Liuzhou Maternity and Child Healthcare Hospital, Liuzhou 545001, China.
Affiliated Maternity Hospital and Affiliated Children's Hospital of Guangxi University of Science and Technology, Liuzhou 545001, China.
Exp Biol Med (Maywood). 2022 Feb;247(3):221-236. doi: 10.1177/15353702211053588. Epub 2021 Oct 27.
Uterine corpus endometrial carcinoma (UCEC) is the third most frequent gynecological malignancies in the female reproductive system. Long non-coding RNAs (lncRNAs) are closely involved in tumor progression. This study aimed to develop an immune subtyping system and a prognostic model based on lncRNAs for UCEC. Paired lncRNAs and non-negative matrix factorization were applied to identify immune subtypes. Enrichment analysis was conducted to assess functional pathways, immune-related genes, and cells. Univariate and multivariate Cox regression analysis were performed to analyze the relation between lncRNAs and overall survival (OS). A prognostic model was constructed and optimized by least absolute shrinkage and selection operator (LASSO) and Akaike information criterion (AIC). Two immune subtypes (C1 and C2) and four paired-prognostic lncRNAs closely associated with overall survival were identified. Some immune features, sensitivity of chemotherapy and immunotherapy, and the relation with immune escape showed variations between two subtypes. A nomogram established based on prognostic model and clinical features was effective in OS prediction. The immune subtyping system based on lncRNAs and the four-paired-lncRNA signature was predictive of UCEC prognosis and can facilitate personalized therapies such as immunotherapy or RNA-based therapy for UCEC patients.
子宫内膜癌（Uterine corpus endometrial carcinoma，UCEC）是女性生殖系统中第三大常见的妇科恶性肿瘤。长链非编码 RNA（Long non-coding RNAs，lncRNAs）与肿瘤的进展密切相关。本研究旨在建立基于 lncRNAs 的免疫亚分型系统和预后模型，用于 UCEC。使用配对的 lncRNAs 和非负矩阵分解（Non-negative matrix factorization）来识别免疫亚型。通过富集分析来评估功能途径、免疫相关基因和细胞。使用单因素和多因素 Cox 回归分析来分析 lncRNAs 与总生存期（Overall survival，OS）之间的关系。通过最小绝对值收缩和选择算子（Least absolute shrinkage and selection operator，LASSO）和赤池信息量准则（Akaike information criterion，AIC）构建和优化预后模型。鉴定了两个与总生存期密切相关的免疫亚型（C1 和 C2）和四个配对的预后 lncRNAs。两种亚型之间存在一些免疫特征、化疗和免疫治疗的敏感性以及与免疫逃逸的关系的差异。基于预后模型和临床特征建立的列线图可有效预测 OS。基于 lncRNAs 和四个配对 lncRNA 特征的免疫亚分型系统可预测 UCEC 的预后，并可促进针对 UCEC 患者的免疫治疗或基于 RNA 的治疗等个性化治疗。
