基于肿瘤相关巨噬细胞的子宫内膜癌预后模型建立和验证

Integration of scRNA-Seq and TCGA RNA-Seq analyses to establish a novel risk prognostic model based on genes related to tumor-associated macrophages in endometrial carcinoma

目的:利用生物信息学方法研究探讨肿瘤相关巨噬细胞(TAMs)相关基因在子宫内膜癌(EC)的发生发展,发现新的EC肿瘤生物标志物,构建新的EC风险模型。方法:从公共数据库中获得EC患者的单细胞RNA测序(scRNA-seq)数据集。在EC肿瘤和健康组织,检测组织和巨噬细胞中的差异表达基因(DEGs)。采用加权基因共表达网络分析(WGCNA)确定巨噬细胞相关的关键基因,通过多种算法筛选交叉基因,确定为新的EC生物标志物。根据这些生物标志物计算风险评分,从而将EC分为高风险组和低风险组。在单变量和多变量Cox分析中评估了风险评分和临床特征,为构建列线图提供了独立的预后因素。结果:发现805个巨噬细胞DEGs、5691个额外DEGs、733个关键基因和43个常见基因。筛选出FBP1、LTB、PTGER4、PARVG、CCR7和HLA-DMB共6个生物标志物,构建了EC风险模型。确定了风险评分、年龄和肿瘤分期,生成了UCEC直方图。结论:确定了六个与TAM相关的预后生物标志物,并构建了一个风险模型,可为子宫内膜癌的治疗和研究提供依据。

Objective:Previous studies revealed that tumor-associated macrophages(TAMs)are involved in the progression of endometrial carcinoma(EC).From this perspective,we explored EC models and prognostic biomarkers.Methods:We derived single-cell sequencing(scRNA-seq)and RNA-seq datasets of EC patients from public databases.Between tumors and healthy tissues,differentially expressed genes in tissues(DEGs)and in macrophages(Macrophage DEGs)were detected.To identify macrophage-associated hub genes,a Weighted Gene Co-expression Network Analysis(WGCNA)was implemented.To uncover EC biomarkers,intercrossed genes were filtered by multiple algorithms.Based on these biomarkers,risk scores were calculated,allowing EC sample division into high-and low-risk groups.Risk scores and clinical features were assessed in univariate and multivariate Cox analyses,providing independent prognostic factors for nomogram construction.Result:805 macrophage DEGs,5691 additional DEGs,733 hub genes,and 43 common genes were identified.Furthermore,six biomarkers,including FBP1,LTB,PTGER4,PARVG,CCR7,and HLA-DMB,were filtered and risk models were constructed.Finally,to generate a EC nomogram,the risk score,age,and tumor stage were identified.Conclusions:We identified six TAM-related prognostic biomarkers and constructed a risk model,which could provide a basis for EC treatment and study.