基于单细胞RNA-seq和Bulk RNA-seq数据构建巨噬细胞相关基因的肝癌预后风险预测模型

doi:10.11904/j.issn.1002-3070.2023.05.003

实用肿瘤学杂志 ›› 2023, Vol. 37 ›› Issue (5): 403-410.doi: 10.11904/j.issn.1002-3070.2023.05.003

基于单细胞RNA-seq和Bulk RNA-seq数据构建巨噬细胞相关基因的肝癌预后风险预测模型

邓洁莲¹, 郑薇², 李康杰¹, 张聪¹, 张源¹, 谢彪¹, 钟晓妮¹

1.重庆医科大学公共卫生学院卫生统计教研室(重庆 401331);
2.重庆医科大学附属第一医院心内科

收稿日期:2022-10-26 修回日期:2023-06-12 出版日期:2023-10-28 发布日期:2024-03-18
通讯作者: 钟晓妮,E-mail:zhongxiaoni@cqmu.edu.cn;谢彪,E-mail:kybiao@cqmu.edu.cn
作者简介:邓洁莲,女,(1996-),硕士研究生,从事肿瘤疾病的类型分类和预后模型预测方面的研究。
基金资助:
国家青年科学基金项目(编号:82204159);重庆市教委科学技术研究计划项目(编号:KJQN202300423)

Construction of a prognostic risk prediction model in liver cancer for macrophage related genes based on single-cell RNA-seq and bulk RNA-seq data

DENG Jielian¹, ZHENG Wei², LI Kangjie¹, ZHANG Cong¹, ZHANG Yuan¹, XIE Biao¹, ZHONG Xiaoni¹

1. School of Public Health,Chongqing Medical University,Chongqing 401331,China;
2. Department of Cardiology,The First Affiliated Hospital of Chongqing Medical University

Received:2022-10-26 Revised:2023-06-12 Online:2023-10-28 Published:2024-03-18

摘要/Abstract

摘要： 目的识别肝癌巨噬细胞的相关基因(Macrophage-related genes,MRGs),并构建肝癌预后风险预测模型。方法从GEO数据库中下载肝癌scRNA-seq数据,识别在巨噬细胞中特异性表达的基因作为MRGs。对MRGs进行GO和KEGG功能富集分析。在TCGA数据库的TCGA-LIHC数据集中,利用多次随机抽样的单因素Cox回归、LASSO回归、多因素Cox回归分析,鉴定用于肝癌预后预测的MRGs,并构建肝癌预后预测模型。结果利用GEO数据库的scRNA-seq进行聚类获得8个含巨噬细胞的主要细胞类型。肝癌免疫微环境中的巨噬细胞比例明显高于正常组织(P=0.016),且高表达SPP1、RNASE1和MMP9等基因。嘌呤代谢、柠檬酸循环、药物代谢-细胞色素P450等多条代谢相关通路在肝癌相关巨噬细胞中被激活。本研究从肝癌scRNA-seq中识别出777个MRGs(LogFC＞0.25,P＜0.05),它们主要参与肌动蛋白结合、调节免疫受体活性等过程。从169个预后相关基因(P＜0.05)中筛选出ATP1B3、ATP6V0B、HBEGF、KLF2、NR1H3、RAB10和SPP1共7个MRGs用于预后模型构建。在TCGA数据库肝癌队列中模型的1、3、5年生存结局AUC值分别为0.791、0.791和0.751,在验证集ICGC队列中为0.614、0.682和0.688,表现出良好的预测性能。在预后风险评分高的肝癌患者中,巨噬细胞M0、中性粒细胞和调节性T细胞的表达较高(P＜0.05),且免疫抑制和免疫激活共存。结论肝癌MRGs可作为预测肝癌患者预后的潜在标志物,这些肝癌MRGs主要与肌动蛋白结合、免疫受体活性相关,并且与多种免疫细胞的浸润相关。

关键词: 原发性肝癌, 生物信息学, 巨噬细胞, 预后, SPP1

Abstract: Objective The aim of this study was to identify macrophage related genes(MRGs)in liver cancer and construct a prognostic risk prediction model for liver cancer. Methods The liver cancer scRNA-seq data from the GEO database were downloaded to identify genes specifically expressed in macrophages as MRGs.The GO and KEGG functional enrichment analyses on MRGs were conducted.In the TCGA-LIHC dataset of the TCGA database,multiple random sampling single factor Cox regression for single-factor Cox regression,LASSO regression,and multivariate Cox regression analyses were employed to identify MRGs for liver cancer prognosis prediction,and a liver cancer prognostic prediction model was constructed. Results The results obtained 8 major cell types,including those containing macrophages through clustering using scRNA-seq data from the GEO database.The proportion of macrophages in the immune microenvironment of liver cancer was significantly higher than that of normal tissues(P=0.016),and genes such as SPP1,RNASE1,and MMP9 were highly expressed.Multiple metabolic pathways,including purine metabolism,citric acid cycle,and drug metabolism cytochrome P450 were activated in liver cancer-associated macrophages.This study identified 777 MRGs from liver cancer scRNA-seq(LogFC＞0.25,P＜0.05),which mainly involved in functions such as actin binding and regulation of immune receptor activity.Seven MRGs,including ATP1B3,ATP6V0B,HBEGF,KLF2,NR1H3,RAB10,and SPP1 were selected from the 169 stable prognostic genes(P＜0.05)for the construction of the prognosis model.The AUC values for the 1,3,and 5-year survival outcomes of the model in the TCGA liver cancer cohort were 0.791,0.791,and 0.751,respectively.In the validation ICGC cohort,they were 0.614,0.682,and 0.688,respectively,demonstrating good predictive performance.In liver cancer patients with high prognosis risk scores,the expression of macrophages M0,neutrophils,and regulatory T cells was higher(P＜0.05),and immunosuppression and immune activation coexisted. Conclusion Liver cancer MRGs can serve as potential biomarkers for predicting the prognosis of liver cancer patients.These liver cancer MRGs are mainly associated with actin binding,immune receptor activity,and infiltration of various immune cells.

Key words: Primary liver cancer, Bioinformatics, Macrophages, Prognosis, SPP1

中图分类号:

R735.7

邓洁莲, 郑薇, 李康杰, 张聪, 张源, 谢彪, 钟晓妮. 基于单细胞RNA-seq和Bulk RNA-seq数据构建巨噬细胞相关基因的肝癌预后风险预测模型[J]. 实用肿瘤学杂志, 2023, 37(5): 403-410.

DENG Jielian, ZHENG Wei, LI Kangjie, ZHANG Cong, ZHANG Yuan, XIE Biao, ZHONG Xiaoni. Construction of a prognostic risk prediction model in liver cancer for macrophage related genes based on single-cell RNA-seq and bulk RNA-seq data[J]. Journal of Practical Oncology, 2023, 37(5): 403-410.

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基于单细胞RNA-seq和Bulk RNA-seq数据构建巨噬细胞相关基因的肝癌预后风险预测模型

Construction of a prognostic risk prediction model in liver cancer for macrophage related genes based on single-cell RNA-seq and bulk RNA-seq data

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