A novel inositol phosphate metabolism signature predicts survival prognosis in low grade glioma

Low grade gliomas (LGGs) are heterogeneous tumors with high recurrence rates and variable clinical outcomes. However, metabolic dysregulation within LGG and its prognosis and tumor pathology remain poorly understood. We investigated the significance of inositol phosphate metabolism (IPM) in LGG and constructed an integrated machine learning survival model to improve the prognostic accuracy. By examining the transcriptomic data of LGGs, we identified a prognostic IPM (pIPM) signature consisting of 33 genes, with PLCG1 and MTMR3 showing the highest and lowest hazard ratios, respectively. Subsequent consensus clustering analysis revealed two distinct clusters (C1 and C2) with different pIPM expression levels and overall survival outcomes. Furthermore, IPM pathway analysis revealed an overall poor prognosis except for MGAT4C, suggesting that IPM plays a key role in driving LGG progression. Using machine learning algorithms, we developed and validated a robust survival model based on pIPM, in which Lasso model showed excellent discriminative power and reliability on both training and validation datasets. Furthermore, an increased IPM risk score was associated with increased cell proliferation and inflammation. Remarkably, the score was linked to treatment response, tumor recurrence, and remodeling of the immune microenvironment, emphasizing its potential as both a prognostic biomarker and a therapeutic target for LGG. In conclusion, our findings revealed a complex involvement of phosphatidylinositol metabolism in the LGG pathogenesis and elucidated its clinical significance for prognostic prediction and therapeutic intervention.