Li Qiong, Liu Hongde
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 211189, China.
Int J Mol Sci. 2025 Feb 21;26(5):1875. doi: 10.3390/ijms26051875.
Glioblastoma (GBM) is the most aggressive primary brain cancer, with poor prognosis due to its aggressive behavior and high heterogeneity. This study aimed to identify cellular senescence (CS) and lipid metabolism (LM)-related prognostic genes to improve GBM prognosis and treatment. Transcriptome and scRNA-seq data, CS-associated genes (CSAGs), and LM-related genes (LMRGs) were acquired from public databases. Prognostic genes were identified by intersecting CSAGs, LMRGs, and differentially expressed genes (DEGs), followed by WGCNA and univariate Cox regression. A risk model and nomogram were constructed. Analyses covered clinicopathological features, immune microenvironment, somatic mutations, and drug sensitivity. GBM scRNA-seq data identified key cells and prognostic gene expression. SOCS1 and PHB2 were identified as prognostic markers, contributing to the construction of a robust risk model with excellent predictive ability. High-risk group (HRG) patients had poorer survival, higher immune and stromal scores, and distinct somatic mutation profiles. Drug sensitivity analysis revealed significant differences in IC50 values. In microglia differentiation, SOCS1 and PHB2 showed dynamic expression patterns. These findings provide new strategies for GBM prognosis and treatment.
胶质母细胞瘤（GBM）是最具侵袭性的原发性脑癌，因其侵袭性和高度异质性而预后较差。本研究旨在鉴定与细胞衰老（CS）和脂质代谢（LM）相关的预后基因，以改善GBM的预后和治疗。从公共数据库获取转录组和单细胞RNA测序（scRNA-seq）数据、CS相关基因（CSAGs）和LM相关基因（LMRGs）。通过交叉CSAGs、LMRGs和差异表达基因（DEGs），随后进行加权基因共表达网络分析（WGCNA）和单变量Cox回归来鉴定预后基因。构建了风险模型和列线图。分析涵盖临床病理特征、免疫微环境、体细胞突变和药物敏感性。GBM的scRNA-seq数据确定了关键细胞和预后基因表达。信号转导子和转录激活子1（SOCS1）和27kDa热休克蛋白（PHB2）被鉴定为预后标志物，有助于构建具有出色预测能力的稳健风险模型。高危组（HRG）患者的生存率较低、免疫和基质评分较高，且具有独特的体细胞突变谱。药物敏感性分析显示半数抑制浓度（IC50）值存在显著差异。在小胶质细胞分化过程中，SOCS1和PHB2呈现出动态表达模式。这些发现为GBM的预后和治疗提供了新策略。
