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.

Gliomas are the most common brain tumors characterized by complicated heterogeneity. The genetic, molecular, and histological pathology of gliomas is characterized by high neuro-inflammation. The inflammatory microenvironment in the central nervous system (CNS) has been closely linked with inflammasomes that control the inflammatory response and coordinate innate host defenses. Dysregulation of the inflammasome causes an abnormal inflammatory response, leading to carcinogenesis in glioma. Because of the clinical importance of the various physiological properties of the inflammasome in glioma, the inflammasome has been suggested as a promising treatment target for glioma management. Here, we summarize the current knowledge on the contribution of the inflammasomes in glioma and therapeutic insights.

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.

Premature ovarian failure (POF) is a complex disease of which the etiology is influenced by numerous genetic variations. Several POF candidate genes have been reported. However, no causal genes with high odds ratio (OR) have yet been discovered. This study included 564 females of Korean ethnicity, comprising 60 patients with POF and 182 controls in the discovery set and 105 patients with POF and 217 controls in the replication set. We conducted genome-wide association analysis to search for novel candidate genes predicted to influence POF development using Axiom Precision Medicine Research Arrays and additive model logistic regression analysis. One statistically significant single nucleotide polymorphism (SNP), rs55941146, which encodes a missense alteration (Val > Gly) in the APBA3 gene, was identified with OR values for association with POF of 13.33 and 4.628 in the discovery and replication sets, respectively. No rs55941146 minor allele homozygotes were present in either cases or controls. The APBA3 protein binds FIH-1 that inhibits hypoxia inducible factor-1α (HIF-1α). HIF-1α contributes to granulosa cell proliferation, which is crucial for ovarian follicle growth, by regulating cell proliferation factors and follicle stimulating hormone-mediated autophagy. Our data demonstrate that APBA3 is a candidate novel causal gene for POF.

Gastric cancer is a common tumor, with a high mortality rate. The severity of gastric cancer is assessed by TNM staging. Long noncoding RNAs (lncRNAs) play a role in cancer treatment; investigating the clinical significance of novel biomarkers associated with TNM staging, such as lncRNAs, is important. In this study, we investigated the association between the expression of the lncRNA LOC441461 and gastric cancer stage. LOC441461 expression was lower in stage IV than in stages I, II, and III. The depletion of LOC441461 promoted cell proliferation, cell cycle progression, apoptosis, cell motility, and invasiveness. LOC441461 downregulation increased the epithelial-to-mesenchymal transition, as indicated by increased TRAIL signaling and decreased RUNX1 interactions. The interaction of the transcription factors RELA, IRF1, ESR1, AR, POU5F1, TRIM28, and GATA1 with LOC441461 affected the degree of the malignancy of gastric cancer by modulating gene transcription. The present study identified LOC441461 and seven transcription factors as potential biomarkers and therapeutic targets for the treatment of gastric cancer.

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.