Integrative analysis of polyamine metabolism-related genes in gliomas: implications for prognosis and therapy

Introduction: Tumor transformation and progression are accompanied by multiple carcinogenic pathways that dysregulate polyamine demand and metabolism. The importance of polyamines has demonstrated that their metabolism is a potential therapeutic strategy. Yet, few prognostic models based on polyamine metabolism-related gene risk have been developed for gliomas. Methods: The mRNA expression profiles and variations in 37 polyamine metabolism-related genes (PMRGs) were obtained from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. PMRGs-related risk model was constructed by least absolute shrinkage and selection operator (LASSO) Cox regression and tested for predictive ability across two independent datasets from the Gene Expression Omnibus (GEO). The landscape of the tumor immune microenvironment and drug sensitivity were investigated systematically using multiple methods based on PMRG-related risk subtypes. Weighted gene co-expression network analysis (WGCNA) was applied to identify the key prognostic genes of the PMRGs. In addition, key genes were validated with regard to their expression and prognostic significance in human glioma tissues. To verify the cell types, single-cell RNA sequencing was performed on the cohorts available at GEO. Results: Based on PMRG clusters, patients with glioma showed significant differences in PMRG expression, prognosis, and biological functions. A 11-gene risk model was constructed, and patients were categorized into high- and low-risk subtype according to the risk score. The high-risk subtype exhibited a poorer prognosis due to its immunosuppressive microenvironment. Furthermore, there were striking differences between the distinct subtypes in terms of immune cell infiltration, anticancer immunity cycle, tumor mutation burden, immune checkpoints, and response to targeted inhibitors. Spermine synthase (SMS) was identified as a key PMRG in patients with gliomas. A significant increase in SMS mRNA and protein expression was observed in tumors compared to normal controls. Single-cell sequencing analyses showed that SMS mRNA was highly expressed in all cell types, except oligodendrocytes. Conclusion: A PMRG-related risk model can be used as a reliable prognostic biomarker in glioma treatment. In addition, polyamine metabolism and function can be successfully targeted therapeutically.