Background Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized. Methods GSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in Cytoscape. The selected genes were coped with functional analysis. CMap could find EC-related drugs that might have potential effect. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient. Kaplan-Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict value of the genes. Mutation and survival analysis in TCGA database and UALCAN validation were completed. Immunohistochemistry staining from Human Protein Atlas database. GSEA, ROC curve analysis, Oncomine and qRT-PCR were also performed. Results Functional analysis showed that the upregulated DEGs were strikingly enriched in chemokine activity, and the down-regulated DEGs in glycosaminoglycan binding. PPI network suggested that NCAPG was the most relevant protein. CMap identified 10 small molecules as possible drugs to treat EC. Cox analysis showed that BCHE, MAL and ASPM were correlated with EC prognosis. TCGA dataset analysis showed significantly mutated BHCE positively related to EC prognosis. MAL and ASPM were further validated in UALCAN. All the results demonstrated that the two genes might promote EC progression. The profile of ASPM was confirmed by the results from immunohistochemistry. ROC curve demonstrated that the mRNA levels of two genes exhibited difference between normal and tumor tissues, indicating their diagnostic efficiency. qRT-PCR results supported the above results. Oncomine results showed that DNA copy number variation of MAL was significantly higher in different EC subtypes than in healthy tissues. GSEA suggested that the two genes played crucial roles in cell cycle. Conclusion BCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.

中文翻译：

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鉴定与子宫内膜癌进展和预后相关的分子标志物：生物信息学研究。

背景子宫内膜癌（EC）是一种女性癌症。生物信息学技术可以筛选出相关基因，使靶向治疗变得常规化。方法 GSE17025 从 GEO 下载。基因组数据和临床数据来自TCGA。R 软件和生物导体包用于识别 DEG。Clusterprofiler 用于功能分析。STRING 用于评估 PPI 信息和插件 MCODE 以筛选 Cytoscape 中的集线器模块。选择的基因进行功能分析。CMap 可以找到可能具有潜在影响的 EC 相关药物。进行单变量和多变量 Cox 比例风险回归分析以预测每位患者的风险。Kaplan-Meier曲线分析可以比较生存时间。进行ROC曲线分析以预测基因的价值。完成了 TCGA 数据库中的突变和生存分析以及 UALCAN 验证。来自人类蛋白质图谱数据库的免疫组织化学染色。还进行了 GSEA、ROC 曲线分析、Oncomine 和 qRT-PCR。结果功能分析表明，上调的DEGs显着富集趋化因子活性，下调的DEGs与糖胺聚糖结合。PPI 网络表明 NCAPG 是最相关的蛋白质。CMap 确定了 10 种小分子作为治疗 EC 的可能药物。Cox分析显示BCHE、MAL和ASPM与EC预后相关。TCGA 数据集分析显示显着突变的 BHCE 与 EC 预后呈正相关。MAL 和 ASPM 在 UALCAN 中得到进一步验证。所有结果表明，这两个基因可能促进 EC 进展。免疫组织化学结果证实了 ASPM 的概况。ROC曲线表明，两个基因的mRNA水平在正常组织和肿瘤组织之间表现出差异，表明它们的诊断效率。qRT-PCR结果支持上述结果。Oncomine 结果表明，不同 EC 亚型中 MAL 的 DNA 拷贝数变异显着高于健康组织。GSEA 表明这两个基因在细胞周期中起着至关重要的作用。结论 BCHE、MAL和ASPM是肿瘤相关基因，可作为EC治疗的潜在生物标志物。ROC曲线表明，两个基因的mRNA水平在正常组织和肿瘤组织之间表现出差异，表明它们的诊断效率。qRT-PCR结果支持上述结果。Oncomine 结果表明，不同 EC 亚型中 MAL 的 DNA 拷贝数变异显着高于健康组织。GSEA 表明这两个基因在细胞周期中起着至关重要的作用。结论 BCHE、MAL和ASPM是肿瘤相关基因，可作为EC治疗的潜在生物标志物。ROC曲线表明，两个基因的mRNA水平在正常组织和肿瘤组织之间表现出差异，表明它们的诊断效率。qRT-PCR结果支持上述结果。Oncomine 结果表明，不同 EC 亚型中 MAL 的 DNA 拷贝数变异显着高于健康组织。GSEA 表明这两个基因在细胞周期中起着至关重要的作用。结论 BCHE、MAL和ASPM是肿瘤相关基因，可作为EC治疗的潜在生物标志物。