Abstract
BACKGROUND: Medulloblastoma (MB) is a common central nervous system malignancy in children, and its relationship with lactate metabolism has become an important area of cancer research in recent years, especially in metabolic reprogramming. This study aimed to determine the effects of lactate metabolism-related genes in the biological mechanisms involved in MB. METHODS: A single-cell analysis was performed on the GEO dataset (GSE155446) in order to analyse the lactate metabolism-related characteristics of differing MB cell populations. Following this, MB cells were divided according to their lactate metabolism-related characteristics, and further developmental trajectories between MB subsets were analyzed. Further studies encompassed cell communication and pathway analysis to elucidate their function and association with immune cells. Additionally, a MB-related bulk dataset (GSE85217) was procured for machine learning-based identification of core lactate-metabolism related genes, with the objective of gaining novel insights into clinical diagnosis and therapeutic targets. RESULTS: In light of the findings from the scRNA-seq analysis, three primary cell types were identified. It was found that cells in MB clusters exhibited distinct biological functions. Metabolic analysis of MB clusters revealed heterogeneity in glycolysis, gluconeogenesis, and lactate metabolism reprogramming. The lactate-metabolism related genes score (LMRGs score) was found to be significantly elevated in MB clusters. Furthermore, differential expression analysis among MB cell clusters revealed distinct metabolic profiles. The Random Forest learning method confirmed that COX4I1, CYC1, MECP2, MYC, NDUFAF3, NDUFS3, COX20, CALR, POMT1 and C1QBP are highly expressed in MB. CONCLUSIONS: The results of the present study demonstrate a close association between lactate-metabolism-related genes and their functions, and the development of MB.