通过单细胞 RNA 和批量 RNA 测序数据的综合分析构建和验证胶质母细胞瘤的 TAMRGs 预后签名。

Construction and validation of a TAMRGs prognostic signature for gliomas by integrated analysis of scRNA and bulk RNA sequencing data.

机构信息

Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical University, Fujian, PR China.

Department of Epidemiology, School of Public Health, Shenzhen University Medical School, Shenzhen, Guangdong, PR China.

出版信息

Brain Res. 2025 Jan 1;1846:149237. doi: 10.1016/j.brainres.2024.149237. Epub 2024 Sep 11.

DOI:10.1016/j.brainres.2024.149237

PMID:39270996

Abstract

BACKGROUND

This study aimed to construct and validate a prognostic model based on tumor associated macrophage-related genes (TAMRGs) by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) data.

METHODS

The scRNA-seq data of three inhouse glioma tissues were used to identify the tumor-associated macrophages (TAMs) marker genes, the DEGs from the The Cancer Genome Atlas (TCGA) - Genotype-Tissue Expression (GTEx) dataset were used to further select TAMs marker genes. Subsequently, a TAMRG-score was constructed by Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis in the TCGA dataset and validated in the Chinese Glioma Genome Atlas (CGGA) dataset.

RESULTS

We identified 186 TAMs marker genes, and a total of 6 optimal prognostic genes including CKS2, LITAF, CTSB, TWISTNB, PPIF and G0S2 were selected to construct a TAMRG-score. The high TAMRG-score was significantly associated with worse prognosis (log-rank test, P<0.001). Moreover, the TAMRG-score outperformed the other three models with AUC of 0.808. Immune cell infiltration, TME scores, immune checkpoints, TMB and drug susceptibility were significantly different between TAMRG-score groups. In addition, a nomogram were constructed by combing the TAMRG-score and clinical information (Age, Grade, IDH mutation and 1p19q codeletion) to predict the survival of glioma patients with AUC of 0.909 for 1-year survival.

CONCLUSION

The high TAMRG-score group was associated with a poor prognosis. A nomogram by incorporating TMARG-score could precisely predict glioma survival, and provide evidence for personalized treatment of glioma.

摘要

背景

本研究旨在通过整合单细胞 RNA 测序（scRNA-seq）和批量 RNA 测序（bulk RNA-seq）数据，构建并验证基于肿瘤相关巨噬细胞相关基因（TAMRGs）的预后模型。

方法

使用三个内部脑胶质瘤组织的 scRNA-seq 数据来鉴定肿瘤相关巨噬细胞（TAMs）标记基因，从 The Cancer Genome Atlas（TCGA）-Genotype-Tissue Expression（GTEx）数据集的 DEGs 中进一步选择 TAMs 标记基因。随后，通过 TCGA 数据集的最小绝对收缩和选择算子（LASSO）回归和多变量 Cox 回归分析构建 TAMRG-评分，并在 Chinese Glioma Genome Atlas（CGGA）数据集进行验证。

结果

我们鉴定了 186 个 TAMs 标记基因，共选择了 6 个最佳预后基因，包括 CKS2、LITAF、CTSB、TWISTNB、PPIF 和 G0S2，构建了 TAMRG-评分。高 TAMRG-评分与预后不良显著相关（对数秩检验，P<0.001）。此外，TAMRG-评分的 AUC 优于其他三个模型，为 0.808。TAMRG-评分组之间的免疫细胞浸润、TME 评分、免疫检查点、TMB 和药物敏感性有显著差异。此外，通过结合 TAMRG-评分和临床信息（年龄、分级、IDH 突变和 1p19q 共缺失）构建了一个列线图，以预测胶质瘤患者的生存情况，1 年生存率的 AUC 为 0.909。