Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
Cancer Biomark. 2020;27(2):213-223. doi: 10.3233/CBM-190533.
Cervical cancer (CC) is one kind of female cancer. With the development of bioinformatics, targeted specific biomarkers therapy has become much more valuable. GSE26511 was obtained from gene expression omnibus (GEO). We utilized a package called "WGCNA" to build co-expression network and choose the hub module. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein-protein interaction (PPI) information of those genes in the hub module. A Plug-in called MCODE was utilized to choose hub clusters of PPI network, which was visualized in Cytoscape. Clusterprofiler was used to do functional analysis. Univariate and multivariate cox proportional hazards regression analysis were both conducted to predict the risk score of CC patients. Kaplan-Meier curve analysis was done to show the overall survival. Receiver operating characteristic (ROC) curve analysis was utilized to evaluate the predictive value of the patient outcome. Validation of the hub gene in databases, Gene set enrichment analysis (GSEA) and GEPIA were completed. We built co-expression network based on GSE26511 and one CC-related module was identified. Functional analysis of this module showed that extracellular space and Signaling pathways regulating pluripotency of stem cells were most related pathways. PPI network screened GNG11 as the most valuable protein. Cox analysis showed that ACKR1 was negatively correlated with CC progression, which was validated in Gene Expression Profiling Interactive Analysis (GEPIA) and datasets. Survival analysis was performed and showed the consistent result. GSEA set enrichment analysis was also completed. This study showed hub functional terms and gene participated in CC and then speculated that ACKR1 might be tumor suppressor for CC.
宫颈癌（CC）是一种女性癌症。随着生物信息学的发展，靶向特定的生物标志物治疗变得更加有价值。GSE26511 从基因表达综合数据库（GEO）获得。我们利用一个名为“WGCNA”的软件包构建共表达网络，并选择枢纽模块。Search Tool for the Retrieval of Interacting Genes Database（STRING）用于分析枢纽模块中基因的蛋白质-蛋白质相互作用（PPI）信息。利用一个名为 MCODE 的插件选择 PPI 网络的枢纽簇，然后在 Cytoscape 中可视化。Clusterprofiler 用于进行功能分析。进行单变量和多变量 cox 比例风险回归分析，以预测 CC 患者的风险评分。Kaplan-Meier 曲线分析用于显示总生存率。利用Receiver operating characteristic（ROC）曲线分析评估患者预后的预测价值。在数据库中验证枢纽基因，完成基因集富集分析（GSEA）和 GEPIA。我们基于 GSE26511 构建共表达网络，确定了一个与 CC 相关的模块。该模块的功能分析表明，细胞外空间和调节干细胞多能性的信号通路是最相关的途径。PPI 网络筛选出 GNG11 作为最有价值的蛋白质。Cox 分析表明，ACKR1 与 CC 进展呈负相关，在 Gene Expression Profiling Interactive Analysis（GEPIA）和数据集上得到了验证。进行生存分析并得到了一致的结果。GSEA 集富集分析也完成了。本研究显示了枢纽功能术语和基因参与 CC，并推测 ACKR1 可能是 CC 的肿瘤抑制因子。
