Mutational Landscape of Secondary Glioblastoma Guides MET-Targeted Trial in Brain Tumor.

Low-grade gliomas almost invariably progress into secondary glioblastoma (sGBM) with limited therapeutic option and poorly understood mechanism. By studying the mutational landscape of 188 sGBMs, we find significant enrichment of TP53 mutations, somatic hypermutation, MET-exon-14-skipping (METex14), PTPRZ1-MET (ZM) fusions, and MET amplification. Strikingly, METex14 frequently co-occurs with ZM fusion and is present in ∼14% of cases with significantly worse prognosis. Subsequent studies show that METex14 promotes glioma progression by prolonging MET activity. Furthermore, we describe a MET kinase inhibitor, PLB-1001, that demonstrates remarkable potency in selectively inhibiting MET-altered tumor cells in preclinical models. Importantly, this compound also shows blood-brain barrier permeability and is subsequently applied in a phase I clinical trial that enrolls MET-altered chemo-resistant glioma patients. Encouragingly, PLB-1001 achieves partial response in at least two advanced sGBM patients with rarely significant side effects, underscoring the clinical potential for precisely treating gliomas using this therapy.

circRNADisease v2.0: an updated resource for high-quality experimentally supported circRNA-disease associations.

circRNADisease v2.0 is an enhanced and reliable database that offers experimentally verified relationships between circular RNAs (circRNAs) and various diseases. It is accessible at http://cgga.org.cn/circRNADisease/ or http://cgga.org.cn:9091/circRNADisease/. The database currently includes 6998 circRNA-disease entries across multiple species, representing a remarkable 19.77-fold increase compared to the previous version. This expansion consists of a substantial rise in the number of circRNAs (from 330 to 4246), types of diseases (from 48 to 330) and covered species (from human only to 12 species). Furthermore, a new section has been introduced in the database, which collects information on circRNA-associated factors (genes, proteins and microRNAs), molecular mechanisms (molecular pathways), biological functions (proliferation, migration, invasion, etc.), tumor and/or cell line and/or patient-derived xenograft (PDX) details, and prognostic evidence in diseases. In addition, we identified 7 159 865 relationships between mutations and circRNAs among 30 TCGA cancer types. Due to notable enhancements and extensive data expansions, the circRNADisease 2.0 database has become an invaluable asset for both clinical practice and fundamental research. It enables researchers to develop a more comprehensive understanding of how circRNAs impact complex diseases.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis.

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

Comprehensive analysis of the clinical and biological significances of cholesterol metabolism in lower-grade gliomas.

BACKGROUND: As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs). METHODS: The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature. RESULTS: Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients. CONCLUSION: The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs.

Molecular and clinical characterization of PTRF in glioma via 1,022 samples.

Polymerase I and transcript release factor (PTRF) plays a role in the regulation of gene expression and the release of RNA transcripts during transcription, which have been associated with various human diseases. However, the role of PTRF in glioma remains unclear. In this study, RNA sequencing (RNA-seq) data (n = 1022 cases) and whole-exome sequencing (WES) data (n = 286 cases) were used to characterize the PTRF expression features. Gene ontology (GO) functional enrichment analysis was used to assess the biological implication of changes in PTRF expression. As a result, the expression of PTRF was associated with malignant progression in gliomas. Meanwhile, somatic mutational profiles and copy number variations (CNV) revealed the glioma subtypes classified by PTRF expression showed distinct genomic alteration. Furthermore, GO functional enrichment analysis suggested that PTRF expression was associated with cell migration and angiogenesis, particularly during an immune response. Survival analysis confirmed that a high expression of PTRF is associated with a poor prognosis. In summary, PTRF may be a valuable factor for the diagnosis and treatment target of glioma.

Integrated analysis of the genomic and transcriptional profile of high-grade gliomas in different age groups.

BACKGROUND: Age is a powerful prognostic factor of high-grade glioma (HGG). However, the underlying genetic mechanisms of the discrepant prognosis among different age groups remain elusive. METHODS: A total of 953 and 559 HGG patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts were enrolled and assigned as young, intermediate, elderly groups. The data of clinicopathological characteristics, mRNA, mutation, copy number alteration was analyzed. RESULTS: Transcriptomic analysis revealed that diverse biological processes including immune response are altered between the young and elderly groups. Combined with the analysis of infiltrating immune cells and immune checkpoints, our results suggest an immune suppression status in the elderly group. Patients from different age groups exhibit different mutation and copy number alteration profiles. CONCLUSIONS: A multi-omics analysis is conducted to explore the biological basis of HGG patients of different age groups. This study suggests an immune-suppressive environment in elderly patients.

Novel roles of VAT1 expression in the immunosuppressive action of diffuse gliomas.

