Lu Xiangdong, Zhou Zijian, Qiu Peng, Xin Tao
Jiangxi Medical College, Nanchang University, Nanchang, 330031, Jiangxi, China.
Department of Neurosurgery, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, 330006, Jiangxi, China.
Heliyon. 2024 May 6;10(9):e30726. doi: 10.1016/j.heliyon.2024.e30726. eCollection 2024 May 15.
Glioma, the most common and aggressive form of brain cancer, possesses a complex biology, which makes elucidating its underlying mechanisms and developing effective treatment strategies challenging. Lactylation is a recently discovered post-translational modification and has emerged as a novel research target to understand its role in various biological processes and diseases. Herein, we explored the role of lactylation in gliomas.
Single-cell RNA-sequencing (scRNA-seq) data were downloaded from the Tumour Immune Single-Cell Hub database. The R package 'Seurat' was used for processing the scRNA-seq data. Lactylation-related genes were identified from published literature and the Molecular Signatures Database. An unsupervised clustering method was used to identify glioma subtypes based on identified lactylation-related genes. Differences among the various clusters were examined, including clinical features, differentially expressed genes (DEGs), enriched pathways and immune cell infiltrates. A lactylation score was generated to predict the overall survival (OS) of patients with glioma using DEGs between the two clusters.
The lactylation-related genes were obtained from the scRNA-seq data, identifying two molecular subtypes, and a prognostic signature was established to stratify patients with glioma into high- and low-score groups. Analysis of the tumour immune microenvironment revealed that patients in the high-score group exhibited increased immune cell infiltration, chemokine expression and immune checkpoint expression but exhibited worse OS and better response to immunotherapy.
Altogether, we established a novel signature based on lactylation-related clusters for robust survival prediction and immunotherapeutic response in gliomas.
神经胶质瘤是最常见且侵袭性最强的脑癌形式，其生物学特性复杂，这使得阐明其潜在机制并制定有效的治疗策略具有挑战性。乳酰化是最近发现的一种翻译后修饰，已成为理解其在各种生物学过程和疾病中作用的一个新的研究靶点。在此，我们探讨了乳酰化在神经胶质瘤中的作用。
从肿瘤免疫单细胞中心数据库下载单细胞RNA测序（scRNA-seq）数据。使用R包“Seurat”处理scRNA-seq数据。从已发表的文献和分子特征数据库中鉴定乳酰化相关基因。采用无监督聚类方法，基于鉴定出的乳酰化相关基因识别神经胶质瘤亚型。检查了不同聚类之间的差异，包括临床特征、差异表达基因（DEGs）、富集通路和免疫细胞浸润情况。利用两个聚类之间的DEGs生成乳酰化评分，以预测神经胶质瘤患者的总生存期（OS）。
从scRNA-seq数据中获得了乳酰化相关基因，识别出两种分子亚型，并建立了一个预后特征，将神经胶质瘤患者分为高分和低分两组。对肿瘤免疫微环境的分析表明，高分患者组免疫细胞浸润增加、趋化因子表达和免疫检查点表达增加，但总生存期较差，对免疫治疗反应较好。
总之，我们基于乳酰化相关聚类建立了一种新的特征，用于对神经胶质瘤进行可靠的生存预测和免疫治疗反应评估。
