Fan Yuan, Li Xingchen, Tian Li, Wang Jianliu
Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing, China.
Reproductive Medical Center, Peking University People's Hospital, Beijing, China.
Front Oncol. 2021 Mar 8;11:630905. doi: 10.3389/fonc.2021.630905. eCollection 2021.
Endometrial cancer (EC) is one of the most common gynecologic malignancies. The present study aims to identify a metabolism-related biosignature for EC and explore the molecular immune-related mechanisms underlying the tumorigenesis of EC.
Transcriptomics and clinical data of EC were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Common differentially expressed metabolism-related genes were extracted and a risk signature was identified by using the least absolute shrinkage and selection operator (LASSO) regression analysis method. A nomogram integrating the prognostic model and the clinicopathological characteristics was established and validated by a cohort of clinical EC patients. Furthermore, the immune and stromal scores were observed and the infiltration of immune cells in EC cells was analyzed.
Six genes, including CA3, HNMT, PHGDH, CD38, PSAT1, and GPI, were selected for the development of the risk prediction model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better overall survival (OS) (P = 7.874e-05). Then a nomogram was constructed and could accurately predict the OS (AUC = 0.827, 0.821, 0.845 at 3-, 5-, and 7-year of OS). External validation with clinical patients showed that patients with low risk scores had a longer OS (p = 0.04). Immune/stromal scores and infiltrating density of six types of immune cells were lower in high-risk group.
In summary, our work provided six potential metabolism-related biomarkers as well as a nomogram for the prognosis of EC patients, and explored the underlying mechanism involved in the progression of EC.
子宫内膜癌（EC）是最常见的妇科恶性肿瘤之一。本研究旨在确定EC的一种与代谢相关的生物标志物，并探讨EC肿瘤发生的分子免疫相关机制。
从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）中检索EC的转录组学和临床数据。提取常见的差异表达代谢相关基因，并使用最小绝对收缩和选择算子（LASSO）回归分析方法确定风险特征。建立了一个整合预后模型和临床病理特征的列线图，并通过一组临床EC患者进行验证。此外，观察免疫和基质评分，并分析免疫细胞在EC细胞中的浸润情况。
选择了包括CA3、HNMT、PHGDH、CD38、PSAT1和GPI在内的6个基因用于构建风险预测模型。Kaplan-Meier曲线表明，低风险组患者的总生存期（OS）明显更好（P = 7.874e-05）。然后构建了一个列线图，能够准确预测OS（OS 3年、5年和7年时的AUC分别为0.827、0.821、0.845）。对临床患者的外部验证表明，低风险评分的患者OS更长（p = 0.04）。高风险组的免疫/基质评分和6种免疫细胞的浸润密度较低。
总之，我们的工作提供了6种潜在的与代谢相关的生物标志物以及用于EC患者预后的列线图，并探讨了EC进展所涉及的潜在机制。