Standard treatment regimen of gliomas has almost reached a bottleneck in terms of survival benefit. Immunotherapy has been explored and applied in glioma treatment. Immunosuppression, as a hallmark of glioma, could be alleviated by inhibiting certain abnormally expressed biomarkers. Here, transcriptome data of 325 whole grade gliomas were collected from the CGGA database. The TCGA RNA sequencing database was used for validation. Western blot was used to verify the expression level of VAT1 on cellular level. The results showed that the expression of VAT1 was positively correlated with the grades of glioma as classified by WHO. A higher expression level of VAT1 was observed in the mesenchymal subtype of gliomas. The area under the curve suggested that the expression level of VAT1 might be a potential prognostic marker of mesenchymal subtype. In survival analysis, we found that patients with high VAT1 expression level tended to have shorter overall survival, which indicated the prognostic value of VAT1 expression. The results of gene ontology analysis showed that most biological processes of VAT1-related genes were involved in immune and inflammatory responses. The results of GSEA analysis showed a negative correlation between VAT1 expression and immune cells. We also identified that the expression of immune checkpoints increased with VAT1 expression. Therefore, the high expression level of VAT1 in patients with glioma was a potential indicator of a lower survival rate for patients with gliomas. Remarkably, VAT1 contributed to glioma-induced immunosuppression and might be a novel target in glioma immunotherapy.

Characterization and prognostic significance of alternative splicing events in lower-grade diffuse gliomas.

Alternative splicing (AS) is assumed to play important roles in the progression and prognosis of cancer. Currently, the comprehensive analysis and clinical relevance of AS in lower-grade diffuse gliomas have not been systematically addressed. Here, we gathered alternative splicing data of lower-grade diffuse gliomas from SpliceSeq. Based on the Percent Spliced In (PSI) values of 515 lower-grade diffuse glioma patients from the Cancer Genome Atlas (TCGA), we performed subtype-differential AS analysis and consensus clustering to determine robust clusters of patients. A total of 48 050 AS events in 10 787 genes in lower-grade diffuse gliomas were profiled. Subtype-differential splicing analysis and functional annotation revealed that spliced genes were significantly enriched in numerous cancer-related biological phenotypes and signalling pathways. Consensus clustering using AS events identified three robust clusters of patients with distinguished pathological and prognostic features. Moreover, each cluster was also associated with distinct genomic alterations. Finally, we developed and validated an AS-related signature with Cox proportional hazards model. The signature, significantly associated with clinical and molecular features, could serve as an independent prognostic factor for lower-grade diffuse gliomas. Thus, our results indicated that AS events could discriminate molecular subtypes and have prognostic impact in lower-grade diffuse gliomas.

Identification of an ATP metabolism-related signature associated with prognosis and immune microenvironment in gliomas.

As the core element of material and energy metabolism pathways, the biological functions and prognostic significance of ATP metabolism in diffuse gliomas have so far remained unclear. Based on comprehensive analysis of ATP metabolism-related gene expression profiles, we constructed an ATP metabolism-related risk signature to determine the role of ATP metabolism. We found that this ATP metabolism-related gene expression profile could divide patients into 2 robust groups with distinct clinical characteristics and prognosis. Patients in the high-risk group tended to be predicted as malignant entities, indicating that the activation of ATP metabolism may promote the malignant progress of diffuse gliomas. Cox regression and Kaplan-Meier analyses suggested that this risk signature was an independent predictor for prognosis. Furthermore, we constructed an individualized prognosis prediction model through nomogram and time-dependent receiver operating characteristic (ROC) curve analyses. Functional analysis suggested that, in addition to material and energy metabolism, ATP metabolism also played an essential role in the regulation of the tumor immune microenvironment. In brief, the ATP metabolism-related signature was tightly associated with regulation of the tumor immune microenvironment and could serve as an independent prognostic biomarker in diffuse gliomas.

Prognostic value of a nine-gene signature in glioma patients based on tumor-associated macrophages expression profiling.

Tumor-associated macrophages (TAMs) are regarded as the most abundantly infiltrating immune cells around the tumor microenvironment in gliomas, which plays an important role in tumorgenesis and immunosuppression. A total of 216 patients diagnosed with primary glioma were obtained from the Chinese Glioma Genome Atlas of which the RNA sequencing data was used as training set. RNA sequencing from the Cancer Genome Atlas was applicated for validation. We found that mesenchymal subtype showed strong positive correlation with macrophage-related genes (MRGs) expression. Survival analysis showed that high expression level of MRG predicted poor prognosis. A TAM-based nine-gene signature was constructed, which divided the samples into high- and low-risk of unfavorable outcome. The result of Cox regression analysis showed that the risk score was an independent prognostic factor in gliomas. Hence, the expression of TAMs was correlated with patient survival. The nine-TAM-related gene signature can predict patient survival efficiently.