基于免疫相关基因的宫颈鳞癌预后模型的构建

Construction a prognostic model for cervical squamous cell cancer based on immune related genes

[目的]宫颈鳞状细胞癌(cervical squamous cell carcinoma, CSCC)中利用免疫相关基因(immune related genes, IRGs)构建风险评分预后模型并评估预测精度。[方法]癌症基因组图谱(The Cancer Genome Atlas, TCGA)数据库253例CSCC患者2 499个IRGs的表达谱进行差异表达及GO和KEGG富集分析。采用LASSO-Cox分析构建模型,使用生存分析、1,3,5年受试者工作特征(receiver operating characteristic, ROC)曲线下面积(area under the curve, AUC)以及主成分析研究风险评分预测性能。构建列线图并单因素和多因素回归分析预后因素。[结果]CSCC中鉴别出53个差异表达的IRGs,主要富集在免疫相关的通路(FDR<0.05)。构建预后模型,筛选具有预测功能的4个基因(PLA1A、S1PR4、CHIT1、CA9),生存分析显示低风险组和高风险组的总生存率差异显著(P=0.000 75)。风险评分1,3,5年ROC曲线的AUC值分别为0.966、0.724和0.818,提示良好的预测效果。所构建的风险模型生存曲线能够显著区分患者预后,高风险组患者预后更差。列线图模型证实风险评分的预后效能好于临床特征同时单因素和多因素回归分析显示风险评分是宫颈癌患者的独立预后因素(P<0.005)。[结论]基于IRGs的预后模型可以更准确地预测宫颈癌患者的预后,免疫风险评分可能是CSCC潜在的预后生物标志物。

[Objective]To construct the prognostic model for cervical squamous cell carcinoma(CSCC)based on immune-related genes(IRGs) and determine the predictive accuracy.[Method] Based on TCGA databases, differential expression of 2499 IRGs were analyzed in 253 CSCC patients and it would be used in GO enrichment analysis and KEGG pathway analysis.The immune-related prognostic model was established by LASSO-Cox analysis.The area under the curve(AUC) of the receiver operating characteristic(ROC) curve for 1,3,and 5 years and PCA analysis was used to study the predictive effect of risk score.A nomogram was constructed and the prognostic factors were analyzed by univariate and multivariate regression.[Result]53 differentially expressed IRGs were identified in CSCC.Enrichment analysis of GO and KEGG pathways showed that they were mainly involved in immunological activities.Through the established prognostic risk model, 4 genes(PLA1A,S1PR4,CHIT1,CA9)with predictive function were identified, survival analysis showed a significant difference in prognosis between the high-risk and low-risk groups(P=0.000 75).The survival of low risk group was significantly better than that of high risk group(P<0.05).The AUC values of ROC curve for risk score 1,3 and 5 years were 0.966,0.724 and 0.818 respectively and indicated that the model showed moderate prognostic efficacy.The survival curves showed that the constructed risk model was able to significantly differentiate the prognosis of patients, patients with in the high-risk group having a worse prognosis.The nomogram model confirmed that the prognostic efficacy of the risk score was better than the clinical features and univariate and multivariate Cox regression showed that risk score was an independent prognostic factor for cervical cancer patients(P<0.005).[Conclusion]The prognostic model based on IRGs can predict the prognosis of cervical cancer patients more accurately and risk score may be a potential prognostic biomarker for CSCC.