构建铜死亡相关lncRNA模型评估头颈鳞状细胞癌预后

Construct of a cuproptosis-related lncRNA model to predict prognosis in head and neck squamous cell carcinoma

摘要

目的 构建一个铜死亡相关长链非编码RNA(long non-coding ribonucleotide, lncRNA)模型，探讨其在预测头颈鳞状细胞癌(head and neck squamous cell carcinoma, HNSCC)患者预后中的价值，并通过药物敏感性分析初步探索HNSCC患者对多种化疗药物的敏感性。方法 从癌症基因组图谱(the cancer genome atlas, TCGA)数据库下载HNSCC患者的转录组数据、肿瘤突变谱和临床信息，鉴定并筛选与预后有关的铜死亡相关lncRNA(cuproptosis-related lncRNA, CRL)并建立预后模型。根据风险评分将患者分为低危组和高危组，采用生存曲线、受试者工作特征(receiver operating characteristic, ROC)曲线、一致性指数和校准曲线评价预后模型的性能。Cox回归分析确定HNSCC的独立预后影响因素，建立预测列线图。功能富集分析与免疫浸润分析进一步探索预后模型的潜在分子机制和生物学基础。分析不同预后患者的肿瘤突变负荷(tumor mutational burden, TMB)及对化疗药物的敏感性。结果 共鉴定出781个CRL，筛选得到9个预后相关的CRL被纳入预后模型的构建。生存曲线显示高危组患者与HNSCC不良预后相关(P<0.05)。在训练集中，预后模型预测1、3、5年生存率的ROC曲线下面积(Area Under the Curve, AUC)分别为0.694、0.753、0.643，且预后模型的一致性指数高于临床特征。年龄、肿瘤分期和风险评分是HNSCC的独立预后影响因素(P均<0.05)。功能富集分析显示，低危组和高危组之间的差异表达基因主要富集免疫相关蛋白和功能。免疫浸润分析显示，低危组患者8种免疫细胞表达水平和7个免疫功能评分较高。生存曲线显示，低TMB+低危组患者预后最好，高TMB+高危组患者预后最差(P<0.05)。药物敏感性分析显示，顺铂、依托泊苷、吉西他滨、丝裂霉素C、索拉非尼、长春瑞滨是治疗高危组患者的潜在药物，而雷帕霉素、苯乙双胍是低危组患者的潜在治疗药物。结论 基于9个CRL构建的HNSCC患者预后预测模型可以较好预测HNSCC患者的预后，为进一步研究HNSCC治疗提供参考。

Abstract

Objective To construct a cuproptosis-related long non-coding ribonucleotide (lncRNA) model and explore its prognostic value in patients with head and neck squamous cell carcinoma (HNSCC), and to preliminarily explore the drug sensitivity of HNSCC patients by drug sensitivity analysis. Methods Transcriptome data, tumor mutation profiles, and clinical information of HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Pearson correlation analysis was used to identify lncRNAs co-expressed with cuproptosis-related gene. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen the prognostic cuproptosis-related lncRNA (CRL) and establish a prognostic model. The risk score of each patient was calculated, and the median was used as the cutoff to divide the patients into low-risk and high-risk groups for survival difference analysis. Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, C-index, and calibration curve were used to evaluate the performance of the prognostic model. Independent prognostic risk factors were identified by univariate and multivariate Cox regression analysis. A predictive nomogram was then constructed based on the risk scores and clinical characteristics. Functional enrichment analysis and immune infiltration analysis were performed to further explore the potential molecular mechanism and biological basis of the prognostic model. Finally, the tumor mutational burden (TMB) and drug sensitivity of patients with different prognosis were analyzed. Results A total of 781 CRLs were identified, and 9 prognostic CRLs were selected and included in the prognostic model. The KM curve showed that the high-risk group was associated with poor prognosis of HNSCC (P<0.05). In the train set, the area under the ROC curves (AUCs) of the CRL prognostic model for 1-year, 3-year, and 5-year survival rates were 0.694, 0.753, and 0.643, respectively. In addition, the C-index of the model was higher than that of the clinical characteristics. Age, tumor stage, and risk score were independent prognostic risk factors (all P < 0.05). Functional enrichment analysis showed that the differentially expressed genes between the low-risk and high-risk groups were mainly enriched in immune-related proteins and functions. Immune infiltration analysis showed that the expression levels of 8 immune cells and 7 immune function scores were higher in the low-risk group. KM curve showed that patients with low TMB + low risk have the best prognosis, and patients with high TMB + high risk have the worst prognosis (P<0.05). Drug sensitivity analysis showed that cisplatin, etoposide, gemcitabine, mitomycin C, sorafenib, and vinorelbine were potential drugs for the treatment of high-risk patients, while rapamycin and phenformin were potential drugs for the treatment of low-risk patients. Conclusion The prognostic prediction model of HNSCC patients based on 9 CRLs can well predict the prognosis of HNSCC patients, and evaluated the sensitivity of patients to chemotherapy drugs to provide a reference for the study of HNSCC treatment.

关键词

铜死亡 / 长链非编码RNA / 头颈鳞状细胞癌 / 预后模型 / 药物敏感性

Key words

Cuproptosis / Long non-coding ribonucleotide / Head and neck squamous cell carcinoma / Prognostic model / Drug sensitivity

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基金资助

泰山学者计划项目(ts20190991)

山东省重点研发计划项目(2022CXPT023)