Yu Lijun, Wei Meiyan, Li Fengyan
Department of Gynecology, First Hospital of Shanxi Medical University, Taiyuan, China.
Evol Bioinform Online. 2020 May 18;16:1176934320920574. doi: 10.1177/1176934320920574. eCollection 2020.
Despite advances in the treatment of cervical cancer (CC), the prognosis of patients with CC remains to be improved. This study aimed to explore candidate gene targets for CC. CC datasets were downloaded from the Gene Expression Omnibus database. Genes with similar expression trends in varying steps of CC development were clustered using Short Time-series Expression Miner (STEM) software. Gene functions were then analyzed using the Gene Ontology (GO) database and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein interactions among genes of interest were predicted, followed by drug-target genes and prognosis-associated genes. The expressions of the predicted genes were determined using real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting. Red and green profiles with upward and downward gene expressions, respectively, were screened using STEM software. Genes with increased expression were significantly enriched in DNA replication, cell-cycle-related biological processes, and the p53 signaling pathway. Based on the predicted results of the Drug-Gene Interaction database, 17 drug-gene interaction pairs, including 3 red profile genes (TOP2A, RRM2, and POLA1) and 16 drugs, were obtained. The Cancer Genome Atlas data analysis showed that high POLA1 expression was significantly correlated with prolonged survival, indicating that POLA1 is protective against CC. RT-qPCR and Western blotting showed that the expressions of TOP2A, RRM2, and POLA1 gradually increased in the multistep process of CC. TOP2A, RRM2, and POLA1 may be targets for the treatment of CC. However, many studies are needed to validate our findings.
尽管宫颈癌（CC）治疗取得了进展，但CC患者的预后仍有待改善。本研究旨在探索CC的候选基因靶点。从基因表达综合数据库下载CC数据集。使用短时序列表达挖掘器（STEM）软件对CC发展不同阶段具有相似表达趋势的基因进行聚类。然后使用基因本体论（GO）数据库和京都基因与基因组百科全书（KEGG）富集分析对基因功能进行分析。预测感兴趣基因之间的蛋白质相互作用，随后确定药物靶点基因和预后相关基因。使用实时定量聚合酶链反应（RT-qPCR）和蛋白质印迹法测定预测基因的表达。使用STEM软件筛选分别具有向上和向下基因表达的红色和绿色图谱。表达增加的基因在DNA复制、细胞周期相关生物学过程和p53信号通路中显著富集。基于药物-基因相互作用数据库的预测结果，获得了17对药物-基因相互作用对，包括3个红色图谱基因（TOP2A、RRM2和POLA1）和16种药物。癌症基因组图谱数据分析表明，POLA1高表达与生存期延长显著相关，表明POLA1对CC具有保护作用。RT-qPCR和蛋白质印迹法表明，TOP2A、RRM2和POLA1的表达在CC的多步骤过程中逐渐增加。TOP2A、RRM2和POLA1可能是CC治疗的靶点。然而，需要许多研究来验证我们的发现。
