在内的15个基因被发现与PPI网络密切相关。我们进一步进行免疫组织化学、qRT-PCR和蛋白质印迹法以鉴定[此处原文缺失具体基因名称]在结直肠癌中的表达高于配对的正常组织。此外，体外和体内实验表明[此处原文缺失具体基因名称]可促进结直肠癌细胞的增殖、侵袭和迁移。
[此处原文缺失具体基因名称]可被视为结直肠癌患者的潜在生物标志物。
