Construction and analysis of prognostic risk model for immune-related genes for clear cell renal cell carcinoma
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摘要: 目的 筛选肾透明细胞癌(clear cell renal cell carcinoma，ccRCC)预后免疫相关基因(immune-related genes，IRGs)，并建立与免疫基因预后风险相关的评分模型。方法 通过TCGA数据库下载与ccRCC有关的转录测序信息，应用生物信息学方法 筛选出在ccRCC组织中的差异表达基因，并通过共表达分析方法获取IRGs，进一步在IRGs中筛选出与患者总生存期相关的基因，通过Cox回归构建预后风险评分模型，评估该模型在临床上的综合预测能力。结果 在ccRCC及其附近组织中共发现1 791个差异表达基因，在这些基因中，筛选出IRGs 220个，利用单因素Cox分析方法，我们发现了30个和患者预后密切相关的免疫基因，接着10个IRGs(PDIA2、HAMP、CXCL5、KLRK1、LTB4R、CRP、IL11、UCN、AVPR1B、LAT)通过多因素Cox分析被筛选出来，基于这10个IRGs构建预后模型，预后评分模型ROC曲线下面积(AUC)为0.758。根据风险评分中位数将患者分为高危组合低危组，生存曲线显示2组患者预后差异有统计学意义(P < 0.05)，多因素分析显示该模型的风险评分为ccRCC预后的独立预测因子。结论 ccRCC中IRGs和患者的预后密切相关，基于其构建的风险评分模型不仅可有效预测ccRCC的预后，而且可用来作为ccRCC患者的预后生物标志物。Abstract: Objective In order to find reliable prognostic indicators and biomarkers for clear cell renal cell carcinoma(ccRCC) patients, ccRCC immune-related genes (IRGs) were screened by bioinformatics, aims to establish a scoring model related to the prognostic risk of immune genes.Methods Firstly, the transcription and sequencing information related to ccRCC was downloaded through TCGA database, and the differentially expressed genes in ccRCC tissues were screened by bioinformatics method. Then, IRGs were obtained by co-expression analysis, and the genes related to the overall survival (OS) of patients were further screened out among these IRGs. Lastly, a prognostic risk scoring model was constructed by Cox regression to evaluate the comprehensive predictive ability of the model in clinical practice.Results A total of 1 791 differentially expressed genes were found in ccRCC and its nearby tissues, among which 220 IRGs were screened. Thirty immune genes closely related to patient prognosis were found using univariate Cox analysis, then 10 IRGs(PDIA2, HAMP, CXCL5, KLRK1, LTB4R, CRP, IL11, UCN, AVPR1B, LAT) were screened by multivariate Cox analysis. A prognostic model was constructed based on these 10 IRGs, and the prognosis score model AUC under the ROC curve was 0.758. The survival curve showed that the prognosis of the high risk group and low risk group which based on the median risk score was statistically significant (P < 0.05), and the multivariate analysis showed that the risk score of the model was an independent predictor of the prognosis of ccRCC.Conclusion IRGs in ccRCC are closely related to the prognosis of patients, and the risk scoring model constructed based on them can not only effectively predict the prognosis of ccRCC, but also be used as a prognostic biomarker for ccRCC patients.
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Key words:
- clear cell renal cell carcinoma /
- immunity /
- prognosis /
- prognostic model /
- bioinformatics
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表 1 多因素Cox回归分析筛选ccRCC构建预测评分模型的IRGs
基因 β HR 95%CI P值 PDIA2 0.200 1.227 1.10~1.37 < 0.001 HAMP 0.200 1.216 1.08~1.37 < 0.001 CXCL5 0.010 1.008 1.00~1.02 0.015 KLRK1 0.530 1.704 1.16~2.50 < 0.001 LTB4R 0.080 1.088 1.01~1.18 0.030 CRP 0.003 1.003 1.00~1.01 < 0.001 IL11 0.320 1.378 1.22~1.55 < 0.001 UCN 0.220 1.243 1.11~1.40 < 0.001 AVPR1B -0.120 0.884 0.79~0.98 0.020 LAT -0.570 0.566 0.31~1.03 0.006 
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