Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis
Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis
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DOI：
10.1002/mgg3.713
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发表时间：
2019-07-01
影响因子：
2
通讯作者：
Guo, Yuehua
中科院分区：
文献类型：
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作者：
Chen, Juan;Wang, Ziheng;Guo, Yuehua
结直肠癌（Colorectal cancer，CRC）是最常见的恶性肿瘤之一。在本研究中，下载了人类多阶段结直肠粘膜组织（包括健康、腺瘤和腺癌样本）的表达谱，以鉴定结直肠癌中的关键基因和潜在药物。方法利用生物信息学方法整合GSE 33113和GSE 44076的表达谱。用R语言分析差异表达基因（DEG）。使用注释、可视化和集成发现数据库（大卫）数据库进行DEG的功能富集分析。然后，利用检索相互作用基因的搜索工具（STRING）数据库和Cytoscape构建蛋白质-蛋白质相互作用（PPI）网络，并确定枢纽基因。随后，使用基因表达谱交互分析（GEPIA）在关键基因之间进行存活分析。使用连接图（CMap）查询CRC的潜在药物。结果共检测到428个基因在大肠癌组织中表达上调，751个基因表达下调。这些DEG的功能变化主要与细胞周期、卵母细胞减数分裂、DNA复制、p53信号通路和孕酮介导的卵母细胞成熟有关。一个PPI网络由STRING识别，有482个节点和2,368条边。生存分析显示，AURKA、CCNB 1、CCNF和EXO 1的高mRNA表达与较长的总生存期显著相关。此外，CMap预测了一组小分子作为可能的辅助药物来治疗CRC。结论本研究发现了与结直肠癌相关的关键失调基因及潜在的抗结直肠癌药物，为结直肠癌的预后评估提供了新的思路和潜在的生物标志物，同时也为结直肠癌的治疗提供了新的思路。
Background Colorectal cancer (CRC) is one of the most common malignant tumors. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma, and adenocarcinoma samples was downloaded to identify critical genes and potential drugs in CRC. Methods Expression profiles, GSE33113 and GSE44076, were integrated using bioinformatics methods. Differentially expressed genes (DEGs) were analyzed by R language. Functional enrichment analyses of the DEGs were performed using the Database for Annotation, visualization, and integrated discovery (DAVID) database. Then, the search tool for the retrieval of interacting genes (STRING) database and Cytoscape were used to construct a protein-protein interaction (PPI) network and identify hub genes. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis (GEPIA). Connectivity Map (CMap) was used to query potential drugs for CRC. Results A total of 428 upregulated genes and 751 downregulated genes in CRC were identified. The functional changes of these DEGs were mainly associated with cell cycle, oocyte meiosis, DNA replication, p53 signaling pathway, and progesterone-mediated oocyte maturation. A PPI network was identified by STRING with 482 nodes and 2,368 edges. Survival analysis revealed that high mRNA expression of AURKA, CCNB1, CCNF, and EXO1 was significantly associated with longer overall survival. Moreover, CMap predicted a panel of small molecules as possible adjuvant drugs to treat CRC. Conclusion Our study found key dysregulated genes involved in CRC and potential drugs to combat it, which may provide novel insights and potential biomarkers for prognosis, as well as providing new CRC treatments.