Xue Han, Sun Zhaojun, Wu Weiqing, Du Dong, Liao Shuping
Department of Health Management, Shenzhen People's Hospital, Shenzhen City, Guangdong Province, People's Republic of China.
Department of Dermatology, Shenzhen People's Hospital, Shenzhen City, GuangdongProvince, People's Republic of China.
Cancer Manag Res. 2021 Jan 8;13:117-131. doi: 10.2147/CMAR.S282989. eCollection 2021.
Cervical cancer belongs to one of the most common female cancers; yet, the exact underlying mechanisms are still elusive. Recently, microarray and sequencing technologies have been widely used for screening biomarkers and molecular mechanism discovery in cancer studies. In this study, we aimed to analyse the microarray datasets using comprehensive bioinformatics tools and identified novel biomarkers associated with the prognosis of patients with cervical cancer.
The differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets including GSE138080, GSE113942 and GSE63514 were analysed using GEO2R tool. The functional enrichment analysis was performed using g:Profiler tool. The protein-protein interaction (PPI) network construction and hub genes identification were performed using the STRING database and Cytoscape software, respectively. The hub genes were subjected to expression and survival analysis in the cervical cancer. The EdU incorporation and Cell Counting Kit-8 assays were performed to evaluate the effects of hub gene knockdown on the proliferation of cervical cancer cells.
A total of 89 overlapping DEGs (63 up-regulated and 26 down-regulated genes) were identified in the microarray datasets. The functional enrichment analysis indicated that the overlapping DEGs were mainly associated with "DNA replication" and "cell cycle". Furthermore, the PPI network analysis revealed that the network contains 87 nodes and 309 edges. Sub-module analysis using the Molecular Complex Detection tool identified 21 hub genes from the PPI network. The expression levels of the 21 hub genes were all up-regulated in the cervical cancer tissues when compared to normal cervical tissues as analysed by GEPIA tool. The survival analysis showed that the low expression of cell division cycle 45 (), GINS complex subunit 2 (), minichromosome maintenance complex component 2 () and proliferating cell nuclear antigen () was significantly correlated with the shorter overall survival of patients with cervical cancer. Moreover, the protein expression levels of and , but not were significantly up-regulated in the cervical cancer tissues when compared to normal cervical tissues. Finally, knockdown of significantly suppressed the proliferation of HeLa and SiHa cells.
In conclusion, we screened a total of 89 overlapping DEGs from the GEO datasets, and further analysis identified four hub genes ( and ) that were likely associated with the prognosis of patients with cervical cancer. knockdown repressed the cervical cancer cell proliferation. The current findings may provide novel insights into understanding the pathophysiology of cervical cancer and develop therapeutic targets for patients with cervical cancer.
宫颈癌是最常见的女性癌症之一；然而，其确切的潜在机制仍不清楚。最近，微阵列和测序技术已广泛用于癌症研究中的生物标志物筛选和分子机制发现。在本研究中，我们旨在使用综合生物信息学工具分析微阵列数据集，并鉴定与宫颈癌患者预后相关的新生物标志物。
使用GEO2R工具分析来自基因表达综合数据库（GEO）数据集（包括GSE138080、GSE113942和GSE63514）的差异表达基因（DEG）。使用g:Profiler工具进行功能富集分析。分别使用STRING数据库和Cytoscape软件构建蛋白质-蛋白质相互作用（PPI）网络并鉴定枢纽基因。对枢纽基因进行宫颈癌中的表达和生存分析。进行EdU掺入和细胞计数试剂盒-8测定以评估枢纽基因敲低对宫颈癌细胞增殖的影响。
在微阵列数据集中共鉴定出89个重叠的DEG（63个上调基因和26个下调基因）。功能富集分析表明，重叠的DEG主要与“DNA复制”和“细胞周期”相关。此外，PPI网络分析显示该网络包含87个节点和309条边。使用分子复合物检测工具进行的子模块分析从PPI网络中鉴定出21个枢纽基因。如通过GEPIA工具分析，与正常宫颈组织相比，这21个枢纽基因在宫颈癌组织中的表达水平均上调。生存分析表明，细胞分裂周期45（）、GINS复合物亚基2（）、微小染色体维持复合物组分2（）和增殖细胞核抗原（）的低表达与宫颈癌患者较短的总生存期显著相关。此外，与正常宫颈组织相比，表示为 和 的蛋白质表达水平在宫颈癌组织中显著上调，但表示为 的蛋白质表达水平未显著上调。最后，敲低 显著抑制了HeLa和SiHa细胞的增殖。
总之，我们从GEO数据集中筛选出总共89个重叠的DEG，进一步分析鉴定出四个可能与宫颈癌患者预后相关的枢纽基因（ 和 ）。敲低 和 可抑制宫颈癌细胞增殖。目前的研究结果可能为理解宫颈癌的病理生理学提供新的见解，并为宫颈癌患者开发治疗靶点。
