The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Haerbin, Heilongjiang Province, China.
The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Haerbin, Heilongjiang Province, China.
Medicine (Baltimore). 2024 Aug 23;103(34):e39335. doi: 10.1097/MD.0000000000039335.
Cuproptosis, a copper-dependent programmed cell death process, holds promise for controlling cell death in tumor cells. Autophagy, a fundamental cellular process, has been linked to various aspects of cancer, such as proliferation, migration, and drug resistance. This research is centered on the investigation of autophagy- and cuproptosis-related long noncoding RNAs (lncRNAs) and the establishment of a prognostic model for head and neck squamous cell carcinoma. RNA sequencing data from head and neck squamous cell carcinoma patients in The Cancer Genome Atlas database identified cuproptosis-related lncRNAs via Pearson analysis. Patients were divided into training and testing sets. A prognostic model developed in the training set using univariate-least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression was tested for accuracy. Kaplan-Meier analysis showed high-risk patients had poorer outcomes. Cox regression confirmed the model's risk score as an independent prognostic indicator, with receiver operating characteristic and decision curve analyses validating its predictive accuracy. Thirteen lncRNAs associated with autophagy and cuproptosis were identified through bioinformatics analysis. Lasso regression narrowed this to 3 significant prognostic lncRNAs. Based on median risk scores, patients were classified into high-risk and low-risk groups. Kaplan-Meier survival curves revealed significant differences between these groups (P < .01). Through a set of bioinformatics analyses, we identified 13 autophagy- and cuproptosis-related lncRNAs. By Lasso regression, 3 prognostic-related lncRNAs were further selected. We also investigated these 3 lncRNAs in relation to clinicopathologic features. The principal component analysis visually showed differences between the high-risk and low-risk groups.
铜死亡是一种依赖铜的程序性细胞死亡过程，有望控制肿瘤细胞中的细胞死亡。自噬是一种基本的细胞过程，与癌症的多个方面有关，如增殖、迁移和耐药性。本研究集中于研究自噬和铜死亡相关的长链非编码 RNA（lncRNA），并建立头颈部鳞状细胞癌的预后模型。通过 Pearson 分析从癌症基因组图谱数据库中的头颈部鳞状细胞癌患者的 RNA 测序数据中确定了铜死亡相关的 lncRNA。患者被分为训练集和测试集。使用单变量最小绝对收缩和选择算子（Lasso）和多变量 Cox 回归在训练集中开发的预后模型用于准确性测试。Kaplan-Meier 分析显示高危患者的预后较差。Cox 回归证实了该模型的风险评分是独立的预后指标，接受者操作特征和决策曲线分析验证了其预测准确性。通过生物信息学分析鉴定了与自噬和铜死亡相关的 13 个 lncRNA。Lasso 回归将其缩小到 3 个有意义的预后 lncRNA。根据中位数风险评分，将患者分为高危和低危组。Kaplan-Meier 生存曲线显示这些组之间存在显著差异（P<0.01）。通过一系列生物信息学分析，我们鉴定了 13 个与自噬和铜死亡相关的 lncRNA。通过 Lasso 回归，进一步选择了 3 个预后相关的 lncRNA。我们还研究了这 3 个 lncRNA 与临床病理特征的关系。主成分分析直观地显示了高危组和低危组之间的差异。
