摘要
目的 构建与口腔鳞状细胞癌(OSCC)相关的免疫和代谢基因的风险评估回归模型,以判断病人的预后状况.方法 从TCGA以及GEO数据库获取OSCC的基因表达数据及其临床信息.筛选出表达差异显著的免疫及代谢基因后,依次运用单因素Cox回归分析、Lasso-Cox回归分析以及多因素Cox回归分析建立免疫基因结合代谢基因相关预后模型,并对该模型的预测效能进行检验.结果 筛选出了 11个建模基因(DEFB1、HTN1、CTSG、TYK2、PTX3、CCR7、OSM、OXT、PSPH、CKM、ACADL),构建了风险回归模型.低风险组与高风险组患者的总生存率存在显著差异(P＜0.001).通过单因素和多因素Cox回归分析验证了该预测模型中的风险评分是独立预后因素(P＜0.001).ROC曲线分析(AUC=0.746)表明了预后模型的良好性能.结论 成功构建了与口腔鳞状细胞癌免疫和代谢基因相关的风险评估回归模型,该模型能帮助临床医师为不同风险的OSCC患者选择个性化的治疗策略.
Abstract
Objective To establish regression models of immune and metabolism-related gene risk in oral squamous cell carcinoma(OSCC)to evaluate the prognosis of patients.Methods To establish genes related to immunity and metabolism of oral squamous cell carcinoma(OSCC),OSCC data pertaining to gene expression along with associated clinical details were retrieved from the TCGA and GEO repositories.Following this,immune and metabolic genes exhibiting differential expression were pinpointed,and a predictive prognostic construction integrating both immune and metabolic gene profiles was developed using univariate,Lasso,and multifactor Cox regression methods.Lastly,the accuracy of this prognostic schema was validated.Results Eleven modeling genes(DEFB1,HTN1,CTSG,TYK2,PTX3,CCR7,OSM,OXT,PSPH,CKM,ACADL)were screened out and a risk regression model was constructed.Substantial disparities in total survival were observed when comparing the groups at low and high risk,with highly noteworthy statistical results(P＜0.001).Both single-variable and multiple-variable analyses using the Cox regression method revealed the model's risk score to be an autonomous predictive element(P＜0.001).Furthermore,the capability of the forecasting model was confirmed by ROC curve assessment,indicated by an AUC of 0.746.Conclusion In summation,the creation of an effective regression model that incorporates immune and metabolic genetic risk factors for oral squamous cell carcinoma could aid medical professionals in selecting tailored therapeutic approaches for patients with varying levels of risk associated with OSCC.
基金项目
河北省科技厅引进国外智力项目(2021)()
河北省卫生健康委青年科技课题(20240699)
河北省卫生健康委重点科技研究计划(20230183)
河北省省级科技计划(21377719D)
NETL
NSTL
万方数据