He Lingling, He Wenjing, Luo Ji, Xu Minjuan
Department of Obstetrics and Gynecology, Ganzhou People's Hospital, Ganzhou, China.
Department of Obstetrics and Gynecology, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Front Cell Dev Biol. 2022 Dec 1;10:919637. doi: 10.3389/fcell.2022.919637. eCollection 2022.
A better knowledge of the molecular process behind uterine corpus endometrial carcinoma (UCEC) is important for prognosis prediction and the development of innovative targeted gene therapies. The purpose of this research is to discover critical genes associated with UCEC. We analyzed the gene expression profiles of TCGA-UCEC and GSE17025, respectively, using Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. From four sets of findings, a total of 95 overlapping genes were retrieved. On the 95 overlapping genes, KEGG pathway and GO enrichment analysis were conducted. Then, we mapped the PPI network of 95 overlapping genes using the STRING database. Twenty hub genes were evaluated using the Cytohubba plugin, including NR3C1, ATF3, KLF15, THRA, NR4A1, FOSB, PER3, HLF, NTRK3, EGR3, MAPK13, ARNTL2, PKM2, SCD, EIF5A, ADHFE1, RERGL, TUB, and ENC1. The expression levels of NR3C1, PKM2, and ENC1 were shown to be adversely linked with the survival time of UCEC patients using univariate Cox regression analysis and Kaplan-Meier survival calculation. ENC1 were also overexpressed in UCEC tumor tissues or cell lines, as shown by quantitative real-time PCR and Western blotting. Then we looked into it further and discovered that ENC1 expression was linked to tumor microenvironment and predicted various immunological checkpoints. In conclusion, our data indicate that ENC1 may be required for the development of UCEC and may serve as a future biomarker for diagnosis and therapy.
深入了解子宫内膜癌（UCEC）背后的分子过程对于预后预测和创新靶向基因治疗的发展至关重要。本研究的目的是发现与UCEC相关的关键基因。我们分别使用加权基因共表达网络分析（WGCNA）和差异基因表达分析，分析了TCGA-UCEC和GSE17025的基因表达谱。从四组研究结果中，共检索到95个重叠基因。对这95个重叠基因进行了KEGG通路和GO富集分析。然后，我们使用STRING数据库绘制了95个重叠基因的PPI网络。使用Cytohubba插件评估了20个枢纽基因，包括NR3C1、ATF3、KLF15、THRA、NR4A1、FOSB、PER3、HLF、NTRK3、EGR3、MAPK13、ARNTL2、PKM2、SCD、EIF5A、ADHFE1、RERGL、TUB和ENC1。单因素Cox回归分析和Kaplan-Meier生存计算显示，NR3C1、PKM2和ENC1的表达水平与UCEC患者的生存时间呈负相关。定量实时PCR和蛋白质印迹显示，ENC1在UCEC肿瘤组织或细胞系中也过表达。然后我们进一步研究发现，ENC1表达与肿瘤微环境相关，并预测了各种免疫检查点。总之，我们的数据表明，ENC1可能是UCEC发生发展所必需的，可能作为未来诊断和治疗的生物标志物。
