Integrated single-cell RNA sequencing and Bulk-RNA technologies reveal the immunological characteristics of lactylation related-genes in glioblastoma

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.