Prognostic and predictive biomarkers in central nervous system tumours: the molecular state of play

Role of Sodium-Dependent Vitamin C Transporter-2 and Ascorbate in Regulating the Hypoxic Pathway in Cultured Glioblastoma Cells

The most common and aggressive brain cancer, glioblastoma, is characterized by hypoxia and poor survival. The pro-tumour transcription factor, hypoxia-inducible factor (HIF), is regulated via HIF-hydroxylases that require ascorbate as cofactor. Decreased HIF-hydroxylase activity triggers the hypoxic pathway driving cancer progression. Tissue ascorbate accumulates via the sodium-dependent vitamin C transporter-2 (SVCT2). We hypothesize that glioblastoma cells rely on SVCT2 for ascorbate accumulation, and that knockout of this transporter would disrupt the regulation of the hypoxic pathway by ascorbate. Ascorbate uptake was measured in glioblastoma cell lines (U87MG, U251MG, T98G) by high-performance liquid chromatography. CRISPR/Cas9 was used to knockout SVCT2. Cells were treated with cobalt chloride, desferrioxamine or 5% oxygen, with/without ascorbate, and key hypoxic pathway proteins were measured using Western blot analysis. Ascorbate uptake was cell line dependent, ranging from 1.7 to 11.0 nmol/106 cells. SVCT2-knockout cells accumulated 90%-95% less intracellular ascorbate than parental cells. The hypoxic pathway was induced by all three stimuli, and ascorbate reduced this induction. In the SVCT2-knockout cells, ascorbate had limited effect on the hypoxic pathway. This study verifies that intracellular ascorbate is required to suppress the hypoxic pathway. As patient survival is related to an activated hypoxic pathway, increasing intra-tumoral ascorbate may be of clinical interest.

A cuproptosis-related gene DLAT as a novel prognostic marker and its relevance to immune infiltration in low-grade gliomas

DLAT has been recognized as a cuproptosis-related gene that is crucial for cuproptosis in earlier research. The study is to look at how DLAT affects individuals with low-grade glioma's prognosis and immune infiltration. The Genotype-Tissue Expression (GTEx) database and the TCGA database were used in this work to download RNAseq data in TPM format. DLAT was found to be overexpressed in LGG by comparing DLAT expression levels between LGG and normal brain tissue, and the expression of DLAT was verified by immunohistochemistry and semi-quantitative analysis. Then, the functional enrichment analysis revealed that the biological functional pathways and possible signal transduction pathways involved were primarily focused on extracellular matrix organization, transmembrane transporter complex, ion channel complex, channel activity, neuroactive ligand-receptor interaction, complement and coagulation cascades, and channel activity. The level of immune cell infiltration by plasmacytoid dendritic cells and CD8 T cells was subsequently evaluated using single-sample gene set enrichment analysis, which showed that high DLAT expression was inversely connected with that level of infiltration. The link between the methylation and mRNA transcription of DLAT was then further investigated via the MethSurv database, and the results showed that DLAT's hypomethylation status was linked to a poor outcome. Finally, by evaluating the prognostic value of DLAT using the Cox regression analysis and Kaplan-Meier technique, a column line graph was created to forecast the overall survival (OS) rate at 1, 3, and 5 years after LGG identification. The aforementioned results demonstrated that high DLAT expression significantly decreased OS and DSS, and that overexpression of DLAT in LGG was significantly linked with WHO grade, IDH status, primary therapy outcome, overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) events. DLAT was discovered as a separate predictive sign of OS in the end. DLAT might thus represent a brand-new predictive biomarker.

Practical Application of Deep Learning in Diagnostic Neuropathology-Reimagining a Histological Asset in the Era of Precision Medicine

In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to be the next innovation to alter the diagnostic workflow. In this review, we will explore the recent changes in the field of neuropathology and how this has led to an increased focus on molecular features in diagnosis and prognosis. Then, we will examine the work carried out to train deep learning models for various diagnostic tasks in neuropathology, as well as the machine learning frameworks they used. Focus will be given to both the challenges and successes highlighted therein, as well as what these trends may tell us about future roadblocks in the widespread adoption of this new technology. Finally, we will touch on recent trends in deep learning, as applied to digital pathology more generally, and what this may tell us about the future of deep learning applications in neuropathology.