Although the treatment of brain tumors by targeting kinase-regulated macroautophagy/autophagy, is under investigation, the precise mechanism underlying autophagy initiation and its significance in glioblastoma (GBM) remains to be defined. Here, we report that PAK1 (p21 [RAC1] activated kinase 1) is significantly upregulated and promotes GBM development. The Cancer Genome Atlas analysis suggests that the oncogenic role of PAK1 in GBM is mainly associated with autophagy. Subsequent experiments demonstrate that PAK1 indeed serves as a positive modulator for hypoxia-induced autophagy in GBM. Mechanistically, hypoxia induces ELP3-mediated PAK1 acetylation at K420, which suppresses the dimerization of PAK1 and enhances its activity, thereby leading to subsequent PAK1-mediated ATG5 (autophagy related 5) phosphorylation at the T101 residue. This event not only protects ATG5 from ubiquitination-dependent degradation but also increases the affinity between the ATG12–ATG5 complex and ATG16L1 (autophagy related 16 like 1). Consequently, ELP3-dependent PAK1 (K420) acetylation and PAK1-mediated ATG5 (T101) phosphorylation are required for hypoxia-induced autophagy and brain tumorigenesis by promoting autophagosome formation. Silencing PAK1 with shRNA or small molecule inhibitor FRAX597 potentially blocks autophagy and GBM growth. Furthermore, SIRT1-mediated PAK1-deacetylation at K420 hinders autophagy and GBM growth. Clinically, the levels of PAK1 (K420) acetylation significantly correlate with the expression of ATG5 (T101) phosphorylation in GBM patients. Together, this report uncovers that the acetylation modification and kinase activity of PAK1 plays an instrumental role in hypoxia-induced autophagy initiation and maintaining GBM growth. Therefore, PAK1 and its regulator in the autophagy pathway might represent potential therapeutic targets for GBM treatment. Abbreviations: 3-MA: 3-methyladenine; Ac-CoA: acetyl coenzyme A; ATG5: autophagy related 5; ATG16L1, autophagy related 16 like 1; BafA₁: bafilomycin A₁; CDC42: cell division cycle 42; CGGA: Chinese Glioma Genome Atlas; CHX, cycloheximide; ELP3: elongator acetyltransferase complex subunit 3; GBM, glioblastoma; HBSS: Hanks balanced salts solution; MAP1LC3B/LC3: microtubule associated protein 1 light chain 3 beta; MAP2K1: mitogen-activated protein kinase kinase 1; MAPK14, mitogen-activated protein kinase 14; PAK1: p21 (RAC1) activated kinase 1; PDK1: pyruvate dehydrogenase kinase 1; PGK1, phosphoglycerate kinase 1; PTMs: post-translational modifications; RAC1: Rac family small GTPase 1; SQSTM1: sequestosome 1; TCGA, The Cancer Genome Atlas.

P53 mutation status is a pivotal biomarker for gliomas. Here, we developed a machine-learning model to predict p53 status in lower-grade gliomas based on radiomic features extracted from conventional magnetic resonance (MR) images. Preoperative MR images were retrospectively obtained from 272 patients with primary grade II/III gliomas. The patients were randomly allocated in a 2:1 ratio to a training (n = 180) or validation (n = 92) set. A total of 431 radiomic features were extracted from each patient. The lest absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomic signature construction. Subsequently, a machine-learning model to predict p53 status was established using the selected features and a Support Vector Machine classifier. The predictive performance of all individual features and the model was calculated using receiver operating characteristic curves in both the training and validation sets. The p53-related radiomic signature was built using the LASSO algorithm; this procedure consisted of four first-order statistics or related wavelet features (including Maximum, Median, Minimum, and Uniformity), a shape and size-based feature (Spherical Disproportion), and ten textural features or related wavelet features (including Correlation, Run Percentage, and Sum Entropy). The prediction accuracies based on the area under the curve were 89.6% in the training set and 76.3% in the validation set, which were better than individual features. These results demonstrate that MR image texture features are predictive of p53 mutation status in lower-grade gliomas. Thus, our procedure can be conveniently used to facilitate presurgical molecular pathological diagnosis.

Intraoperative histology is essential for surgical guidance and decision-making. However, frozen-sectioned hematoxylin and eosin (H&E) staining suffers from degraded accuracy, whereas the gold-standard formalin-fixed and paraffin-embedded (FFPE) H&E is too lengthy for intraoperative use. Stimulated Raman scattering (SRS) microscopy has shown rapid histology of brain tissue with lipid/protein contrast but is challenging to yield images identical to nucleic acid–/protein-based FFPE stains interpretable to pathologists. Here, we report the development of a semi-supervised stimulated Raman CycleGAN model to convert fresh-tissue SRS images to H&E stains using unpaired training data. Within 3 minutes, stimulated Raman virtual histology (SRVH) results that matched perfectly with true H&E could be generated. A blind validation indicated that board-certified neuropathologists are able to differentiate histologic subtypes of human glioma on SRVH but hardly on conventional SRS images. SRVH may provide intraoperative diagnosis superior to frozen H&E in both speed and accuracy, extendable to other types of solid tumors.

Background: Mutations in isocitrate dehydrogenase (IDH) affect the development and prognosis of gliomas. We investigated the role of IDH mutations in the regulation of immune phenotype in lower-grade gliomas (LGGs).Method and patients: A total of 1,008 cases with clinical and IDH mutation data from five cohorts were enrolled. Samples with RNA sequencing data from the Chinese Glioma Genome Atlas (CGGA) were used as training set, whereas RNA data from the Cancer Genome Atlas, Repository for Molecular Brain Neoplasia, GSE16011, and CGGA microarray databases were used for validation. R language tools and bioinformatics analysis were used for gene signature construction and biological function annotation.Results: We found that IDH mutations caused down-regulation of local immune response as among 332 immune system-related genes, 196(59.0%) were differentially expressed according to IDH mutation status. Nearly 70% of those differentially expressed genes exhibited prognostic value in LGGs. An immune response-based gene signature was constructed that distinguished cases with high- or low-risk of unfavorable prognosis and remained an independent prognostic factor in multivariate analyses in both training and validation cohorts. Samples from high-risk cases exhibited elevated expression of genes involved in immune response and NF-κB pathway activation. Furthermore, we found a strong correlation between the risk score and T cells, macrophage-related immune response, and expression of several prominent immune checkpoints.Conclusion: Our results indicated that mutant IDH is highly associated with the regulation of the immune microenvironment in LGGs. The observed immune system gene signature, which was sensitive to IDH mutation status, efficiently predicted patient survival.