Immune Infiltration Associated MAN2B1 Is a Novel Prognostic Biomarker for Glioma

Oncological Aspects of Lysosomal Storage Diseases

Lysosomal storage diseases (LSDs) are caused by the deficient activity of a lysosomal hydrolase or the lack of a functional membrane protein, transporter, activator, or other protein. Lysosomal enzymes break down macromolecular compounds, which contribute to metabolic homeostasis. Stored, undegraded materials have multiple effects on cells that lead to the activation of autophagy and apoptosis, including the toxic effects of lyso-lipids, the disruption of intracellular Ca2+ ion homeostasis, the secondary storage of macromolecular compounds, the activation of signal transduction, apoptosis, inflammatory processes, deficiencies of intermediate compounds, and many other pathways. Clinical observations have shown that carriers of potentially pathogenic variants in LSD-associated genes and patients affected with some LSDs are at a higher risk of cancer, although the results of studies on the frequency of oncological diseases in LSD patients are controversial. Cancer is found in individuals affected with Gaucher disease, Fabry disease, Niemann-Pick type A and B diseases, alfa-mannosidosis, and sialidosis. Increased cancer prevalence has also been reported in carriers of a potentially pathogenic variant of an LSD gene, namely CLN3, SGSH, GUSB, NEU1, and, to a lesser extent, in other genes. In this review, LSDs in which oncological events can be observed are described.

Multi-proteomic analyses of 5xFAD mice reveal new molecular signatures of early-stage Alzheimer's disease

An early diagnosis of Alzheimer's disease is crucial as treatment efficacy is limited to the early stages. However, the current diagnostic methods are limited to mid or later stages of disease development owing to the limitations of clinical examinations and amyloid plaque imaging. Therefore, this study aimed to identify molecular signatures including blood plasma extracellular vesicle biomarker proteins associated with Alzheimer's disease to aid early-stage diagnosis. The hippocampus, cortex, and blood plasma extracellular vesicles of 3- and 6-month-old 5xFAD mice were analyzed using quantitative proteomics. Subsequent bioinformatics and biochemical analyses were performed to compare the molecular signatures between wild type and 5xFAD mice across different brain regions and age groups to elucidate disease pathology. There was a unique signature of significantly altered proteins in the hippocampal and cortical proteomes of 3- and 6-month-old mice. The plasma extracellular vesicle proteomes exhibited distinct informatic features compared with the other proteomes. Furthermore, the regulation of several canonical pathways (including phosphatidylinositol 3-kinase/protein kinase B signaling) differed between the hippocampus and cortex. Twelve potential biomarkers for the detection of early-stage Alzheimer's disease were identified and validated using plasma extracellular vesicles from stage-divided patients. Finally, integrin α-IIb, creatine kinase M-type, filamin C, glutamine γ-glutamyltransferase 2, and lysosomal α-mannosidase were selected as distinguishing biomarkers for healthy individuals and early-stage Alzheimer's disease patients using machine learning modeling with approximately 79% accuracy. Our study identified novel early-stage molecular signatures associated with the progression of Alzheimer's disease, thereby providing novel insights into its pathogenesis.

Genetics of enzymatic dysfunctions in metabolic disorders and cancer

Inherited metabolic disorders arise from mutations in genes involved in the biogenesis, assembly, or activity of metabolic enzymes, leading to enzymatic deficiency and severe metabolic impairments. Metabolic enzymes are essential for the normal functioning of cells and are involved in the production of amino acids, fatty acids and nucleotides, which are essential for cell growth, division and survival. When the activity of metabolic enzymes is disrupted due to mutations or changes in expression levels, it can result in various metabolic disorders that have also been linked to cancer development. However, there remains much to learn regarding the relationship between the dysregulation of metabolic enzymes and metabolic adaptations in cancer cells. In this review, we explore how dysregulated metabolism due to the alteration or change of metabolic enzymes in cancer cells plays a crucial role in tumor development, progression, metastasis and drug resistance. In addition, these changes in metabolism provide cancer cells with a number of advantages, including increased proliferation, resistance to apoptosis and the ability to evade the immune system. The tumor microenvironment, genetic context, and different signaling pathways further influence this interplay between cancer and metabolism. This review aims to explore how the dysregulation of metabolic enzymes in specific pathways, including the urea cycle, glycogen storage, lysosome storage, fatty acid oxidation, and mitochondrial respiration, contributes to the development of metabolic disorders and cancer. Additionally, the review seeks to shed light on why these enzymes represent crucial potential therapeutic targets and biomarkers in various cancer types.

GRN is a prognostic biomarker and correlated with immune infiltration in glioma: A study based on TCGA data

Background

Among primary brain tumors, gliomas are associated with a poor prognosis and a median survival that varies depending on the tumor grade and subtype. As the most malignant form of glioma, glioblastoma (GBM) constitutes a significant health concern. Alteration in granulin(GRN) has been proved to be accountable for several diseases. However, the relationship between GRN and GBM remains unclear. We evaluated the role of GRN in GBM through The Cancer Genome Atlas (TCGA) database

Methods

First, we assessed the relationship between GRN and GBM through the GEPIA database. Next, the relationship between GRN and GBM prognosis was analyzed by logistic regression and multivariate cox methods. Using CIBERSORT and the GEPIA correlation module, we also investigated the link between GRN and immune infiltrates in cancer. Using the TCGA data, a gene set enrichment analysis (GSEA) was performed. We also employed Tumor Immune Estimation Resource (TIMER) to examine the data set of GRN expression and immune infiltration level in GBM and investigate the cumulative survival in GBM. We also validated tissues from GBM patients by Western blotting, RT-qPCR, and immunohistochemistry.

Results

Increased GRN expression was shown to have a significant relationship to tumor grade in a univariate study utilizing logistic regression. Furthermore, multivariate analysis disclosed that GRN expression down-regulation is an independent predictive factor for a favorable outcome. GRN expression level positively correlates with the number of CD4+ T cells, neutrophils, macrophages, and dendritic cells (DCs) that infiltrate a GBM. The GSEA also found that the high GRN expression phenotype pathway was enriched for genes involved in immune response molecular mediator production, lymphocyte-mediated immunity, cytokine-mediated signaling pathway, leukocyte proliferation, cell chemotaxis, and CD4+ alpha beta T cell activation. Differentially enriched pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) include lysosome, apoptosis, primary immunodeficiency, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and B cell receptor signaling pathway. Validated result showed that GRN was upregulated in GBM tissues. These results suggested that GRN was a potential indicator for the status of GBM.

Conclusion

GRN is a prognostic biomarker and correlated with immune infiltrates in GBM.