Zhao Hongtao, Sun Ruonan, Wu Lei, Huang Peiluo, Liu Wenjing, Ma Qiuhong, Liao Qinyuan, Du Juan
Department of Immunology, College of Basic Medicine, Guilin Medical University, Guilin, 541199, Guangxi, China.
College of Department of Information and Library Science, Guilin Medical University, Guilin, 541004, China.
Biochem Genet. 2024 May 28. doi: 10.1007/s10528-024-10828-z.
Lung squamous cell carcinoma (LUSC) kills more than four million people yearly. Creating more trustworthy tumor molecular markers for LUSC early detection, diagnosis, prognosis, and customized treatment is essential. Cuproptosis, a novel form of cell death, opened up a new field of study for searching for trustworthy tumor indicators. Our goal was to build a risk model to assess drug sensitivity, monitor immune function, and predict prognosis in LUSC patients. The 19 cuproptosis-related genes were found in the literature, and patient genomic and clinical information was collected using the Cancer Genomic Atlas (TCGA) database. The LUSC patients were grouped using unsupervised clustering techniques, and 7626 differentially expressed genes were identified. Using univariate COX analysis, LASSO regression analysis, and multivariate COX analysis, a prognostic model for LUSC patients was developed. The tumor immune escape was evaluated using the Tumor Immune Dysfunction and Exclusion (TIDE) method. The R packages 'pRRophetic,' 'ggpubr,' and 'ggplot2' were utilized to examine drug sensitivity. For modeling, a 6-cuproptosis-based gene signature was found. Patients with high-risk LUSC had significantly worse survival rates than those with low-risk conditions. The possibility of tumor immunological escape was increased in patients with higher risk scores due to more immune cell inactivation. For patients with high-risk LUSC, we discovered seven potent potential drugs (AZD6482, CHIR.99021, CMK, Embelin, FTI.277, Imatinib, and Pazopanib). In conclusion, the cuproptosis-based genes predictive risk model can be utilized to predict outcomes, track immune function, and evaluate medication sensitivity in LUSC patients.
肺鳞状细胞癌（LUSC）每年导致超过400万人死亡。为LUSC的早期检测、诊断、预后和个性化治疗创建更可靠的肿瘤分子标志物至关重要。铜死亡是一种新型的细胞死亡形式，为寻找可靠的肿瘤指标开辟了一个新的研究领域。我们的目标是建立一个风险模型，以评估LUSC患者的药物敏感性、监测免疫功能并预测预后。从文献中找到了19个与铜死亡相关的基因，并使用癌症基因组图谱（TCGA）数据库收集了患者的基因组和临床信息。使用无监督聚类技术对LUSC患者进行分组，鉴定出7626个差异表达基因。通过单变量COX分析、LASSO回归分析和多变量COX分析，建立了LUSC患者的预后模型。使用肿瘤免疫功能障碍与排除（TIDE）方法评估肿瘤免疫逃逸。利用R包“pRRophetic”、“ggpubr”和“ggplot2”来检测药物敏感性。为了建模，发现了一个基于6个铜死亡相关基因的特征。高危LUSC患者的生存率明显低于低危患者。由于更多免疫细胞失活，风险评分较高的患者发生肿瘤免疫逃逸的可能性增加。对于高危LUSC患者，我们发现了七种有效的潜在药物（AZD6482、CHIR.99021、CMK、紫铆因、FTI.277、伊马替尼和帕唑帕尼）。总之，基于铜死亡的基因预测风险模型可用于预测LUSC患者的预后、跟踪免疫功能和评估药物敏感性。
