肾透明细胞癌微环境中关键基因鉴定及预后列线图模型建立  

Identification of key genes in the tumor microenvironment of renal clear cell carcinoma and establishment of a nomogram

作　　者：周玉贺 张龙[1] 黄珍林 贾占奎[1] 杨锦建[1] ZHOU Yuhe;ZHANG Long;HUANG Zhenlin;JIA Zhankui;YANG Jinjian(Department of Urology,First Affiliated Hospital of Zhengzhou University,Zhengzhou,450052,China)

机构地区：[1]郑州大学第一附属医院泌尿外科,郑州450052

出　　处：《临床泌尿外科杂志》2023年第8期569-575,共7页Journal of Clinical Urology

基　　金：河南省科技攻关项目(No:212102310116)。

摘　　要：目的筛选肾透明细胞癌(clear cell renal cell carcinoma,ccRCC)微环境中的关键基因,并建立预后预测模型。方法从TCGA数据库中下载ccRCC RNA-seq FPKM数据及临床数据。基于ESTIMATE算法对肿瘤微环境中免疫细胞、基质细胞进行评分,以免疫细胞,基质细胞评分中位值为界把样品各自分组,组间进行差异表达基因(DEGs)分析,并用wilcoxon检验进行验证,对得到的DEGs取交集获得肿瘤微环境中的关键基因。对获得的关键基因进行功能富集。把样品按照1:1的比例分为训练组和验证组。利用Cox-LASSO法从训练组中的关键基因中筛选出建模基因。多因素Cox回归建立风险评估模型,将风险评估模型与临床指标进行单因素和多因素Cox回归筛选患者预后的独立影响因素。联合各项患者预后独立相关的因素绘制列线图。结果免疫细胞评分组共获得DEGs 658个,而利用基质细胞评分共获得DEGs 411个,取交集后共得到95个关键基因。随机分组后,训练组与验证组性别、年龄、肿瘤分期及分级的构成比较均差异无统计学意义。训练组中34个基因与患者预后相关,进而通过LASSO回归获得15个特征基因。经多因素Cox分析得到由9个基因(HMGCS2、FREM1、CASP5、SLN、SPIC、SPIB、RORB、CPN1、F7)构成的最优模型。生存分析表明训练组与验证组高低风险组间生存率的差异有统计学意义(P<0.001),且各组对于1、3、5年生存率的评估有着较高的灵敏度与特异度。经单因素和多因素Cox回归分析发现患者年龄、肿瘤分期、分级、风险评分为患者预后的独立影响因素(均P<0.001)。联合患者临床因素如年龄、肿瘤分级和肿瘤分期及风险评分构建了预后评估的列线图模型。结论由9个基因构成的风险评分模型可对ccRCC风险做出准确判断;风险评分、年龄、肿瘤分期、肿瘤分级为ccRCC独立预后影响因素,由其构成的列线图模型可精准预测患者生存Objective To construct a risk prediction model for patients with clear cell renal cell carcinoma(ccRCC)and create a nomogram for survival prediction.Methods RNA-seq FPKM data and clinical data of ccRCC were downloaded from TCGA database.Based on the ESTIMATE algorithm,immune cells and stromal cells in the tumor microenvironment were scored;The samples were grouped by the median value of immune cells and stromal cells,and the differentially expressed genes(DEGs)were analyzed between groups and validated by wilcoxon test.Functional enrichment was performed on the obtained key genes.The samples were divided into a training group and a validation group in a 1∶1 ratio.Modeling genes were screened from the key genes in the training group using the Cox-LASSO method.Multi-factor Cox regression was used to establish risk assessment models,and univariate and multi-factor Cox regressions were performed to screen independent influences on patient prognosis with clinical indicators.The factors independently associated with each patient prognosis were combined to draw a nomogram.Results A total of 658 DEGs were obtained according to the immune cell scoring group,while a total of 411 DEGs were obtained using stromal cell scoring,and a total of 95 key genes were obtained after taking the intersection.After randomization,there were no significant differences in the composition of gender,age,tumor stage or grade between the training and validation groups.Of these,34 genes were associated with patient prognosis,and then 15 signature genes were obtained by LASSO regression.The optimal model consisting of 9 genes(HMGCS2,FREM1,CASP5,SLN,SPIC,SPIB,RORB,CPN1,F7)was obtained by multifactorial Cox analysis.Survival analysis showed significant differences in survival between the high and low risk groups in the training and validation groups(P<0.001),and the groups had high sensitivity and specificity for the assessment of survival at 1,3 and 5 years.Univariate and multifactorial Cox regression analysis revealed patients'age,tumor stage,g

关 键 词：肾透明细胞癌 肿瘤微环境 列线图 

分 类 号：R737.11[医药卫生—肿瘤]