Tim-3/Gal-9 and the NLRC4 inflammasome contribute to glioma progression. However, the underlying mechanisms involved are unclear. Here, we observed that Tim-3/Gal-9 expression increased with glioma malignancy and found that Tim-3/Gal-9 regulate NLRC4 inflammasome formation and activation. Tim-3/Gal-9 and NLRC4 inflammasome-related molecule expression levels increased with WHO glioma grade, and this association was correlated with low survival. We investigated NLRC4 inflammasome formation by genetically regulating Tim-3 and its ligand Gal-9. Tim-3/Gal-9 regulation was positively correlated with the NLRC4 inflammasome, NLRC4, and caspase-1 expression. Tim-3/Gal-9 did not trigger IL-1β secretion but were strongly positively correlated with caspase-1 activity as they induced programmed cell death in glioma cells. A protein–protein interaction analysis revealed that the FYN-JAK1-ZNF384 pathways are bridges in NLRC4 inflammasome regulation by Tim-3/Gal-9. The present study showed that Tim-3/Gal-9 are associated with poor prognosis in glioma patients and induce NLRC4 inflammasome formation and activation. We proposed that a Tim-3/Gal-9 blockade could be beneficial in glioma therapy as it would reduce the inflammatory microenvironment by downregulating the NLRC4 inflammasome.

The alteration of the cellular metabolism is a hallmark of glioma. The high glycolytic phenotype is a critical factor in the pathogenesis of high-grade glioma, including glioblastoma multiforme (GBM). GBM has been stratified into three subtypes as the proneural, mesenchymal, and classical subtypes. High glycolytic activity was found in mesenchymal GBM relative to proneural GBM. NADPH oxidase 2 (NOX2) has been linked to cellular metabolism and epithelial-mesenchymal transition (EMT) in tumors. The role of NOX2 in the regulation of the high glycolytic phenotype and the gain of the mesenchymal subtype in glioma remain unclear. Here, our results show that the levels of NOX2 were elevated in patients with GBM. NOX2 induces hexokinase 2 (HK2)-dependent high glycolytic activity in U87MG glioma cells. High levels of NOX2 are correlated with high levels of HK2 and glucose uptake in patients with GBM relative to benign glioma. Moreover, NOX2 increases the expression of mesenchymal-subtype-related genes, including COL5A1 and FN1 in U87MG glioma cells. High levels of NOX2 are correlated with high levels of COL5A1 and the accumulation of extracellular matrix (ECM) in patients with GBM relative to benign glioma. Furthermore, high levels of HK2 are correlated with high levels of COL5A1 in patients with GBM relative to benign glioma. Our results suggest that NOX2-induced high glycolytic activity contributes to the gain of the COL5A1-mediated mesenchymal phenotype in GBM.

Glioma accounts for 80% of all malignant brain tumours and is the most common adult primary brain tumour. Age is an important factor affecting the development of cancer, as somatic mutations accumulate with age. Here, we aimed to analyse the significance of age-dependent non-silent somatic mutations in glioma prognosis. Histological tumour grade depends on age at diagnosis in patients with IDH1, TP53, ATRX, and EGFR mutations. Age of patients with wild-type IDH1 and EGFR increased with increase in tumour grade, while the age of patients with IDH1 or EGFR mutation remained constant. However, the age of patients with EGFR mutation was higher than that of patients with IDH1 mutation. The hierarchical clustering of patients was dominantly separated by IDH1 and EGFR mutations. Furthermore, patients with IDH1 mutation were dominantly separated by TP53 and ATRX double mutation and its double wild-type counterpart. The age of patients with ATRX and TP53 mutation was lower than that of patients with wild-type ATRX and TP53. Patients with the double mutation showed poorer prognosis than those with the double wild type genotype. Unlike IDH1 mutant, IDH1 wild-type showed upregulation of expression of epithelial mesenchymal transition associated genes.

Oligodendroglioma (OD) is a subtype of glioma occurring in the central nervous system. The 1p/19q codeletion is a prognostic marker of OD with an isocitrate dehydrogenase (IDH) mutation and is associated with a clinically favorable overall survival (OS); however, the exact underlying mechanism remains unclear. Long non-coding RNAs (lncRNAs) have recently been suggested to regulate carcinogenesis and prognosis in cancer patients. Here, we performed in silico analyses using low-grade gliomas from datasets obtained from The Cancer Genome Atlas to investigate the effects of ceRNA with 1p/19q codeletion on ODs. Thus, we selected modules of differentially expressed genes that were closely related to 1p/19q codeletion traits using weighted gene co-expression network analysis and constructed 16 coding RNA–miRNA–lncRNA networks. The ceRNA network participated in ion channel activity, insulin secretion, and collagen network and extracellular matrix (ECM) changes. In conclusion, ceRNAs with a 1p/19q codeletion can create different tumor microenvironments via potassium ion channels and ECM composition changes; furthermore, differences in OS may occur. Moreover, if extrapolated to gliomas, our results can provide insights into the consequences of identical gene expression, indicating the possibility of tracking different biological processes in different subtypes of glioma.

Traumatic brain injury (TBI) occurs frequently, and acute TBI requiring surgical treatment is closely related to patient survival. Models for predicting the prognosis of patients with TBI do not consider various factors of patient status; therefore, it is difficult to predict the prognosis more accurately. In this study, we created a model that can predict the survival of patients with TBI by adding hematologic parameters along with existing non-hematologic parameters. The best-fitting model was created using the Akaike information criterion (AIC), and hematologic factors including preoperative hematocrit, preoperative C-reactive protein (CRP), postoperative white blood cell (WBC) count, and postoperative hemoglobin were selected to predict the prognosis. Among several prediction models, the model that included age, Glasgow Coma Scale, Injury Severity Score, preoperative hematocrit, preoperative CRP, postoperative WBC count, postoperative hemoglobin, and postoperative CRP showed the highest area under the curve and the lowest corrected AIC for a finite sample size. Our study showed a new prediction model for mortality in patients with TBI using non-hematologic and hematologic parameters. This prediction model could be useful for the management of patients with TBI.

(1) Background: Hepatocellular carcinoma (HCC) remains a major cause of cancer mortality and is characterized by pronounced inter- and intra-tumoral heterogeneity and therapy resistance. We aimed to define core transcriptional circuits that drive HCC malignancy and to delineate how these programs shape the tumor microenvironment (TME). (2) Methods: We integrated single-cell, spatial, and bulk transcriptomic datasets from public cohorts. (3) Results: We identified nine tumor-restricted transcription factors (TFs)—HTATIP2, HES6, ILF2, E2F1, MYBL2, DDIT3, FOXM1, HMGA1, and ETV4—whose expression and regulon activity associated with malignant phenotypes and poor survival. These TFs organize a progression axis from an early proliferative state (cluster C4) toward an invasive, metabolically adapted state (cluster C1) enriched for hypoxia, epithelial–mesenchymal transition (EMT), and inflammatory signaling. The C1 state remodeled the TME by establishing an immunosuppressive niche marked by expansion of T regulatory cells (Treg) and by accumulation of SPP1⁺ macrophages. These macrophages, recruited and polarized by C1 tumor cells, exhibited M2-like, pro-angiogenic, and immunosuppressive features and engaged epithelial, immune, and stromal partners via SPP1-CD44 and SPP1-integrin interactions. (4) Conclusions: In summary, a tumor-intrinsic TF network cooperates with SPP1⁺ macrophage signaling to promote a permissive microenvironment and HCC progression. This integrated axis highlights tractable vulnerabilities for therapeutic intervention.