单细胞测序与加权共表达网络的综合分析基于细胞衰老相关基因鉴定出一种预测胶质母细胞瘤预后的新特征。

Integrated analysis of single-cell sequencing and weighted co-expression network identifies a novel signature based on cellular senescence-related genes to predict prognosis in glioblastoma.

作者信息

Bao Qingquan, Yu Xuebin, Qi Xuchen

机构信息

Department of Neurosurgery, Shaoxing People's Hospital, Shaoxing, China.

Department of Neurosurgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

出版信息

Environ Toxicol. 2024 Feb;39(2):643-656. doi: 10.1002/tox.23921. Epub 2023 Aug 11.

DOI:10.1002/tox.23921

PMID:37565732

Abstract

BACKGROUND

Glioblastoma (GBM) is a highly aggressive cancer with heavy mortality rates and poor prognosis. Cellular senescence exerts a pivotal influence on the development and progression of various cancers. However, the underlying effect of cellular senescence on the outcomes of patients with GBM remains to be elucidated.

METHODS

Transcriptome RNA sequencing data with clinical information and single-cell sequencing data of GBM cases were obtained from CGGA, TCGA, and GEO (GSE84465) databases respectively. Single-sample gene set enrichment analysis (ssGSEA) analysis was utilized to calculate the cellular senescence score. WGCNA analysis was employed to ascertain the key gene modules and identify differentially expressed genes (DEGs) associated with the cellular senescence score in GBM. The prognostic senescence-related risk model was developed by least absolute shrinkage and selection operator (LASSO) regression analyses. The immune infiltration level was calculated by microenvironment cell populations counter (MCPcounter), ssGSEA, and xCell algorithms. Potential anti-cancer small molecular compounds of GBM were estimated by "oncoPredict" R package.

RESULTS

A total of 150 DEGs were selected from the pink module through WGCNA analysis. The risk-scoring model was constructed based on 5 cell senescence-associated genes (CCDC151, DRC1, C2orf73, CCDC13, and WDR63). Patients in low-risk group had a better prognostic value compared to those in high-risk group. The nomogram exhibited excellent predictive performance in assessing the survival outcomes of patients with GBM. Top 30 potential anti-cancer small molecular compounds with higher drug sensitivity scores were predicted.

CONCLUSION

Cellular senescence-related genes and clusters in GBM have the potential to provide valuable insights in prognosis and guide clinical decisions.

摘要

背景

胶质母细胞瘤（GBM）是一种侵袭性很强的癌症，死亡率高且预后较差。细胞衰老对各种癌症的发生和发展具有关键影响。然而，细胞衰老对GBM患者预后的潜在影响仍有待阐明。

方法

分别从CGGA、TCGA和GEO（GSE84465）数据库中获取GBM病例的转录组RNA测序数据及临床信息和单细胞测序数据。利用单样本基因集富集分析（ssGSEA）计算细胞衰老评分。采用加权基因共表达网络分析（WGCNA）确定关键基因模块，并识别与GBM细胞衰老评分相关的差异表达基因（DEG）。通过最小绝对收缩和选择算子（LASSO）回归分析建立预后衰老相关风险模型。通过微环境细胞群体计数器（MCPcounter）、ssGSEA和xCell算法计算免疫浸润水平。利用“oncoPredict”R包评估GBM潜在的抗癌小分子化合物。

结果

通过WGCNA分析从粉色模块中筛选出150个DEG。基于5个细胞衰老相关基因（CCDC151、DRC1、C2orf73、CCDC13和WDR63）构建风险评分模型。低风险组患者的预后价值高于高风险组患者。该列线图在评估GBM患者生存结局方面表现出优异的预测性能。预测出30种药物敏感性评分较高的潜在抗癌小分子化合物。