Objective Glioblastoma (GBM) is the most aggressive type of intracranial malignant tumor, known for its extremely poor prognosis. Lactylation, a newly identified post-translational modification, has been linked to tumorigenesis, though its specific role in GBM remains unclear. This study aims to integrate single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to create a novel prognostic model for GBM, focusing on lactylation-related factors. Methods We studied lactate metabolism genes as markers in GBM. We obtained bulk transcriptomic data from TCGA and the GSE141383 and GSE162631 cohorts in the GEO databases. We used the R package Seurat to analyze scRNA-seq data, CellChat for cell communication analysis, and AUCell to assess lactate metabolism gene set scores across cell types. We developed a prognostic model using machine learning algorithms and tested its efficacy across multiple cohorts. Additionally, we investigated differences in immune infiltration, predicted sensitivity, and other factors between high and low-risk groups. We validated the function of the key gene CD93 at the cellular level. Results The scRNA-seq data identified nine major cell types in GBM, with FCGBP+ macrophages showing the highest score in the lactate metabolism gene set. Authors designed a model informed by machine learning pinpointed three key genes（CD93, FCER1G, and GRB2）and developed a model with optimal prognostic value across cohorts.The high-risk group presented significantly poorer clinical outcomes. Immune-related bioinformatic analysis revealed significant differences in immune cell infiltration and checkpoint gene expression between risk groups. High-risk patients demonstrated lower immune infiltration and higher immunosuppression, rendering them less suitable for immunotherapy. Predictive algorithms indicated that axitinib and imatinib could be potential therapeutic drugs for these high-risk patients. In GBM tissue and cells, CD93 expression was significantly elevated, identifying it as a key risk gene in this model. Inhibition of CD93 expression via siRNA significantly reduced the proliferation, invasion, and migration of U87 and U251 cells. Conclusion In summary, we developed a novel characterization of lactylation-related clusters using single-cell sequencing technology. This study provided insights into the prognostic significance of lactate metabolism-related genes in GBM.
Wang et al. (Fri,) studied this question.
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