通过scRNA-seq和批量RNA-seq的综合分析建立基于CAF特征的HCC预后预测模型
Establish a prognostic prediction model of HCC based on the comprehensive analysis of scRNA-seq and bulck RNA-seq
安外尔·约麦尔阿卜拉 1孙莉莉 1刘富中 1迪丽娜尔·叶尔夏提 1翟晓艺 1郭文佳 1董晓刚2
作者信息
- 1. 新疆医科大学附属肿瘤医院肿瘤防治研究所,新疆 乌鲁木齐 830011
- 2. 新疆医科大学附属肿瘤医院肝胆胰外科
- 折叠
摘要
目的 以整合批量 RNA-seq数据和单细胞 RNA-seq 数据挖掘癌症相关成纤维细胞(cancer-associated fibroblasts,CAF)的特征标记以及探索 CAF特征与肝细胞癌(hepatocellular carcinoma,HCC)预后之间的关系.方法 从基因表达综合数据库(gene ex-pression omnibus,GEO)数据库获得 HCC scRNA-seq数据,用 Seurat,Monocle 2 软件包分析 scRNA-seq 数据确定了细胞簇以及分化轨迹,还对所有细胞簇特异性表达的标记基因集进行了富集分析.然后整合 RNA-seq基因表达和相应的临床信息数据,鉴定 CAF特征,并采用单因素 Cox回归和最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归分析,筛选预后相关 CAF特征基因、构建预后模型,划定风险组,建立列线图验证模型预测效能.结果 通过对 scRNA-seq和 RNA-seq数据的整合分析,确定了 HCC中的 7 个细胞簇,并鉴定出了 15 个预后相关CAF基因.通过单因素Cox回归和LASSO回归分析筛选出TTK、EZH2、EME1、SLC7A11、DNAJC6、PNCK、TERB2、S100A8 和 PTPRD-AS1 作为 CAF 特征基因.基于这些基因构建并验证预后特征,根据特征风险评分对患者进行分组,低风险组患者的生存时间明显长于高风险组,此外 ROC 曲线和列线图表明风险评分模型可以更好地评估肝癌患者的预后.结论 我们通过 scRNA-seq分析技术试图探索 HCC 中的 CAF 特征,并建立基于 CAF 的风险特征来预测 HCC患者的预后,该特征有助于对 HCC患者进行个体化治疗.
Abstract
Objective To integrate batch and single-cell RNA-seq data for data mining of cancer-associated fibro-blasts(CAFs)and to explore the relationship between CAF characteristics and prognosis of hepatocellular carcinoma(HCC).Methods HCC scRNA-seq data was obtained from the Gene Expression Omnibus(GEO)database.The scRNA-seq data was analyzed using the Seurat and Monocle 2 software packages to identify cell clusters and differentia-tion trajectories.Additionally,enrichment analysis was performed on the marker gene sets specifically expressed in all cell clusters.RNA-seq gene expression data was then integrated with the corresponding clinical information to identify CAF characteristics.Univariate Cox regression and least absolute shrinkage and selection operator(LASSO)regression analyses were conducted to screen for prognostic-related CAF feature genes,construct a prognostic model,delineate risk groups,and establish a nomogram to validate the predictive efficacy of the model.Results Through the scRNA-seq and RNA-seq data integration analysis,we identified seven HCC cell clusters,and identified the prognosis related 15 CAF genes.By single factor Cox regression and LASSO regression analysis to screen the TTK,EZH2,EME1,SLC7A11,DNAJC6,PNCK,TERB2,S100A8 and PTPRD-AS1 as CAF trait genes.On the basis of these genes we build and veri-fy prognosis characteristics,and patients were grouped according to the characteristics of risk score,the survival time of patients with low risk group was obviously longer than high-risk group.In addition the ROC curve and nomogram risk score model can better assess the prognosis of patients with liver cancer.Conclusion Based on the risk of CAF signa-ture can effectively predict the prognosis of HCC,the signature helps for individualized treatment in patients with HCC.
关键词
肝癌/CAF特征/scRNA分析技术/预后预测模型Key words
Liver cancer/CAF feature/scRNA analysis technology/Prognosis prediction model引用本文复制引用
基金项目
新疆维吾尔自治区"天池英才"计划(2023TCYCDXG)
新疆维吾尔自治区自然科学基金(2022D01C290)
出版年
2024
NETL
NSTL
万方数据