Glioma Diagnosis and Treatment

The paper identifies six differentially expressed genes in glioblastoma multiforme, highlighting FPR2 as a potential immunotherapeutic target.

The paper identifies differentially expressed genes and pathways in glioblastoma multiform using genomic data and validates hub genes for potential biomarkers.

The paper develops a 4-layer neural network to predict GBM prognosis and radiosensitivity using CUL2 copy number, patient age, and SvV ratio with 80-85% accuracy.

This study identifies MET gene alterations in gliomas and shows that these alterations are associated with better response to MET inhibitors.

The paper finds that higher cerebral blood volume in infiltrated peripheral edema is associated with the proneural glioblastoma subtype.

This study shows that purine metabolism affects DNA repair and therapy resistance in glioblastoma, suggesting that targeting purine synthesis could improve treatment outcomes.

This study identifies biomarker genes and potential drugs for G3 medulloblastoma using integrative omics analysis of gene expression, methylation, and copy number data.

This study tracks glioma progression in mice, showing decreased neuronal synchrony, disrupted neurovascular coupling, and seizure-related hypoxia in infiltrated cortex.

This study identifies PRKCG as a glioma biomarker through multi-omics integration, showing its association with glioma progression and survival outcomes when combined with MGMT.

This study compares tumor cell phenotype and heterogeneity differences between IDH1 mutant and wild-type gliomas using integrated multi-omics data.

This study analyzes mutation distribution in GBM tumors using 3D spatial organization and network-guided methods, revealing four patient clusters with distinct mutation patterns and pathways.

This study evaluates intraoperative contrast-enhanced ultrasound (iCEUS) for monitoring glioma resection and its correlation with microvessel density across glioma grades.

The paper identifies four molecularly distinct glioblastoma subtypes using single-cell protein-activity analysis and shows drug sensitivity is state-dependent.

The paper quantifies low perfusion compartments in glioblastoma using MRI and correlates them with patient survival outcomes.

The paper compares machine learning and logistic regression for predicting 2-year mortality in a small glioma dataset, finding similar performance between ML methods and logistic regression.

The paper analyzes immune cell abundance in brain cancer using gene expression and copy number data from the REMBRANDT project to compare with TCGA datasets and assess clinical relevance.

The paper develops a 39-gene signature to stratify anaplastic gliomas into two subgroups with distinct survival outcomes, validated in an external dataset.

The paper investigates copy number loss of the ERBB4 gene in glioblastoma, finding it is not a driver mutation or predisposing factor, despite initial hypotheses from cell line data.

This paper integrates multi-parametric and multi-regional MRI histogram features to identify patient subgroups with distinct survival outcomes in glioblastoma.

This study shows that targeting Nlgn3 secretion via ADAM10 inhibitors can block high-grade glioma growth in mouse models.

This study uses an AAV-mediated CRISPR screen in mice to identify functional suppressors of glioblastoma, revealing novel and canonical tumor suppressors and their interactions.

The paper trains a machine learning classifier on cancer transcriptomes to detect NF1 inactivation in glioblastoma, showing it can predict NF1 status based on gene expression and mutation data.