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 expression 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。
Objective To integrate batch and single-cell RNA-seq data for data mining of cancer-associated fibroblasts(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 differentiation 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 verify 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 signature can effectively predict the prognosis of HCC,the signature helps for individualized treatment in patients with HCC.
作者
安外尔·约麦尔阿卜拉
孙莉莉
刘富中
迪丽娜尔·叶尔夏提
翟晓艺
郭文佳
董晓刚
ANWAIER·Yuemaierabola;SUN Lili;LIU Fuzhong;DILINAER·Yeerxiati;ZHAI Xiaoyi;GUO Wenjia;DONG Xiaogang(Institute of Cancer Prevention and Treatment,Cancer Hospital of Xinjiang Medical University,Urumqi 830011;Department of Hepatobiliary and Pancreatic Surgery,Cancer Hospital of Xinjiang Medical University,China)
出处
《胃肠病学和肝病学杂志》
CAS
2024年第8期1021-1026,共6页
Chinese Journal of Gastroenterology and Hepatology
基金
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)。
