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Background: Lower-grade gliomas (LGGs) show highly metabolic heterogeneity and adaptability. To develop effective therapeutic strategies targeting metabolic processes, it is necessary to identify metabolic differences and define metabolic subtypes. Here, we aimed to develop a classification system based on metabolic gene expression profile in LGGs., Methods: The metabolic gene profile of 402 diffuse LGGs from the Cancer Genome Atlas (TCGA) was used for consensus clustering to determine robust clusters of patients, and the reproducibility of the classification system was evaluated in three Chinese Glioma Genome Atlas (CGGA) cohorts. Then, the metadata set for clinical characteristics, immune infiltration, metabolic signatures and somatic alterations was integrated to characterise the features of each subtype., Results: We successfully identified and validated three highly distinct metabolic subtypes in LGGs. M2 subtype with upregulated carbohydrate, nucleotide and vitamin metabolism correlated with worse prognosis, whereas M1 subtype with upregulated lipid metabolism and immune infiltration showed better outcome. M3 subtype was associated with low metabolic activities and displayed good prognosis. Three metabolic subtypes correlated with diverse somatic alterations. Finally, we developed and validated a metabolic signature with better performance of prognosis prediction., Conclusions: Our study provides a new classification based on metabolic gene profile and highlights the metabolic heterogeneity within LGGs., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

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., (© 2020 The Authors. Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

Transcriptomic data derived from bulk sequencing have been applied to delineate the tumor microenvironment (TME) and define immune subtypes in various cancers, which may facilitate the design of immunotherapy treatment strategies. We herein gathered published gene expression data from diffuse lower-grade glioma (LGG) patients to identify immune subtypes. Based on the immune gene profiles of 402 LGG patients from The Cancer Genome Atlas, we performed consensus clustering to determine robust clusters of patients, and evaluated their reproducibility in three Chinese Glioma Genome Atlas cohorts. We further integrated immunogenomics methods to characterize the immune environment of each subtype. Our analysis identified and validated three immune subtypes-Im1, Im2, and Im3-characterized by differences in lymphocyte signatures, somatic DNA alterations, and clinical outcomes. Im1 had a higher infiltration of CD8+ T cells, Th17, and mast cells. Im2 was defined by elevated cytolytic activity, exhausted CD8+ T cells, macrophages, higher levels of aneuploidy, and tumor mutation burden, and these patients had worst outcome. Im3 displayed more prominent T helper cell and APC coinhibition signatures, with elevated pDCs and macrophages. Each subtype was associated with distinct somatic alterations. Moreover, we applied graph structure learning-based dimensionality reduction to the immune landscape and revealed significant intracluster heterogeneity with Im2 subtype. Finally, we developed and validated an immune signature with better performance of prognosis prediction. Our results demonstrated the immunological heterogeneity within diffuse LGG and provided valuable stratification for the design of future immunotherapy., (© 2020 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.)

Isocitrate dehydrogenase (IDH) wild-type diffuse lower-grade glioma (LGG) is usually associated with poor outcome, but there have been disputes over its clinical outcome and classification. We present here a robust gene expression-based molecular classification of IDH wild-type diffuse LGG into two subtypes with distinct biological and clinical features. A discovery cohort of 49 IDH wild-type diffuse LGGs from the Chinese Glioma Genome Atlas (CGGA) was subjected to clustering and function analysis. Seventy-three tumors from The Cancer Genome Atlas (TCGA) were used to validate our findings. Consensus clustering of transcriptional data uncovered concordant classification of two robust and prognostically significant subtypes of IDH wild-type LGG. Subtype 1, associated with poorer outcomes, was characterized by significantly higher immune and cytolytic scores, M2 macrophages, and up-regulation of immune exhaustion markers, while Subtype 2, which had elevated lymphocytes and plasma cells, showed relatively favorable survival. Somatic alteration analysis revealed that Subtype 1 showed more frequently deleted regions, such as the locus of CDKN2A/CDKN2B, DMRTA1, C9orf53, and MTAP. Furthermore, we developed and validated a five-gene signature for better application of this acquired stratification. Our data demonstrate the biological and prognostic heterogeneity within IDH wild-type diffuse LGGs and deepen our molecular understandi-g of this tumor entity. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd., (© 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.)

Lipid metabolism reprogramming plays important role in cell growth, proliferation, angiogenesis and invasion in cancers. However, the diverse lipid metabolism programmes and prognostic value during glioma progression remain unclear. Here, the lipid metabolism-related genes were profiled using RNA sequencing data from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database. Gene ontology (GO) and gene set enrichment analysis (GSEA) found that glioblastoma (GBM) mainly exhibited enrichment of glycosphingolipid metabolic progress, whereas lower grade gliomas (LGGs) showed enrichment of phosphatidylinositol metabolic progress. According to the differential genes of lipid metabolism between LGG and GBM, we developed a nine-gene set using Cox proportional hazards model with elastic net penalty, and the CGGA cohort was used for validation data set. Survival analysis revealed that the obtained gene set could differentiate the outcome of low- and high-risk patients in both cohorts. Meanwhile, multivariate Cox regression analysis indicated that this signature was a significantly independent prognostic factor in diffuse gliomas. Gene ontology and GSEA showed that high-risk cases were associated with phenotypes of cell division and immune response. Collectively, our findings provided a new sight on lipid metabolism in diffuse gliomas., (© 2019 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine.)

