摘要
目的 开发并验证一种基于坏死性凋亡相关的IncRNA预后模型，旨在探讨坏死性凋亡相关lncRNA在子宫内膜癌中的潜在预后价值及其与子宫内膜癌（EC）免疫微环境和治疗反应的关系。方法 从癌症基因组图谱（TCGA）（https://portal.gdc.cancer.gov）下载子宫内膜癌患者的转录组数据和临床数据。通过Pearson相关分析鉴定坏死性凋亡相关的lncRNA。采用单因素Cox和多因素Cox回归分析、LASSO回归构建坏死性凋亡相关IncRNA模型。采用Kaplan-Meier分析、受试者工作特征（ROC）曲线、列线图和校准曲线来验证该模型。最后进行了基因集富集分析（GSEA）、免疫分析和药物敏感性分析。结果 基于坏死性凋亡相关基因鉴定出377个在正常和子宫内膜癌样本之间差异表达的lncRNA。通过Cox回归分析和LASSO回归建立了由9种坏死性凋亡相关lncRNA（AL021578.1、CDKN2B-AS1、AL137003.1、UNQ6494、AC064801.1、AC027319.1、AC244517.7、LINC00942、AL031770.1）组成的预后风险模型。根据这一模型，子宫内膜癌患者被分为低风险组和高风险组。Cox 回归证实该特征为独立的预后预测因子，AUC值为0.729，用于预测患者1年的总生存期（OS）。然后建立临床特征（包括年龄和病理分级）的列线图及校准图，校准图用来验证列线图与模型中1年、3年和5年生存期的预测具有良好的一致性。此外，GSEA分析表明，与癌症相关的信号通路被富集在低危组；免疫分析显示，大多数免疫细胞、ESTIMAT 评分和免疫评分均与风险评分呈负相关，低风险组免疫检查点基因的表达更高。此外，低风险组患者对司美替尼、塞来昔布、来他替尼、阿糖胞苷、二甲双胍等药物的敏感性更高。结论 基于9种坏死性凋亡相关的lncRNA，建立了一个可以用来预测子宫内膜癌患者的预后、肿瘤免疫微环境及治疗反应的模型。靶向这些lncRNA将是全身治疗失败的另外一种新方法。因此，IncRNA、坏死性凋亡和子宫内膜癌之间的分子机制值得研究。
关键词： 子宫内膜癌；坏死性凋亡；长非编码RNA；预后模型；生物信息学
Abstract
Objetive To develop and validate a prognostic model based on necroptosis-associated IncRNA, aiming to explore the potential prognostic value of necroptosis-associated lncRNA in endometrial cancer and its relationship with the immune microenvironment and treatment response in endometrial cancer (EC). Methods Transcriptomic and clinical data of endometrial cancer patients were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov). Necroptosis-associated lncRNAs were identified by Pearson correlation analysis. one-way Cox and multi-way Cox regression analysis, and LASSO regression were used to construct necroptosis-associated IncRNA models. Kaplan-Meier analysis, subject operating characteristic (ROC) curves, column line plots and calibration curves were used to validate the model. Finally, gene set enrichment analysis (GSEA), immunoassay and drug sensitivity analysis were performed. Results 377 lncRNAs differentially expressed between normal and endometrial cancer samples were identified based on necroptosis-associated genes. lncRNAs were established by Cox regression analysis and LASSO regression consisting of nine necroptosis-associated lncRNAs (AL021578.1, CDKN2B-AS1, AL137003.1, UNQ6494 AC064801.1, AC027319.1, AC244517.7, LINC00942, AL031770.1) as a prognostic risk model. According to this model, patients with endometrial cancer were divided into low- and high-risk groups. cox regression confirmed this feature as an independent prognostic predictor with an AUC value of 0.729 for predicting patients' overall survival (OS) at 1 year. Columnar plots of clinical characteristics (including age and pathological grading) and calibration plots were then created, and calibration plots were used to verify that the columnar plots were in good agreement with the predictions of 1-year, 3-year, and 5-year survival in the model. In addition, GSEA analysis showed that signaling pathways associated with cancer were enriched in the low-risk group; immune analysis showed that most immune cells, ESTIMAT score and immune score were negatively correlated with risk score, and the expression of immune checkpoint genes was higher in the low-risk group. In addition, patients in the low-risk group were more sensitive to drugs such as semitinib, celecoxib, leptatinib, cytarabine, and metformin. Conclusion s Based on nine necroptosis-associated lncRNAs, a model was developed that can be used to predict prognosis, tumor immune microenvironment, and treatment response in patients with endometrial cancer. Targeting these lncRNAs would be an additional novel approach to systemic treatment failure. Therefore, the molecular mechanisms between IncRNAs, necroptosis and endometrial cancer deserve to be investigated.
Key words： Endometrial cancer; necroptosis; long non-coding RNA; prognostic model; bioinformatics
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