Adult Glioma WHO Classification Update, Genomics, and Imaging

Bai, James; Varghese, Jerrin; Jain, Rajan

doi:10.1097/rmr.0000000000000234

Cited by 50 publications

(46 citation statements)

References 54 publications

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“…Glioma is one of the most common primary brain tumor, with an annual incidence of 3–8 per 100,000 population ( 1 ). As the most malignant subtype of glioma, glioblastoma multiforme (GBM) have an around 14 months of median survival ( 2 , 3 ). Despite the availability of conventional treatments, such as surgical resection, adjuvant radiotherapy, and chemotherapy, the effects of prolonged survivals in GBM patients were still limited ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

Matrix Remodeling-Associated Protein 8 as a Novel Indicator Contributing to Glioma Immune Response by Regulating Ferroptosis

Chen

Song

et al. 2022

Front. Immunol.

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Glioma is a highly malignant brain tumor with a poor survival rate. Novel biomarkers that act as prompt indicators of glioma are urgently needed. In this study, we identified and validated prognosis-related differentially expressed genes by datasets of glioma in the GEO and TCGA databases. Ferroptosis is a newly recognized process of cell death playing a vital role in cancer biology. Pearson correlation coefficient were used to discovery the prognosis-related genes which have the highest correlation with ferroptosis. Matrix remodeling-associated protein 8 (MXRA8) was identified as a novel prognosis indicator which may be involved in ferroptosis. The expression of MXRA8 was significantly higher in glioma compared with normal brain tissue, and increased expression of MXRA8 was associated with unfavorable survivals. Furthermore, in vitro analysis showed that knockdown of MXRA8 inhibited the cell viability in T98G and U251 cells and increased the sensitivity of glioma cells to temozolomide. We further observed that downregulation of MXRA8 elevated the levels of intracellular ferrous iron and lipid peroxidation, accompanied by upregulation of NCOA4 and suppression of FTH1. Moreover, co-expression analyses showed that GO term and KEGG pathways were mainly enriched in immunity-related pathways, such as neutrophil-related immunity, adaptive immune response, and cytokine binding. Through ssGSEA algorithm and TISIDB database, immunological analyses showed that MXRA8 was significantly correlated with various immune infiltration cells including NK cells, macrophages, and neutrophils. Meanwhile, MXRA8 was also associated with chemokines and multiple immunoinhibitory molecules, such as TGF-β1, IL-10, PD-L1, and CTLA4. We also found that MXRA8 was positively associated with immune infiltration score, and patients with higher immune score underwent worse overall survivals. Moreover, IHC staining indicated a highly positive correlation of MXRA8 with a macrophage marker CSF1R. The co-cultured models of glioma cells and M2 macrophages showed MXRA8 knockdown glioma cells alleviated the infiltration of M2 macrophage, while the reduced M2 macrophage infiltration generated by MXRA8 could be rescued by Fer-1 treatment. These results suggest that MXRA8 promotes glioma progression and highlight the pivotal role of MXRA8 in ferroptosis and immune microenvironment of glioma. Therefore, MXRA8 may serve as a novel prognostic marker and therapeutic target for glioma.

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Section: Introductionmentioning

confidence: 99%

Matrix Remodeling-Associated Protein 8 as a Novel Indicator Contributing to Glioma Immune Response by Regulating Ferroptosis

Chen

Song

et al. 2022

Front. Immunol.

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“…Glioma is a common and lethal primary malignant central nervous system tumor with a poor prognosis. It accounts for 30% of all brain tumors and central nervous system tumors and 80% of all malignant brain tumors [ 1 ]. The median survival time of patients with glioma is about 20 to 36 months, and the survival time of patients with glioblastoma (GBM) is less than 14 months [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Characterization of the Immune Cell Infiltration Landscape and a New Prognostic Score in Glioblastoma

Wang

Chi

2022

Journal of Healthcare Engineering

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Glioblastoma (GBM) is the most aggressive, malignant primary brain tumor, which has abundant tumor-infiltrating immune cells and stroma in the tumor microenvironment (TME). So far, the TME landscape of GBM has not been elucidated. GBM samples were retrieved from TCGA and GEO databases. We used ESTIMATE and CIBERSORT algorithms to calculate risk score associated with TME, and immune cell infiltration (ICI) score of each patient is calculated by PCA. GSEA analysis is explored for each subgroup. Finally, the patient prognosis in different ICI score subgroup is determined. Two ICI clusters are determined in 208 GBM patients, and 207 differentially expressed genes (DGEs) are found between ICI clusters. And then, two gene clusters were determined. Finally, we obtained ICI score for each patient using principal component analysis (PCA). Patients were divided into high and low ICI score subgroups by setting the median as cutoff. Through GSEA, we found ECM-receptor interaction, mTOR signaling pathway, pathways in cancer, TGF-beta signaling pathway, and other immunosuppressive pathway related genes in the low ICI score group. Furthermore, patients with high ICI score group have more better prognosis. Targeting the stroma in GBM may be an effective new therapeutic approach, and the ICI score is an effective potential prognostic classifier of GBM.

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“…Here we consider an application of quasi-diffusion imaging to a high-grade glial tumour, the glioblastoma multiforme (GBM). Clinical assessment of glial tumour grade and genetic subtype is relevant for predicting patient survival time and treatment strategy which includes surgical resection, chemo-and radiotherapy, and the need for accurate delineation of tumour tissue boundaries [68,69].…”

Section: Quasi-diffusion Imaging Of Brain Tumourmentioning

confidence: 99%

The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

et al. 2021

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Quasi-diffusion imaging (QDI) is a novel quantitative diffusion magnetic resonance imaging (dMRI) technique that enables high quality tissue microstructural imaging in a clinically feasible acquisition time. QDI is derived from a special case of the continuous time random walk (CTRW) model of diffusion dynamics and assumes water diffusion is locally Gaussian within tissue microstructure. By assuming a Gaussian scaling relationship between temporal () and spatial () fractional exponents, the dMRI signal attenuation is expressed according to a diffusion coefficient, (in mm2 s−1), and a fractional exponent, . Here we investigate the mathematical properties of the QDI signal and its interpretation within the quasi-diffusion model. Firstly, the QDI equation is derived and its power law behaviour described. Secondly, we derive a probability distribution of underlying Fickian diffusion coefficients via the inverse Laplace transform. We then describe the functional form of the quasi-diffusion propagator, and apply this to dMRI of the human brain to perform mean apparent propagator imaging. QDI is currently unique in tissue microstructural imaging as it provides a simple form for the inverse Laplace transform and diffusion propagator directly from its representation of the dMRI signal. This study shows the potential of QDI as a promising new model-based dMRI technique with significant scope for further development.

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Adult Glioma WHO Classification Update, Genomics, and Imaging

Cited by 50 publications

References 54 publications

Matrix Remodeling-Associated Protein 8 as a Novel Indicator Contributing to Glioma Immune Response by Regulating Ferroptosis

Matrix Remodeling-Associated Protein 8 as a Novel Indicator Contributing to Glioma Immune Response by Regulating Ferroptosis

Characterization of the Immune Cell Infiltration Landscape and a New Prognostic Score in Glioblastoma

The Mathematics of Quasi-Diffusion Magnetic Resonance Imaging

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