Upregulation of glutaminase 2 and neutrophil cytosolic factor 2 is associated with the poor prognosis of glioblastoma

The ferroptosis signature predicts the prognosis and immune microenvironment of nasopharyngeal carcinoma

Nasopharyngeal carcinoma (NPC) is a cancer with a high metastatic rate and poor prognosis. Growing studies suggest that ferroptosis take part in the development of tumours. At the same time, the connection between ferroptosis-related genes (FRGs) and the prognosis of NPC remains unclear. In this study, we explored the dysregulated FRGs between normal control and tumour samples of NPC. Firstly, 14 of 36 differentially expressed FRGs were identified in NPC tissues compared to normal tissues, among which ABCC1, GLS2, CS and HMGCR were associated with poor prognosis for patients. The four ferroptosis genes were used for consensus cluster analysis and two risk-related FRGs (ABCC1 and GLS2) were used in a risk model. The ROC curve revealed the good predictive performance of this risk signature. Multivariate analysis revealed that risk score and intratumoral TILs were independent risk factors linked to prognosis. Additionally, our results suggested that the risk signature was attached to the immune microenvironment. Moreover, the NPC patients with high risk were sensitive to chemotherapeutic drugs including axitinib, docetaxel, embelin, epothilone.B, parthenolide, thapsigargin, tipifarnib, vinorelbine. Finally, the expression of ABCC1 and GLS2 was validated in NPC tissues using immunohistochemistry. Together, these results revealed ferroptosis may be a potential biomarker in NPC and representing a promising future direction in prognosis and therapeutic strategy for the treatment of NPC.

Novel macrophage-related gene prognostic index for glioblastoma associated with M2 macrophages and T cell dysfunction

This study aims to construct a Macrophage-Related Gene Prognostic Index (MRGPI) for glioblastoma (GBM) and explore the underlying molecular, metabolic, and immunological features. Based on the GBM dataset from The Cancer Genome Atlas (n = 156), 13 macrophage-related hub genes were identified by weighted gene co-expression network (WGCNA) analysis. 5 prognostic genes screened by Kaplan-Meire (K-M) analysis and Cox regression model were used to construct the MRGPI, including GPR84, NCF2, HK3, LILRB2, and CCL18. Multivariate Cox regression analysis found that the MRGPI was an independent risk factor (HR = 2.81, CI95: 1.13-6.98, p = 0.026), leading to an unfavorable outcome for the MRGPI-high group, which was further validated by 4 validation GBM cohorts (n = 728). Thereafter, the molecular, metabolic, and immune features and the clinical implications of the MRGPI-based groups were comprehensively characterized. Gene set enrichment analysis (GSEA) found that immune-related pathways, including inflammatory and adaptive immune response, and activated eicosanoid metabolic pathways were enriched in the MRGPI-high group. Besides, genes constituting the MRGPI was primarily expressed by monocytes and macrophages at single-cell scope and was associated with the alternative activation of macrophages. Moreover, correlation analysis and receiver operating characteristic (ROC) curves revealed the relevance between the MRGPI with the expression of immune checkpoints and T cell dysfunction. Thus, the responsiveness of samples in the MRGPI-high group to immune checkpoint inhibitors (ICI) was detected by algorithms, including Tumor Immune Dysfunction and Exclusion (TIDE) and Submap. In contrast, the MRGPI-low group had favorable outcome, was less immune active and insensitive to ICI. Together, we have developed a promising biomarker to classify the prognosis, metabolic and immune features for GBM, and provide references for facilitating the personalized application of ICI in GBM.

Bioinformatics Strategies to Identify Shared Molecular Biomarkers That Link Ischemic Stroke and Moyamoya Disease with Glioblastoma

Expanding data suggest that glioblastoma is accountable for the growing prevalence of various forms of stroke formation, such as ischemic stroke and moyamoya disease. However, the underlying deterministic details are still unspecified. Bioinformatics approaches are designed to investigate the relationships between two pathogens as well as fill this study void. Glioblastoma is a form of cancer that typically occurs in the brain or spinal cord and is highly destructive. A stroke occurs when a brain region starts to lose blood circulation and prevents functioning. Moyamoya disorder is a recurrent and recurring arterial disorder of the brain. To begin, adequate gene expression datasets on glioblastoma, ischemic stroke, and moyamoya disease were gathered from various repositories. Then, the association between glioblastoma, ischemic stroke, and moyamoya was established using the existing pipelines. The framework was developed as a generalized workflow to allow for the aggregation of transcriptomic gene expression across specific tissue; Gene Ontology (GO) and biological pathway, as well as the validation of such data, are carried out using enrichment studies such as protein–protein interaction and gold benchmark databases. The results contribute to a more profound knowledge of the disease mechanisms and unveil the projected correlations among the diseases.