Chen Weigang, Gao Chang, Liu Yong, Wen Ying, Hong Xiaoling, Huang Zunnan
Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Research Platform Service Management Center, Guangdong Medical University, Dongguan, China.
Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Guangdong Medical University, Dongguan, China.
Front Genet. 2020 Jun 9;11:478. doi: 10.3389/fgene.2020.00478. eCollection 2020.
This study aims to lay a foundation for studying the regulation of microRNAs (miRNAs) in colon cancer by applying bioinformatics methods to identify miRNAs and their potential critical target genes associated with colon cancer and prognosis. Data of differentially expressed miRNAs (DEMs) and genes (DEGs) downloaded from two independent databases (TCGA and GEO) and analyzed by R software resulted in 472 DEMs and 565 DEGs in colon cancers, respectively. Next, we developed an 8-miRNA (hsa-mir-6854, hsa-mir-4437, hsa-mir-216a, hsa-mir-3677, hsa-mir-887, hsa-mir-4999, hsa-mir-34b, and hsa-mir-3189) prognostic signature for patients with colon cancer by Cox proportional hazards regression analysis. To predict the target genes of these miRNAs, we used TargetScan and miRDB. The intersection of DEGs with the target genes predicted for these eight miRNAs retrieved 112 consensus genes. GO and KEGG pathway enrichment analyses showed these 112 genes were mainly involved in protein binding, one-carbon metabolic process, nitrogen metabolism, proteoglycans in cancer, and chemokine signaling pathways. The protein-protein interaction network of the consensus genes, constructed using the STRING database and imported into Cytoscape, identified 14 critical genes in the pathogenesis of colon cancer (, , , , , , , , , , , , , and ). Finally, we verified the critical genes by weighted gene co-expression network analysis (WGCNA) of the GEO data, and further mined the core genes involved in colon cancer. In summary, this study identified an 8-miRNA model that can effectively predict the prognosis of colon cancer patients and 14 critical genes with vital roles in colon cancer carcinogenesis. Our findings contribute new ideas for elucidating the molecular mechanisms of colon cancer carcinogenesis and provide new therapeutic targets and biomarkers for future treatment and prognosis.
本研究旨在通过应用生物信息学方法来鉴定与结肠癌及预后相关的微小RNA（miRNA）及其潜在的关键靶基因，从而为研究miRNA在结肠癌中的调控奠定基础。从两个独立数据库（TCGA和GEO）下载的差异表达miRNA（DEM）和基因（DEG）数据经R软件分析后，分别在结肠癌中得到了472个DEM和565个DEG。接下来，我们通过Cox比例风险回归分析，为结肠癌患者开发了一个包含8个miRNA（hsa-mir-6854、hsa-mir-4437、hsa-mir-216a、hsa-mir-3677、hsa-mir-887、hsa-mir-4999、hsa-mir-34b和hsa-mir-3189）的预后特征。为了预测这些miRNA的靶基因，我们使用了TargetScan和miRDB。将DEG与为这8个miRNA预测的靶基因进行交集分析，得到了112个共有基因。基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析表明，这112个基因主要参与蛋白质结合、一碳代谢过程、氮代谢、癌症中的蛋白聚糖以及趋化因子信号通路。使用STRING数据库构建并导入Cytoscape的共有基因的蛋白质-蛋白质相互作用网络，确定了14个在结肠癌发病机制中起关键作用的基因（此处原文未列出具体基因名称）。最后，我们通过对GEO数据进行加权基因共表达网络分析（WGCNA）验证了这些关键基因，并进一步挖掘了参与结肠癌的核心基因。总之，本研究鉴定出一个能够有效预测结肠癌患者预后的8-miRNA模型以及14个在结肠癌致癌过程中起重要作用的关键基因。我们的研究结果为阐明结肠癌致癌的分子机制提供了新思路，并为未来的治疗和预后提供了新的治疗靶点和生物标志物。
