基于代谢相关基因的生物信息学分析构建肺鳞癌预后模型

Construction of prognostic model based on the expression of metabolism-related gene in squamous cell lung carcinoma via bioinformatics

目的基于代谢相关基因的生物信息学分析构建肺鳞癌预后模型。方法从癌症基因组图谱数据库下载肺鳞癌相关数据,从GENECARDS网站查找代谢相关基因,使用R软件筛选差异表达的代谢相关基因,然后进一步进行单因素Cox回归和LASSO回归分析并构建预后模型。运用Kaplan-Meier生存分析、ROC曲线对模型进行评价。采用多因素Cox回归鉴定模型是否可作为独立预后因子,通过决策曲线分析和诺莫列线图评价模型的可行性和精确度。结果成功构建8个代谢相关基因组成的预后模型。模型将患者分为高风险和低风险组,且提示低风险组的预后更好(P<0.001),ROC曲线显示3年和5年生存率的曲线下面积分别为0.765和0.758。多因素Cox回归分析表明模型可以作为一个独立的预后因子(HR=1.139,95%CI=1.101-1.179,P<0.05)。决策曲线及诺莫列线图均提示本预后模型有较好的预测能力。结论本研究成功建立了基于8个代谢相关基因的肺鳞癌预后模型,该模型可以对患者的个体化治疗提供一定帮助,并提高肺鳞癌患者的个体化预测结果的准确度。

Objective To construct a prognostic model of metabolic related genes for lung squamous cell carcinoma and to search for prognostic biomarkers.Methods The data of squamous cell lung carcinoma was downloaded from the Cancer Genome Atals database,and metabolic-related genes were found from GENECARDS website.Firstly,deferentially expressed metabolism-related genes were analyzed by R software.Secondly,univariate Cox regression analysis and LASSO regression analysis were used to construct the prognostic model.According to the risk score obtained by LASSO regression,the patients were divided into the high risk group and the low risk group.Kaplan-Meier survival analysis and receiver operating characteristic(ROC)curve were used to evaluate the model.Next,the model was assessed by multivariable Cox regression analysis to confirm whether the model was an independent factor.Decision model curve analysis and Nomogram were used to calculate the ability of the prognostic model.Results The prognostic model,based on 8 metabolism-related genes,was constructed successfully.The patients were divided into the high risk group and the low risk group by the model.In addition,the overall survival time of the low-risk group was obviously longer than that of the high-risk group(P<0.001).The ROC curve showed that the area under the curve of 3-year survival rate was 0.765,and the area under the curve of 5-year survival rate was 0.758.Multivariate Cox regression showed that the prognostic model could be used as an independent prognostic factor(HR=1.139,95%CI=1.101-1.179,P<0.05).Both the decision curve and Nomogram indicated that this prognostic model had a good predictive ability.Conclusion Through bioinformatics analysis,the study successfully establishes a prognostic model for lung squamous cell carcinoma based on the expression levels of 8 metabolic-related genes,which can provide certain help for the individualized treatment of patients and improve the accuracy of individualized prediction results for lung squamous cell carcinoma.