Colorectal cancer (CRC) is a kind of malignant cancer with high morbidity and mortality. The purpose of this study was to explore potential regulated key genes involved in CRC through bioinformatics analysis and experimental verification. The gene expression profile data were downloaded from the Gene Expression Omnibus, and the differential expression genes were detected in cancerous and paracancerous samples of CRC patients, respectively. Then functional enrichment analysis, such as the Kyoto Encyclopedia of Genes and Genomes pathway analysis as well as the protein-protein interaction network were constructed, and the highly related genes were clustered by Molecular COmplex DEtection algorithm to find out the core interaction in different genes' crosstalk. The genes affecting CRC prognosis were screened by the Human Protein Atlas database. In addition, the expression level of core genes was detected by GEPIA database, and the core genes' changes in large-scale cancer genome data set were directly analyzed by cBioPortal database. The expression of the predicted hub genes DSN1, AHCY, and ERCC6L was verified by reverse-transcription quantitative polymerase chain reaction in CRC cells. The gene function of DSN1 was analyzed by wound healing and colony formation assays. The results showed that silencing of DSN1 could significantly reduce the migration and proliferation of CRC cells. Further, BUB1B, the potential interacting protein of DSN1, was also predicted via bioinformatics analysis. Above all, this study shows that bioinformatics analysis combined with experimental method verification provide more potential vital genes for the prevention and therapy of CRC.

中文翻译：

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通过生物信息学分析与结直肠癌的实验验证相结合，识别潜在的中心基因。

大肠癌（CRC）是一种高发病率和高死亡率的恶性肿瘤。这项研究的目的是通过生物信息学分析和实验验证来探索参与CRC的潜在调控关键基因。从Gene Expression Omnibus下载基因表达谱数据，并分别在CRC患者的癌性和癌旁样本中检测到差异表达基因。然后构建功能丰富的分析方法，如《京都议定书》的基因百科全书，基因组途径分析以及蛋白质-蛋白质相互作用网络，并通过分子复合检测算法将高度相关的基因聚类，以找出不同基因的核心相互作用。相声。通过人蛋白质图谱数据库筛选了影响CRC预后的基因。另外，通过GEPIA数据库检测核心基因的表达水平，并通过cBioPortal数据库直接分析大规模癌症基因组数据集中核心基因的变化。通过反转录定量聚合酶链反应在CRC细胞中验证了预测的枢纽基因DSN1，AHCY和ERCC6L的表达。通过伤口愈合和集落形成分析来分析DSN1的基因功能。结果表明，沉默DSN1可以显着降低CRC细胞的迁移和增殖。此外，还通过生物信息学分析预测了DSN1的潜在相互作用蛋白BUB1B。最重要的是，这项研究表明，生物信息学分析与实验方法验证相结合，为预防和治疗CRC提供了更多潜在的重要基因。通过GEPIA数据库检测核心基因的表达水平，并通过cBioPortal数据库直接分析大规模癌症基因组数据集中核心基因的变化。通过反转录定量聚合酶链反应在CRC细胞中验证了预测的枢纽基因DSN1，AHCY和ERCC6L的表达。通过伤口愈合和集落形成分析来分析DSN1的基因功能。结果表明，沉默DSN1可以显着降低CRC细胞的迁移和增殖。此外，还通过生物信息学分析预测了DSN1的潜在相互作用蛋白BUB1B。最重要的是，这项研究表明，生物信息学分析与实验方法验证相结合，为CRC的预防和治疗提供了更多潜在的重要基因。通过GEPIA数据库检测核心基因的表达水平，并通过cBioPortal数据库直接分析大规模癌症基因组数据集中核心基因的变化。通过反转录定量聚合酶链反应在CRC细胞中验证了预测的枢纽基因DSN1，AHCY和ERCC6L的表达。通过伤口愈合和集落形成试验分析DSN1的基因功能。结果表明，沉默DSN1可以显着降低CRC细胞的迁移和增殖。此外，还通过生物信息学分析预测了DSN1的潜在相互作用蛋白BUB1B。最重要的是，这项研究表明，生物信息学分析与实验方法验证相结合，为预防和治疗CRC提供了更多潜在的重要基因。