Glioblastoma multiforme (GBM) is a devastating brain tumour without effective treatment. Recent studies have shown that autophagy is a promising therapeutic strategy for GBM. Therefore, it is necessary to identify novel biomarkers associated with autophagy in GBM. In this study, we downloaded autophagy-related genes from Human Autophagy Database (HADb) and Gene Set Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were performed to identify genes for constructing a risk signature. A nomogram was developed by integrating the risk signature with clinicopathological factors. Time-dependent receiver operating characteristic (ROC) curve and calibration plot were used to evaluate the efficiency of the prognostic model. Finally, four autophagy-related genes (DIRAS3, LGALS8, MAPK8 and STAM)were identified and were used for constructing a risk signature, which proved to be an independent risk factor for GBM patients. Furthermore, a nomogram was developed based on the risk signature and clinicopathological factors (IDH1 status, age and history of radiotherapy or chemotherapy). ROC curve and calibration plot suggested the nomogram could accurately predict 1-, 3-and 5-year survival rate of GBM patients.For function analysis, the risk signature was associated with apoptosis, necrosis, immunity, inflammation response and MAPK signalling pathway. In conclusion, the risk signature with 4 autophagy-related genes could serve as an independent prognostic factor for GBM patients. Moreover, we developed a nomogram based on the risk signature and clinical traits which was validated to perform better for predicting 1-, 3-and 5-year survival rate of GBM. K E Y W O R D Sautophagy, glioblastoma multiform, nomogram, prognosis, risk signature

Background Immunosuppressive microenvironment is a major cause of immunotherapeutic resistance in glioma. In addition to secreting compounds, tumor cells under programmed cell death (PCD) processes release abundant mediators to modify the neighboring microenvironment. However, the complex relationship among PCD status, immunosuppressive microenvironment and immunotherapy is still poorly understood. Methods Four independent glioma cohorts comprising 1,750 patients were enrolled for analysis. The relationships among PCD status, microenvironment cellular components and biological phenotypes were fully explored. Tissues from our hospital and experiments in vitro and in vivo were used to confirm the role of ferroptosis in glioma. Results Analyses to determine enriched PCD processes showed that ferroptosis was the main type of PCD in glioma. Enriched ferroptosis correlated with progressive malignancy, poor outcomes and aggravated immunosuppression in glioblastoma (GBM) patients. Enhanced ferroptosis was shown to induce activation and infiltration of immune cells but attenuated antitumor cytotoxic killing. Tumor-associated macrophages (TAMs) were found to participate in ferroptosis-mediated immunosuppression. Preclinically, ferroptosis inhibition combined with PD-1/L1 blockade generated a synergistic therapeutic outcome in GBM murine models. Conclusions This work provides a molecular, clinical and biological landscape of ferroptosis, suggesting a role of ferroptosis in glioma malignancy and a novel synergic immunotherapeutic strategy that combines immune checkpoint blockade (ICB) treatment with ferroptosis inhibition.

Objective: Glioblastoma (GBM) is the most common and fatal primary brain tumor in adults. It is necessary to identify novel and effective biomarkers or risk signatures for GBM patients. Methods: Differentially expressed genes (DEGs) between GBM and low-grade glioma (LGG) in TCGA samples were screened out and weight correlation network analysis (WGCNA) was performed to confirm WHO grade-related genes. Five genes were selected via multivariate Cox proportional hazards regression analysis and were used to construct a risk signature. A nomogram composed of the risk signature and clinical characters (age, radiotherapy, and chemotherapy experience) was established to predict 1, 3, 5-year survival rate for GBM patients. Results: One hundred ninety-four DEGs in blue gene module were found to be positively related to WHO grade via WGCNA. Five genes (DES, RANBP17, CLEC5A, HOXC11, POSTN) were selected to construct a risk signature for GBM via R language. This risk signature was identified to independently predict the outcome of GBM patients, as well as stratified by IDH1 status, MGMT promoter status, and radio-chemotherapy. The nomogram was established which combined the risk signature with clinical factors. The results of c-index, ROC curve and calibration plot revealed the nomogram showing a good accuracy for predicting 1, 3, or 5-year survival of GBM patients. Conclusion: The risk signature with five genes could serve as an independent factor for predicting the prognosis of patients with GBM. Moreover, the nomogram with the risk signature and clinical traits proved to perform better for predicting 1, 3, 5-year survival rate.