Wen Wenjie, Chen Jiongxue, Deng Fuyin, Guo Daji, Zuo You, Chen Xuewen, Li Youjia, Li Yi, Tang Yamei
Department of Neurology, Sun Yat-Sen Memorial Hospital, Sun Yat‑sen University, Guangzhou, China.
Brain Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat‑sen University, Guangzhou, China.
Mol Biotechnol. 2025 May 23. doi: 10.1007/s12033-025-01446-0.
Glioblastoma multiforme, one of the most malignant types of brain tumor, heavily relies on glycolytic pathways and is significantly influenced by immune infiltration and its surrounding microenvironment. Growing evidence implies that increase in glycolysis can lead to lactate accumulation, which further contributed to histone lactylation, playing a crucial role in tumor development, maintenance, and therapeutic response. This study explores the prognostic and therapeutic potential of lactylation-related genes in glioblastoma multiforme. Using single-cell (GSE162631) and bulk transcriptome datasets (TCGA, CGGA, and GSE16011), we identified lactylation-related genes through ssGSEA and WGCNA. Moreover, a machine learning framework, incorporating 10 algorithms and 101 combinations, was used to establish an eight-gene lactylation-related signature (POLDIP3, MMP14, MDK, KDELR2, GSTK1, DEDD2, CD151, and BRI3) with robust predictive accuracy for patient survival. A nomogram with lactylation-related signature integration was developed as a quantitative prognostic instrument for clinical use. Moreover, patients classified by lactylation-related signature risk scores showed distinct immune status, tumor mutation burden, immunotherapy response, and drug sensitivity. The expression of those lactylation-related genes was further validated by quantitative PCR and functional experiment in normal and GBM cell lines. Overall, this study establishes a lactylation-related signature with significant potential for glioblastoma multiforme prognostic prediction, targeted prevention, and individualized therapy.
多形性胶质母细胞瘤是最恶性的脑肿瘤类型之一，严重依赖糖酵解途径，并受到免疫浸润及其周围微环境的显著影响。越来越多的证据表明，糖酵解增加会导致乳酸积累，这进一步促进了组蛋白乳酸化，在肿瘤的发生、维持和治疗反应中发挥关键作用。本研究探讨了乳酸化相关基因在多形性胶质母细胞瘤中的预后和治疗潜力。利用单细胞（GSE162631）和批量转录组数据集（TCGA、CGGA和GSE16011），我们通过单样本基因集富集分析（ssGSEA）和加权基因共表达网络分析（WGCNA）鉴定了乳酸化相关基因。此外，使用一个包含10种算法和101种组合的机器学习框架，建立了一个与乳酸化相关的八基因特征（POLDIP3、MMP14、MDK、KDELR2、GSTK1、DEDD2、CD151和BRI3），对患者生存具有强大的预测准确性。开发了一个整合乳酸化相关特征的列线图作为临床使用的定量预后工具。此外，根据乳酸化相关特征风险评分分类的患者表现出不同的免疫状态、肿瘤突变负荷、免疫治疗反应和药物敏感性。通过定量PCR和功能实验在正常和胶质母细胞瘤细胞系中进一步验证了这些乳酸化相关基因的表达。总体而言，本研究建立了一个与乳酸化相关的特征，在多形性胶质母细胞瘤的预后预测、靶向预防和个体化治疗方面具有显著潜力。