Background: Aberrant expression of RNA processing genes may drive the alterative RNA profile in lower-grade gliomas (LGGs). Thus, we aimed to further stratify LGGs based on the expression of RNA processing genes., Methods: This study included 446 LGGs from The Cancer Genome Atlas (TCGA, training set) and 171 LGGs from the Chinese Glioma Genome Atlas (CGGA, validation set). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was conducted to develop a risk-signature. The receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to study the prognosis value of the risk-signature., Results: Among the tested 784 RNA processing genes, 276 were significantly correlated with the OS of LGGs. Further LASSO Cox regression identified a 19-gene risk-signature, whose risk score was also an independently prognosis factor (P<0.0001, multiplex Cox regression) in the validation dataset. The signature had better prognostic value than the traditional factors "age", "grade" and "WHO 2016 classification" for 3- and 5-year survival both two datasets (AUCs > 85%). Importantly, the risk-signature could further stratify the survival of LGGs in specific subgroups of WHO 2016 classification. Furthermore, alternative splicing events for genes such as EGFR and FGFR were found to be associated with the risk score. mRNA expression levels for genes, which participated in cell proliferation and other processes, were significantly correlated to the risk score., Conclusions: Our results highlight the role of RNA processing genes for further stratifying the survival of patients with LGGs and provide insight into the alternative splicing events underlying this role.

Background: Immunotherapy provided unprecedented advances in the treatment of several previously untreated cancers. However, these immunomodulatory maneuvers showed limited response to patients with glioma in clinical trials. Our aim was to depict the immune characteristics of glioma with immune cytolytic activity at genetic and transcriptome levels. Methods: In total, 325 gliomas from CGGA dataset as training cohort and 699 gliomas from TCGA dataset as validation cohort were enrolled in our analysis. We calculated the immune cytolytic activity for 1,000 of gliomas. The characteristics of immune cytolytic activity in gliomas were interpreted by the corresponding clinical, molecular genetics and radiological information. Results: We found that immune cytolytic activity was highly associated with molecular, clinical, and edema extent. High cytolytic activity gliomas were more likely to be diagnosed as glioblastoma and might be a potential marker of mesenchymal subtype. Moreover, those gliomas exhibited significantly increased copy number alterations including recurrent focal amplifications of PDGFA and EGFR, as well as recurrent deletions of CDKN2A/B. Subsequent biological function analysis revealed that the immune response and immune checkpoints expression were significantly correlated with the cytolytic activity of gliomas. Immune cytolytic activity was significantly positively associated with the extent of peri-tumor edema and was independently correlated with reduced survival time. Conclusion: Our results highlighted the immunoregulatory mechanism heterogeneity of gliomas. Cytolytic activity, indirectly reflected by the extent of peri-tumor edema, may provide a potential index to evaluate the status of immune microenvironment and immune checkpoints in glioma, which should be fully valued for precision classification and immunotherapy.

Isocitrate dehydrogenase (IDH) mutant glioblastoma (GBM), accounts for ~10% GBMs, arises from lower grade diffuse glioma and preferentially appears in younger patients. Here, we aim to establish a robust gene expression-based molecular classification of IDH -mutant GBM. A total of 33 samples from the Chinese Glioma Genome Atlas RNA-sequencing data were selected as training set, and 21 cases from Chinese Glioma Genome Atlas microarray data were used as validation set. Consensus clustering identified three groups with distinguished prognostic and molecular features. G1 group, with a poorer clinical outcome, mainly contained TERT promoter wild-type and male cases. G2 and G3 groups had better prognosis differed in gender. Gene ontology analysis showed that genes enriched in G1 group were involved in DNA replication, cell division and cycle. On the basis of the differential genes between G1 and G2/G3 groups, a six-gene signature was developed with a Cox proportional hazards model. Kaplan–Meier analysis found that the acquired signature could differentiate the outcome of low- and high-risk cases. Moreover, the signature could also serve as an independent prognostic factor for IDH -mutant GBM in the multivariate Cox regression analysis. Gene ontology and gene set enrichment analyses revealed that gene sets correlated with high-risk group were involved in cell cycle, cell proliferation, DNA replication and repair. These finding highlights heterogeneity within IDH -mutant GBMs and will advance our molecular understanding of this lethal cancer. [ABSTRACT FROM AUTHOR]

Background: Gliomas are aggressive tumors with various molecular and clinical characteristics and exhibit strongly resistance to radio-chemotherapy. Programmed cell death 1 ligand 2 (PD-L2) is a cell surface protein, which was reported in many cancers, modulating cancer-associated immune responses, while the role of PD-L2 in gliomas remained unclear. Herein, we aimed to investigate the biological behaviors and clinical prognostic values of PD-L2 in gliomas. Methods: Totally, we enrolled RNA sequencing data of 325 glioma samples from Chinese Glioma Genome Atlas (CGGA) as training cohort and RNA expression data of 1032 samples from The Cancer Genome Atlas (TCGA) dataset as validation cohort in this research. Then, the clinical and molecular characteristics, and the prognostic value of PD-L2 were analyzed. Results: We found that PD-L2 expression level was significantly upregulated in higher grade glioma and IDH wild-type glioma. Receiver Operating Characteristic (ROC) analysis revealed that PD-L2 was a potential indicator of mesenchymal subtype. PD-L2 exhibited tight relationship with immune response and immune-modulating process in glioma. Moreover, PD-L2 expression level could predict unfavorable prognoses of patients independent of age, grade, IDH status and 1p/19q status. Conclusions: Our study revealed that PD-L2 was closely related with inflammation and immune response. Patients with lower PD-L2 expression level tended to experience improved survival. Targeting PD-L2 may become a valuable approach for the treatment of gliomas in clinical practice. [ABSTRACT FROM AUTHOR]