table1_Mining TCGA Data for Key Biomarkers Related to Immune Microenvironment in Endometrial cancer by Immune Score and Weighted Correlation Network Analysis.docx

Background: Endometrial cancer (EC) is one of the most lethal gynecological cancers around the world. The aim of this study is to identify the potential immune microenvironment-related biomarkers associated with the prognosis for EC.Methods: RNA-seq data and clinical information of EC patients were derived from The Cancer Genome Atlas (TCGA). The immune score of each EC sample was obtained by ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) was used to identify the interesting module and potential key genes concerning the immune score. The expression patterns of the key genes were then verified via the GEPIA database. Finally, CIBERSORT was applied to evaluate the relative abundances of 22 immune cell types in EC.Results: Immune scores were significantly associated with tumor grade and histology of EC, and high immune scores may exert a protective influence on the survival outcome for EC. WGCNA indicated that the black module was significantly correlated with the immune score. Function analysis revealed it mainly involved in those terms related to immune regulation and inflammatory response. Moreover, 11 key genes (APOL3, C10orf54, CLEC2B, GIMAP1, GIMAP4, GIMAP6, GIMAP7, GIMAP8, GYPC, IFFO1, TAGAP) were identified from the black module, validated by the GEPIA database, and revealed strong correlations with infiltration levels of multiple immune cell types, as was the prognosis of EC.Conclusion: In this study, 11 key genes showed abnormal expressions and strong correlations with immune infiltration in EC, most of which were significantly associated with the prognosis of EC. These findings made them promising therapeutic targets for the treatment of EC.

背景：子宫内膜癌（EC）是全球最为致命的妇科恶性肿瘤之一。本研究旨在识别与子宫内膜癌预后相关的潜在免疫微环境相关生物标志物。方法：本研究从癌症基因组图谱（TCGA）获取了子宫内膜癌患者的RNA测序数据和临床信息。通过ESTIMATE算法计算了每个子宫内膜癌样本的免疫评分。采用加权基因共表达网络分析（WGCNA）识别与免疫评分相关的有趣模块和潜在关键基因。通过GEPIA数据库验证了关键基因的表达模式。最后，应用CIBERSORT评估了22种免疫细胞类型在子宫内膜癌中的相对丰度。结果：免疫评分与子宫内膜癌的肿瘤分级和组织学显著相关，高免疫评分可能对子宫内膜癌的生存结果产生保护性影响。WGCNA分析表明，黑色模块与免疫评分显著相关。功能分析揭示了其主要涉及与免疫调节和炎症反应相关的术语。此外，从黑色模块中鉴定出11个关键基因（APOL3、C10orf54、CLEC2B、GIMAP1、GIMAP4、GIMAP6、GIMAP7、GIMAP8、GYPC、IFFO1、TAGAP），这些基因经GEPIA数据库验证，与多种免疫细胞浸润水平及子宫内膜癌的预后存在强烈相关性。结论：在本研究中，11个关键基因表现出异常表达，且与子宫内膜癌的免疫浸润密切相关，其中大部分与子宫内膜癌的预后显著相关。这些发现使其成为子宫内膜癌治疗的潜在治疗靶点。