Warburg effect-related risk scoring model to assess clinical significance and immunity characteristics of glioblastoma.

Abstract

Glioblastoma (GBM), the most common primary malignant brain tumor, has a poor prognosis, with a median survival of only 14.6 months. The Warburg effect is an abnormal energy metabolism, which is the main cause of the acidic tumor microenvironment. This study explored the role of the Warburg effect in the prognosis and immune microenvironment of GBM. A prognostic risk score model of Warburg effect-related genes (Warburg effect signature) was constructed using GBM cohort data from The Cancer Genome Atlas. Cox analysis was performed to identify independent prognostic factors. Next, the nomogram was built to predict the prognosis for GBM patients. Finally, the drug sensitivity analysis was utilized to find the drugs that specifically target Warburg effect-related genes. Age, radiotherapy, chemotherapy, and WRGs score were confirmed as independent prognostic factors for GBM by Cox analyses. The C-index (0.633 for the training set and 0.696 for the validation set) and area under curve (>0.7) indicated that the nomogram exhibited excellent performance. The calibration curve also indicates excellent consistency of the nomogram between predictions and actual observations. In addition, immune microenvironment analysis revealed that patients with high WRGs scores had high immunosuppressive scores, a high abundance of immunosuppressive cells, and a low response to immunotherapy. The Cell Counting Kit-8 assays showed that the drugs targeting Warburg effect-related genes could inhibit the GBM cells growth invitro. Our research showed that the Warburg effect is connected with the prognosis and immune microenvironment of GBM. Therefore, targeting Warburg effect-related genes may provide novel therapeutic options.