基于乳酸代谢相关基因的头颈部鳞状细胞癌分子亚型和临床特征的生物信息学分析

Bioinformatics analysis on molecular subtypes and clinical characteristics of head and neck squamous cell carcinoma based on genes associated with lactate metabolism

目的:筛选头颈部鳞状细胞癌(HNSCC)差异预后乳酸代谢相关基因(LRGs),构建HNSCC的LRGs预后模型,并阐明其潜在的作用机制。方法 :由癌症基因组图谱(TCGA)数据库和基因表达综合(GEO)数据库获取HNSCC基因表达及临床数据,由GeneCards数据库中获取LRGs,采用R软件筛选HNSCC的LRGs。采用单因素Cox回归分析得到预后相关基因,基于预后相关LRGs鉴定出2种不同亚型,采用Kaplan-Meier (K-M)曲线分析比较2组患者预后,采用CIBERSORT算法进行2组患者间的免疫相关分析。采用多因素Cox回归分析和LASSO回归分析构建预后模型,采用受试者工作特征曲线(ROC)和K-M生存曲线评估LRGs与HNSCC患者生存和预后的关系。采用GSE27020、GSE41613和GSE65858数据集验证预后模型。基于风险评分进行分组,并进行免疫相关分析和肿瘤相关评分分析。结果:通过TCGA数据库从HNSCC样本中差异分析筛选出1 196个LRGs,单因素Cox回归分析筛选出27个差异表达基因(DEGs)与HNSCC患者预后相关,根据预后相关基因鉴定出2种不同的LRGs亚型(分组1和分组2),K-M生存曲线显示分组2患者总生存期(OS)明显高于分组1,分组2患者免疫细胞浸润水平明显高于分组1。多因素Cox回归分析和LASSO回归分析筛选出9个LRGs,包括次黄嘌呤磷酸核糖基转移酶1 (HPRT1)、淀粉样蛋白前体蛋白(APP)、糖原磷酸化酶(PYGL)、尿激酶型纤溶酶原激活物(PLAU)、大麻素受体2(CNR2)、斯钙素2(STC2)、核苷酸结合寡聚化结构域样受体1 (NLRP1)、整合素连接激酶(ILK)和叉头框蛋白B1 (FOXB1),构建预后模型,K-M曲线和ROC曲线显示上述9个基因表达水平与HNSCC患者生存和预后有关联,且均具有良好的1、 2和3年生存预测作用,ROC曲线下面积(AUC)均大于0.650,且预后模型的预后预测作用在GSE27020、GSE41613和GSE65858数据集中得到验证。根据风险评分分类的患者具有可区分的免疫状态。结论:基于生物信息学方法筛选出的HNSCC差异表达LRGs与HNSCC患者生存和预后有关联,由9个LRGs构建的预后模型可预测HNSCC患者的生存情况和治疗反应。

Objective:To select the differential prognostic lactic acid metabolism-related genes(LRGs)of the head and neck squamous cell carcinoma(HNSCC)to construct the LRGs prognostic model of HNSCC,and to clarify the potential mechanism.Methods:The HNSCC gene expression and clinical data were obtained from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)Databases,the LRGs were identified through GeneCards Database,and R software was used to screen out the LRGs of HNSCC;univariate Cox regression analysis was used to identify prognosis-related genes;two different subtypes were identified based on the prognostis-related LRGs;Kaplan-Meier(K-M)curve analysis was used to compare the prognosis of the patients between two groups;CIBERSORT algorithm was used to perform the immuno-correlation analysis between two groups;multivariate Cox regression analysis and LASSO regression analysis were used to construct the prognostic model;receiver operating characteristic curve(ROC)and K-M survival curve were used to assess the relationship between LRGs and survival and prognosis of the HNSCC patients.The prognostic model was validated by GSE27020,GSE41613,and GSE65858 datasets.The experiment were grouped based on risk score,and immune-related analysis and tumor score analysis were performed.Results:The TCGA Database differential analysis results showed that 1196 LRGs were identified from HNSCC samples;univariate Cox regression analysis selected 27 differentially expressed genes(DEGs)associated with the prognosis of the HNSCC patients.Two different LRGs subtypes(Group 1 and Group 2)were identified according to the prognosis-related genes.The K-M survival curves results showed that the overall survival(OS)of the patients in Group 2 was significantly higher than that in Group 1,and the immune cell expression amount of the patients in Group 2 was also higher than that in group 1.The multivariate Cox regression and LASSO regression analysis results screened out 9 LRGs,including hypoxanthine phosphoribosyltransferase 1(HPRT1),amyloid precursor protein(APP),glycogen phosphorylase L(PYGL),urokinase-type plasminogen activator(PLAU),cannabinoid receptor 2(CNR2),stanniocalcin 2(STC2),nucleotide binding oligomerization domain-like receptor protein 1(NLRP1),integrin-linked kinase(ILK),and forkhead box B1(FOXB1);the prognostic model was constructed.The K-M and ROC curve results indicated that the expression levels of above 9 genes were associated with the survival and prognosis of the HNSCC patients,providing good 1-year,2-year,and 3-year survival prediction effect,and the area under ROC curve(AUC)values were all greater than 0.650.Furthermore,the predictive ability of the prognosis model was validated in GSE27020,GSE41613,and GSE65858 datasets.The patients classified based on the risk scores had distinguishable immune statuses.Conclusion:The differentially expressed LRGs of HNSCC screened by bioinformatics methods are related to the survival and prognosis of the HNSCC patients;the prognostic model constructed by 9 LRGs can predict the survival status and treatment response of the HNSCC patients.