Identification and validation of a glycolysis-related taxonomy for improving outcomes in glioma

Reprogramming of glucose metabolism is a prominent abnormal energy metabolism in glioma. However, the efficacy of treatments targeting glycolysis varies among patients. The present study aimed to classify distinct glycolysis subtypes (GS) of glioma, which may help to improve the therapy response.

The expression profiles of glioma were downloaded from public datasets to perform an enhanced clustering analysis to determine the GS. A total of 101 combinations based on 10 machine learning algorithms were performed to screen out the most valuable glycolysis-related glioma signature (GGS). Through RSF and plsRcox algorithms, adrenomedullin (ADM) was eventually obtained as the most significant glycolysis-related gene for prognostic prediction in glioma. Furthermore, drug sensitivity analysis, molecular docking, and in vitro experiments were utilized to verify the efficacy of ADM and ingenol mebutate (IM).

Glioma patients were classified into five distinct GS (GS1-GS5), characterized by varying glycolytic metabolism levels, molecular expression, immune cell infiltration, immunogenic modulators, and clinical features. Anti-CTLA4 and anti-PD-L1 antibodies significantly improved the prognosis for GS2 and GS5, respectively. ADM has been identified as a potential biomarker for targeted glycolytic therapy in glioma patients. In vitro experiments demonstrated that IM inhibited glioma cell progression by inhibiting ADM.

This study elucidates that evaluating GS is essential for comprehending the heterogeneity of glioma, which is pivotal for predicting immune cell infiltration (ICI) characterization, prognosis, and personalized immunotherapy regimens. We also explored the glycolysis-related genes ADM and IM to develop a theoretical framework for anti-tumor strategies targeting glycolysis.