通过生物信息学分析预测肺鳞癌病人预后的免疫信号

A BIOINFORMATICS ANALYSIS OF IMMUNE SIGNALS FOR PREDICTING THE PROGNOSIS OF PATIENTS WITH LUNG SQUAMOUS CELL CARCINOMA

目的基于免疫相关基因(IRGs)构建肺鳞癌(LUSC)病人预后风险模型。方法从癌症基因组图谱(TCGA)数据库下载551例LUSC病人的基因表达谱,并从ImmPort数据库中获得IRGs,再利用R软件筛选差异表达的免疫相关基因(DEIRGs);构建转录因子调控网络,探讨其异常表达的机制。应用单变量Cox回归确定DEIRGs的预后价值,应用Lasso回归和多变量Cox比例风险回归构建预后风险模型,并对模型的预测性能进行验证。评估这些危险因素与各种临床变量和免疫浸润细胞之间的关系。结果由PLAU、JUN、RNASE7、FOS、IGGD3-22、IGKV1-6、SEMA4C、APLN、FGFR4和TRAV39共10个IRGs构成的风险模型显示出良好的预测性能,有望成为预测LUSC病人预后的独立因素,且该模型还能对不同预后的病人进行分层,风险评分高的病人更容易出现免疫细胞浸润。结论 IRGs可用于评估LUSC病人的预后和肿瘤免疫微环境的状态。

Objective To establish a prognostic risk model for patients with lung squamous cell carcinoma(LUSC) based on immune-related genes(IRGs). Methods The gene expression profiles of 551 LUSC patients were downloaded from The Cancer Genome Atlas(TCGA) database, and IRGs were obtained from the ImmPort database. R software was used to screen out differentially expressed IRGs(DEIRGs), and a transcription factor regulatory network was constructed to investigate the mechanism of abnormal expression. A univariate Cox regression analysis was used to investigate the prognostic value of DEIRGs;Lasso regression analysis and multivariate Cox proportional-hazards regression model were used to construct a prognostic risk model, and the predictive performance of this model was verified. In addition, the association of these risk factors with various clinical variables and immune infiltrating cells was evaluated. Results The risk model based on the 10 IRGs of PLAU, JUN, RNASE7, FOS, IGGD3-22, IGKV1-6, SEMA4 C, APLN, FGFR4, and TRAV39 showed good predictive performance and was expected to be used as an independent factor for predicting the prognosis of LUSC patients. The model was able to stratify patients based on prognosis, and patients with high risk scores were more likely to develop immune cell infiltration. Conclusion IRGs can be used to evaluate the prognosis of patients with LUSC and the state of tumor immune microenvironment.