Inhibition of hyaluronic acid degradation pathway suppresses glioma progression by inducing apoptosis and cell cycle arrest.

BACKGROUND: Abnormal hyaluronic acid (HA) metabolism is a major factor in tumor progression, and the metabolic regulation of HA mainly includes HA biosynthesis and catabolism. In glioma, abnormal HA biosynthesis is intimately involved in glioma malignant biological properties and the formation of immunosuppressive microenvironment; however, the role of abnormal HA catabolism in glioma remains unclear. METHODS: HA catabolism is dependent on hyaluronidase. In TCGA and GEPIA databases, we found that among the 6 human hyaluronidases (HYAL1, HYAL2, HYAL3, HYAL4, HYALP1, SPAM1), only HYAL2 expression was highest in glioma. Next, TCGA and CGGA database were further used to explore the correlation of HYAL2 expression with glioma prognosis. Then, the mRNA expression and protein level of HYAL2 was determined by qRT-PCR, Western blot and Immunohistochemical staining in glioma cells and glioma tissues, respectively. The MTT, EdU and Colony formation assay were used to measure the effect of HYAL2 knockdown on glioma. The GSEA enrichment analysis was performed to explore the potential pathway regulated by HYAL2 in glioma, in addition, the HYAL2-regulated signaling pathways were detected by flow cytometry and Western blot. Finally, small molecule compounds targeting HYAL2 in glioma were screened by Cmap analysis. RESULTS: In the present study, we confirmed that Hyaluronidase 2 (HYAL2) is abnormally overexpressed in glioma. Moreover, we found that HYAL2 overexpression is associated with multiple glioma clinical traits and acts as a key indicator for glioma prognosis. Targeting HYAL2 could inhibit glioma progression by inducing glioma cell apoptosis and cell cycle arrest. CONCLUSION: Collectively, these observations suggest that HYAL2 overexpression could promote glioma progression. Thus, treatments that disrupt HA catabolism by altering HYAL2 expression may serve as effective strategies for glioma treatment.

Targeting copper death genotyping associated gene RARRES2 suppresses glioblastoma progression and macrophages infiltration.

BACKGROUND: Copper homeostasis is associated with malignant biological behavior in various tumors. The excessive accumulation of copper can induce tumor death, which is named cuproptosis, and it is also closely related to tumor progression and the formation of the immune microenvironment. However, the associations of cuproptosis with glioblastoma (GBM) prognosis and microenvironment construction are poorly understood. METHOD: First, TCGA and GEO (GSE83300, GSE74187) merged datasets were used to analyze the association of cuproptosis-related genes (CRGs) with GBM. Then, we performed cluster analysis of CRGs in GBM from the GEO (GSE83300, GSE74187) and TCGA merged datasets. Subsequently, the prognostic risk model was constructed by least absolute shrinkage and selection operator (LASSO) according to gene expression features in CRG clusters. Next, we performed a series of in-depth analyses, including tumor mutational burden (TMB) analysis, cluster analysis, and GBM IDH status prediction. Finally, RARRES2 was identified as a target gene for GBM treatment, especially IDH wild-type GBM. In addition, we further analyzed the correlation of CRG clusters and RARRES2 expression with the GBM immune microenvironment by ESTIMATE and CIBERSORT analyses. In vitro experiments were conducted to demonstrate that targeting RARRES2 inhibits glioblastoma progression and macrophage infiltration, particularly IDH wild-type GBM. RESULTS: In the present study, we demonstrated that the CRG cluster was closely related to GBM prognosis and immune cell infiltration. Moreover, the prognostic risk model constructed with the three genes (MMP19, G0S2, RARRES2) associated with the CRG clusters could well evaluate the prognosis and immune cell infiltration in GBM. Subsequently, after further analyzing the tumor mutational burden (TMB) in GBM, we confirmed that RARRES2 in the prognostic risk model could be used as a crucial gene signature to predict the prognosis, immune cell infiltration and IDH status of GBM patients. CONCLUSION: This study fully revealed the potential clinical impact of CRGs on GBM prognosis and the microenvironment, and determined the effect of the crucial gene (RARRES2) on the prognosis and tumor microenvironment construction of GBM, meanwhile, our study also revealed over-expressed RARRES2 is related to the IDH satus of GBM, which provides a novel strategy for the treatment of GBM, particularly IDH wild-type GBM.

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors.

Diagnosis of primary brain tumors relies heavily on histopathology. Although various computational pathology methods have been developed for automated diagnosis of primary brain tumors, they usually require neuropathologists' annotation of region of interests or selection of image patches on whole-slide images (WSI). We developed an end-to-end Vision Transformer (ViT) - based deep learning architecture for brain tumor WSI analysis, yielding a highly interpretable deep-learning model, ViT-WSI. Based on the principle of weakly supervised machine learning, ViT-WSI accomplishes the task of major primary brain tumor type and subtype classification. Using a systematic gradient-based attribution analysis procedure, ViT-WSI can discover diagnostic histopathological features for primary brain tumors. Furthermore, we demonstrated that ViT-WSI has high predictive power of inferring the status of three diagnostic glioma molecular markers, IDH1 mutation, p53 mutation, and MGMT methylation, directly from H&E-stained histopathological images, with patient level AUC scores of 0.960, 0.874, and 0.845, respectively.

Screening Key Pathogenic Genes and Small Molecule Compounds for PNET.

Primitive neuroectodermal tumors (PNET) are rare malignant tumors, but the mortality rate of the patients is extremely high. The aim of this study was to identify the hub genes and pathways involved in the pathogenesis of PNET and to screen the potential small molecule drugs for PNET. We extracted gene expression profiles from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs) through Limma package in R. Two expression profiles (GSE14295 and GSE74195) were downloaded, including 33 and 5 cases separately. Four hundred sixty-eight DEGs (161 upregulated; 307 downregulated) were identified. Functional annotation and KEGG pathway enrichment of the DEGs were performed using DAVID and Kobas. Gene Ontology analysis showed the significantly enriched Gene Ontology terms included but not limited to mitosis, nuclear division, cytoskeleton, synaptic vesicle, syntaxin binding, and GABA A receptor activity. Cancer-related signaling pathways, such as DNA replication, cell cycle, and synaptic vesicle cycle, were found to be associated with these genes. Subsequently, the STRING database and Cytoscape were utilized to construct a protein-protein interaction and screen the hub genes, and we identified 5 hub genes (including CCNB1, CDC20, KIF11, KIF2C, and MAD2L1) as the key biomarkers for PNET. Finally, we identified potential small molecule drugs through CMap. Seven small molecule compounds, including trichostatin A, luteolin, repaglinide, clomipramine, lorglumide, vorinostat, and resveratrol may become potential candidates for PNET drugs.

Hypoxia related long non-coding RNAs in ischemic stroke.

With high rates of mortality and disability, stroke has caused huge social burden, and 85% of which is ischemic stroke. In recent years, it is a progressive discovery of long non-coding RNA (lncRNA) playing an important regulatory role throughout ischemic stroke. Hypoxia, generated from reduction or interruption of cerebral blood flow, leads to changes in lncRNA expression, which then influence disease progression. Therefore, we reviewed studies on expression of hypoxia-related lncRNAs and relevant molecular mechanism in ischemic stroke. Considering that hypoxia-inducible factor (HIF) is a crucial regulator in hypoxic progress, we mainly focus on the HIF-related lncRNA which regulates the expression of HIF or is regulated by HIF, further reveal their pathogenesis and adaption after brain ischemia and hypoxia, so as to find effective biomarker and therapeutic targets.