Ye Lei, Wang Xiaojun, Li Bilan
Department of Gynecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, China.
J Cancer. 2021 Sep 3;12(21):6484-6496. doi: 10.7150/jca.62729. eCollection 2021.
Epithelial-mesenchymal transition (EMT) is regulated by inducible factors, transcription factors, and a series of genes involved in diverse signaling pathways, which are correlated with tumor invasion and progression. In the present study, we analyzed the expression profile data of 1169 EMT-related genes in endometrial cancer (EC) from the Cancer Genome Atlas (TCGA) dataset, and performed consistency clustering to divide EC samples into two subgroups based on overall survival. The genes differentially expressed between the two subtypes included EMT-related genes. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) were applied to construct a prognostic model based on the 44 genes signature. Five genes (L1CAM, PRKCI, ESR1, CDKN2A, and VIM) were finally included to establish a formula for prognostic risk score. The low-risk group showed significantly better prognosis compared with the high-risk group in the TCGA dataset. In addition, the risk-scoring model successfully predicted prognosis in an external GEO dataset (GSE102073). The relationship between ERα and vimentin levels was confirmed through immunohistochemistry. In conclusion, these data indicate that the expression profile of EMT-related genes could predict prognosis in EC.
上皮-间质转化（EMT）受诱导因子、转录因子以及一系列参与多种信号通路的基因调控，这些与肿瘤侵袭和进展相关。在本研究中，我们分析了来自癌症基因组图谱（TCGA）数据集的1169个子宫内膜癌（EC）中EMT相关基因的表达谱数据，并进行一致性聚类，根据总生存期将EC样本分为两个亚组。两个亚型之间差异表达的基因包括EMT相关基因。应用单变量Cox分析和最小绝对收缩和选择算子（LASSO）基于44个基因特征构建预后模型。最终纳入五个基因（L1CAM、PRKCI、ESR1、CDKN2A和VIM）以建立预后风险评分公式。在TCGA数据集中，低风险组的预后明显优于高风险组。此外，风险评分模型在外部GEO数据集（GSE102073）中成功预测了预后。通过免疫组织化学证实了雌激素受体α（ERα）与波形蛋白水平之间的关系。总之，这些数据表明EMT相关基因的表达谱可以预测EC的预后。
