Xia Leilei, Su Xiaoling, Shen Jizi, Meng Qi, Yan Jiuqiong, Zhang Caihong, Chen Yu, Wang Han, Xu Mingjuan
Department of Obstetrics and Gynecology, Changhai Hospital, Second Military Medical University, Shanghai, People's Republic of China.
Department of Obstetrics and Gynecology, No. 455 Hospital, Shanghai, People's Republic of China.
Cancer Manag Res. 2018 Apr 5;10:663-670. doi: 10.2147/CMAR.S162813. eCollection 2018.
Cervical cancer, one of the leading causes of female deaths, remains a top cause of mortality in gynecologic oncology and tends to affect younger individuals. However, the pathogenesis of cervical cancer is still far from clear. Given the high incidence and mortality of cervical cancer, uncovering the causes and pathogenesis as well as identifying novel biomarkers are of great significance and are desperately needed.
First, raw data were downloaded from the Gene Expression Omnibus database. The Robuse Multi-Array Average algorithm and combat function of the sva package were subsequently applied to preprocess and remove batch effects. Differentially expressed genes (DEGs) analyzed with the limma package were followed by gene ontology and pathway analysis, and a protein-protein interaction (PPI) network based on the STRING website and the Cytoscape software was constructed. Weighted Correlation Network Analysis (WGCNA) was utilized to build the coexpression network. Subsequently, UALCAN websites were employed to conduct survival analysis. Finally, the oncomine database was used to validate the expression of ANLN in other datasets.
GSE29570 and GSE89657, including 49 cervical cancer tissues and 20 normal cervical tissues, were screened as the datasets. Three-hundred-twenty-four DEGs were identified and, among them, 123 were upregulated, while 201 were downregulated. The DEGs PPI network complex, contained 305 nodes and 4,962 edges, and 8 clusters were calculated according to k-core =2. Among them, cluster 1, which had 65 nodes and 1,780 edges, had the highest score in these clusters. In coexpression analysis, there were 86 hubgenes from the Brown modules that were chosen for further analysis. Sixty-one key genes were identified as the intersecting genes of the Brown module of WGCNA and DEGs. In survival analysis, only ANLN was a prognostic factor, and the survival was significantly better in the low-expression ANLN group.
Our study suggested that ANLN may be a potential tumor oncogene and could serve as a biomarker for predicting the prognosis of cervical cancer patients.
宫颈癌是女性死亡的主要原因之一，仍然是妇科肿瘤学中导致死亡的首要原因，且往往影响较年轻的个体。然而，宫颈癌的发病机制仍远未明确。鉴于宫颈癌的高发病率和死亡率，揭示其病因和发病机制以及鉴定新的生物标志物具有重要意义且迫切需要。
首先，从基因表达综合数据库下载原始数据。随后应用Robuse多阵列平均算法和sva包的战斗函数进行预处理并消除批次效应。使用limma包分析差异表达基因（DEG），接着进行基因本体论和通路分析，并基于STRING网站和Cytoscape软件构建蛋白质-蛋白质相互作用（PPI）网络。利用加权相关网络分析（WGCNA）构建共表达网络。随后，使用UALCAN网站进行生存分析。最后，使用肿瘤在线数据库验证ANLN在其他数据集中的表达。
筛选出GSE29570和GSE89657数据集，包括49例宫颈癌组织和20例正常宫颈组织。鉴定出324个DEG，其中123个上调，201个下调。DEG的PPI网络复合体包含305个节点和4962条边，根据k核=2计算出8个簇。其中，簇1有65个节点和1780条边，在这些簇中得分最高。在共表达分析中，从棕色模块中选择86个枢纽基因进行进一步分析。61个关键基因被鉴定为WGCNA棕色模块和DEG的交集基因。在生存分析中，只有ANLN是一个预后因素，ANLN低表达组的生存率明显更好。
我们的研究表明，ANLN可能是一种潜在的肿瘤癌基因，可作为预测宫颈癌患者预后的生物标志物。
