子宫内膜癌（EC）是最常见的妇科恶性肿瘤之一，需要进一步分类以进行治疗和预后。长链非编码RNA (lncRNA) 和免疫原性细胞死亡(ICD) 在肿瘤进展中发挥着关键作用。然而，lncRNA 在 EC 的 ICD 中的作用仍不清楚。本研究旨在通过生物信息学探讨ICD相关lncRNA在EC中的作用，并建立基于ICD相关lncRNA的预后风险模型。我们还探讨了跨预后组的免疫浸润和免疫细胞功能，并提出了治疗建议。从癌症基因组图谱（TCGA）数据库和加州大学圣克鲁斯分校（UCSC）中提取了总共 552 个 EC 样本和 548 名 EC 患者的临床数据。分别是谢娜。使用最小绝对收缩和选择算子（LASSO）开发了与预后相关的特征和风险模型。通过共识聚类分析对亚型进行分类，并通过 t 分布随机邻域嵌入 (tSNE) 进行验证。进行卡普兰-迈耶分析以评估生存差异。通过单样本基因集富集分析（ssGSEA）、肿瘤免疫估计资源（TIMER）算法来估计免疫细胞的浸润。采用定量聚合酶链反应（qPCR）检测临床样本和细胞系中的lncRNA表达。在体外和体内进行了一系列研究，以检查 lncRNA 的敲低或过表达对 ICD 的影响。总共鉴定了 16 个具有预后价值的 ICD 相关 lncRNA。使用 SCARNA9、FAM198B-AS1、FKBP14-AS1、FBXO30-DT、LINC01943 和 AL161431.1 作为风险模型，评估了它们的预测准确性和辨别力。我们将 EC 患者分为高风险组和低风险组。分析表明，风险模型是一个独立的预后因素。高危组和低危组的预后不同，高危组的总生存期（OS）较低。低风险组的免疫细胞浸润和免疫评分较高。一致性聚类分析将样本分为4个亚型，其中第4个亚型具有较高的免疫细胞浸润和免疫评分。建立了由EC中6个ICD相关lncRNA组成的预后特征，基于该特征的风险模型可用于预测通过 EC.2024 转化癌症研究预测患者的预后。版权所有。

Endometrial carcinoma (EC) is one of the most prevalent gynecologic malignancies and requires further classification for treatment and prognosis. Long non-coding RNAs (lncRNAs) and immunogenic cell death (ICD) play a critical role in tumor progression. Nevertheless, the role of lncRNAs in ICD in EC remains unclear. This study aimed to explore the role of ICD related-lncRNAs in EC via bioinformatics and establish a prognostic risk model based on the ICD-related lncRNAs. We also explored immune infiltration and immune cell function across prognostic groups and made treatment recommendations.A total of 552 EC samples and clinical data of 548 EC patients were extracted from The Cancer Genome Atlas (TCGA) database and University of California Santa Cruz (UCSC) Xena, respectively. A prognostic-related feature and risk model was developed using the least absolute shrinkage and selection operator (LASSO). Subtypes were classified with consensus cluster analysis and validated with t-Distributed Stochastic Neighbor Embedding (tSNE). Kaplan-Meier analysis was conducted to assess differences in survival. Infiltration by immune cells was estimated by single sample gene set enrichment analysis (ssGSEA), Tumor IMmune Estimation Resource (TIMER) algorithm. Quantitative polymerase chain reaction (qPCR) was used to detect lncRNAs expression in clinical samples and cell lines. A series of studies was conducted in vitro and in vivo to examine the effects of knockdown or overexpression of lncRNAs on ICD.In total, 16 ICD-related lncRNAs with prognostic values were identified. Using SCARNA9, FAM198B-AS1, FKBP14-AS1, FBXO30-DT, LINC01943, and AL161431.1 as risk model, their predictive accuracy and discrimination were assessed. We divided EC patients into high-risk and low-risk groups. The analysis showed that the risk model was an independent prognostic factor. The prognosis of the high- and low-risk groups was different, and the overall survival (OS) of the high-risk group was lower. The low-risk group had higher immune cell infiltration and immune scores. Consensus clustering analysis divided the samples into four subtypes, of which cluster 4 had higher immune cell infiltration and immune scores.A prognostic signature composed of six ICD related-lncRNAs in EC was established, and a risk model based on this signature can be used to predict the prognosis of patients with EC.2024 Translational Cancer Research. All rights reserved.