Hu Panwei, Wang Yongxiang, Chen Xiuhui, Zhao Lijie, Qi Cong, Jiang Guojing
Department of Gynaecology and Obstetrics, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Transl Cancer Res. 2023 Aug 31;12(8):1963-1979. doi: 10.21037/tcr-23-61. Epub 2023 Aug 28.
Uterine corpus endometrial carcinoma (UCEC) is a prevalent gynecologic malignant tumor with high recurrence and mortality rates. This study aimed to develop and validate a prognostic model for patients with UCEC based on cuproptosis-related long non-coding RNA (lncRNA) signature.
Transcriptome and clinical UCEC data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis was conducted to screen out the cuproptosis-related lncRNAs, and univariate regression analysis was performed to determine prognostic factors associated with overall survival (OS). A cuproptosis-related lncRNA risk model was constructed through least absolute shrinkage and selection operator (LASSO) regression and cross-validation. The accuracy and reliability of the model were verified through Kaplan-Meier (KM), proportional hazards model (Cox) regression, nomogram, principal component analysis (PCA), and stage analysis. Gene Ontology (GO) enrichment, immune function, and tumor mutation burden (TMB) analyses were conducted between low-risk and high-risk groups, and antineoplastic drugs were predicted.
By correlation analysis, 155 cuproptosis-related lncRNAs were acquired, and 9 lncRNAs were identified as independent prognostic factors. A 6-cuproptosis-related lncRNA model was established. The results revealed that patients in the high-risk group were more inclined to have a poor OS than those in the low-risk group. Risk score was an independent prognostic factor and had a high accuracy and predictive value. The extracellular structure and anchored components of membrane-related GO terms were significantly enriched. Immune function and TMB results were assumed to be different from each other, which might explain a better outcome in the low-risk group than that in the high-risk group. Eighteen compounds were predicted as chemotherapy drugs with high half maximal inhibitory concentration (IC50) in the high-risk group.
We successfully developed a cuproptosis-related lncRNA risk model for the prediction of prognosis, while simultaneously providing insights on new approaches for immunotherapy and chemotherapy for patients with UCEC.
子宫体子宫内膜癌（UCEC）是一种常见的妇科恶性肿瘤，复发率和死亡率较高。本研究旨在基于铜死亡相关长链非编码RNA（lncRNA）特征开发并验证一种UCEC患者的预后模型。
从癌症基因组图谱（TCGA）数据库中获取转录组和临床UCEC数据。进行相关性分析以筛选出铜死亡相关的lncRNAs，并进行单因素回归分析以确定与总生存期（OS）相关的预后因素。通过最小绝对收缩和选择算子（LASSO）回归及交叉验证构建铜死亡相关lncRNA风险模型。通过Kaplan-Meier（KM）、比例风险模型（Cox）回归、列线图、主成分分析（PCA）和分期分析验证模型的准确性和可靠性。在低风险和高风险组之间进行基因本体（GO）富集、免疫功能和肿瘤突变负担（TMB）分析，并预测抗肿瘤药物。
通过相关性分析，获得了155个铜死亡相关的lncRNAs，其中9个lncRNAs被确定为独立的预后因素。建立了一个6个铜死亡相关lncRNA的模型。结果显示，高风险组患者的OS比低风险组患者更倾向于较差。风险评分是一个独立的预后因素，具有较高的准确性和预测价值。与膜相关的GO术语的细胞外结构和锚定成分显著富集。免疫功能和TMB结果被认为彼此不同，这可能解释了低风险组比高风险组有更好的预后。预测有18种化合物在高风险组中作为化疗药物具有高半数最大抑制浓度（IC50）。
我们成功开发了一种用于预测预后的铜死亡相关lncRNA风险模型，同时为UCEC患者的免疫治疗和化疗新方法提供了见解。
