肾透明细胞癌细胞焦亡相关基因预后模型的建立与应用

Constructionand Application of Pyroptosis-related Genes Prognosis Model in Clear Cell Renal Cell Carcinoma

目的基于TCGA数据库构建肾透明细胞癌(ccRCC)细胞焦亡相关基因预后模型。方法从TCGA数据库下载509例ccRCC及71例癌旁正常组织的转录组数据和临床数据,获取差异表达焦亡基因;一致性聚类将ccRCC患者分为两个亚型,基于两亚型间的差异基因,进行Lasso-Cox回归构建预后模型,计算预后评分;根据最佳截止值,将ccRCC患者分为高风险组和低风险组,绘制Kaplan-Meier生存曲线,比较两组生存差异;绘制ROC曲线,评价预后模型的预测能力,将预后评分和临床病理因素纳入Cox回归分析ccRCC患者的预后影响因素,构建列线图,评估预测与实际生存概率的一致性;GSEA、GSVA分析比较高风险组和低风险组信号通路间差异,通过ESTIMATE、CIBERSORT和quanTIseq三种算法比较高风险组与低风险组免疫细胞浸润的差异,比较两组免疫检查点基因表达水平,预测高、低风险组对免疫治疗的反应。结果共筛选23个上调和1个下调的差异焦亡基因,确定了2种ccRCC亚型,筛选出248个可作为独立预后特征的差异基因,通过LASSO回归,最终纳入10个焦亡相关基因构建预后模型;预后风险评分=1.0518×C10orf90+0.0845×HS3ST3A1+0.0407×SAA1-0.324×MPP7+0.2061×IGF2BP3+0.0566×ADAMTS14+0.2176×CILP-0.0119×SHOX2-0.1069×TRIM2+0.3367×HHIPL2;Kaplan-Meier生存分析显示,低风险组比高风险组有生存优势(P<0.001);Cox回归分析显示,风险评分是ccRCC患者预后独立影响因素(P<0.001);GSEA、GSVA分析显示,高风险组主要富集在免疫相关通路,低风险组主要富集在代谢相关通路;免疫细胞浸润分析显示,高风险组含有更高水平的免疫抑制细胞;免疫治疗反应预测结果显示,低风险组有更高的反应率。结论ccRCC焦亡相关基因预后模型可用于预测ccRCC患者的预后,可以作为免疫治疗反应的预测指标,有利于进一步指导临床治疗。

Objective To construct the pyroptosis-related gene prognosis model of clear cell renal cell carcinoma(ccRCC)based on TCGA database.Methods The transcription group and clinical data for 509 cases of ccRCC and 71 cases of normal tissues were downloaded from the TCGA database for extracting the expression profiles of pyroptosis-related genes.Consistency clustering divided ccRCC patients into two subtypes.Based on the differential genes between the two subtypes,Lasso-Cox regression was performed to construct the prognosis model and calculate the prognosis score.Patients were divided into high-risk and low-risk groups based on the best cutoff value.The survival difference between two groups was analyzed with Kaplan-Meier survival curve,and the predictive capability of prognosis model was evaluated with ROC curve.The factors influencing survival were analyzed by incorporating clinicopathological factors and prognostic score into Cox regression method.Assess consistency of predicted and actual survival probability based on constructed a nomogram.Compared the signal pathway differences between the high-risk and the low-risk groups by GSEA and GSVA analysis.The three algorithms of ESTIMATE,CIBERSORT and quanTIseq were used to compare the differences in immune cell infiltration between the high-risk and the low-risk group.The gene expression levels of immune checkpoints between the two groups were compared to predict the response immunotherapy.Results A total of 23 up-regulated and 1 down-regulated differential pyroptosis genes were screened,and two subtypes of ccRCC were determined.A total of 248 differential genes that could be used as independent prognostic features were screened.Through LASSO regression,10 pyroptosis-related genes were finally incorporated LASSO regression.Prognostic risk score=1.0518×C10orf90+0.0845×HS3ST3A1+0.0407×SAA1-0.324×MPP7+0.2061×IGF2BP3+0.0566×ADAMTS14+0.2176×CILP-0.0119×SHOX2-0.1069×TRIM2+0.3367×HHIPL2;Kaplan-Meier survival analysis showed that the low-risk group had a survival advantage than the high-risk group(P<0.001).Cox regression analysis showed that risk score was an independent prognostic factor for ccRCC patients(P<0.001).GSEA and GSVA analysis showed that the high risk group was mainly enriched in immune-related pathways,and the low risk group was mainly enriched in metabolic pathways.Immunocyte infiltration analysis showed that the high risk group contained higher levels of immunosuppressive cells;immunotherapy response prediction showed that low-risk groups had higher response rates.Conclusion The model is able to predict the prognosis of ccRCC patients,and can be used as a predictor of immunotherapy response,which is beneficial to further guide clinical treatment.