Identification of EMT-Related Gene Signatures to Predict the Prognosis of Patients With Endometrial Cancer.
Identification of EMT-Related Gene Signatures to Predict the Prognosis of Patients With Endometrial Cancer.
复制标题
鉴定 EMT 相关基因特征以预测子宫内膜癌患者的预后。
DOI：
10.3389/fgene.2020.582274
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发表时间：
2020
影响因子：
3.7
通讯作者：
Xiao Q
中科院分区：
文献类型：
--
作者：
Cai L;Hu C;Yu S;Liu L;Zhao J;Zhao Y;Lin F;Du X;Yu Q;Xiao Q
子宫内膜癌是最常见的妇科肿瘤之一。上皮-间质转化（Epithelial-mesenchymal transition， EMT）被认为与肿瘤的恶性进展密切相关。然而，emt相关基因（ERG）特征与EC患者预后的关系尚无相关研究。我们从Cancer Genome Atlas数据库中提取了543例肿瘤组织和23例正常组织的mRNA表达谱。然后，我们从这些mrna中选择差异表达的ERGs （DEERGs）。然后分别进行单因素和多因素Cox回归分析，筛选对EC患者预后有预测能力的ERGs。此外，根据选择的基因构建风险评分模型，预测患者的总生存期（OS）、无进展生存期（PFS）和无病生存期（DFS）。最后，构建形态学图估计EC患者的OS和PFS，并进行泛癌分析进一步分析某一基因的功能。获得6例OS-， 10例PFS-和5例dfs -相关的ERGs。通过构建预后风险评分模型，我们发现高危组的OS、PFS和DFS明显较差。最后，我们发现AQP5在这三个基因签名中均有出现，并且通过泛癌分析，还发现它在低级别胶质瘤（LGG）的免疫中发挥重要作用，这可能是导致LGG患者预后不良的原因之一。我们利用生物信息学方法构建ERG特征来预测EC患者的预后。我们的研究结果为深入了解EMT在EC患者中的作用，为个性化治疗提供新的靶点和思路，具有重要的临床意义。
Endometrial cancer (EC) is one of the most common gynecological cancers. Epithelial–mesenchymal transition (EMT) is believed to be significantly associated with the malignant progression of tumors. However, there is no relevant study on the relationship between EMT-related gene (ERG) signatures and the prognosis of EC patients. We extracted the mRNA expression profiles of 543 tumor and 23 normal tissues from The Cancer Genome Atlas database. Then, we selected differentially expressed ERGs (DEERGs) among these mRNAs. Next, univariate and multivariate Cox regression analyses were performed to select the ERGs with predictive ability for the prognosis of EC patients. In addition, risk score models were constructed based on the selected genes to predict patients’ overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS). Finally, nomograms were constructed to estimate the OS and PFS of EC patients, and pan-cancer analysis was performed to further analyze the functions of a certain gene. Six OS-, ten PFS-, and five DFS-related ERGs were obtained. By constructing the prognostic risk score model, we found that the OS, PFS, and DFS of the high-risk group were notably poorer. Last, we found that AQP5 appeared in all three gene signatures, and through pan-cancer analysis, it was also found to play an important role in immunity in lower grade glioma (LGG), which may contribute to the poor prognosis of LGG patients. We constructed ERG signatures to predict the prognosis of EC patients using bioinformatics methods. Our findings provide a thorough understanding of the effect of EMT in patients with EC and provide new targets and ideas for individualized treatment, which has important clinical significance.