Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor. The unregulated expression of Claudin-4 (CLDN4) plays an important role in tumor progression. However, the biological role of CLDN4 in GBM is still unknown. This study aimed to determine whether CLDN4 mediates glioma malignant progression, if so, it would further explore the molecular mechanisms of carcinogenesis. Our results revealed that CLDN4 was significantly upregulated in glioma specimens and cells. The inhibition of CLND4 expression could inhibit mesenchymal transformation, cell invasion, cell migration and tumor growth in vitro and in vivo. Moreover, combined with in vitro analysis, we found that CLDN4 can modulate tumor necrosis factor-α (TNF-α) signal pathway. Meanwhile, we also validated that the transforming growth factor-β (TGF-β) signal pathway can upregulate the expression of CLDN4, and promote the invasion ability of GBM cells. Conversely, TGF-β signal pathway inhibitor ITD-1 can downregulate the expression of CLDN4, and inhibit the invasion ability of GBM cells. Furthermore, we found that TGF-β can promote the nuclear translocation of CLDN4. In summary, our findings indicated that the TGF-β/CLDN4/TNF-α/NF-κB signal axis plays a key role in the biological progression of glioma. Disrupting the function of this signal axis may represent a new treatment strategy for patients with GBM.
Immunogenic cell death (ICD) is a type of regulated cell death (RCD) and is correlated with the progression, prognosis, and therapy of tumors, including glioma. Numerous studies have shown that the immunotherapeutic and chemotherapeutic agents of glioma might induce ICD. However, studies on the comprehensive analysis of the role of ICD-related genes and their correlations with overall survival (OS) in glioma are lacking. The genetic, transcriptional, and clinical data of 1896 glioma samples were acquired from five distinct databases and analyzed in terms of genes and transcription levels. The method of consensus unsupervised clustering divided the patients into two disparate molecular clusters: A and B. All of the patients were randomly divided into training and testing groups. Employing the training group data, 14 ICD-related genes were filtered out to develop a risk-score model. The correlations between our risk groups and prognosis, cells in the tumor microenvironment (TME) and immune cells infiltration, chemosensitivity and cancer stem cell (CSC) index were assessed. A highly precise nomogram model was constructed to enhance and optimize the clinical application of the risk score. The results demonstrated that the risk score could independently predict the OS rate and the immunotherapeutic response of glioma patients. This study analyzed the ICD-related genes in glioma and evaluated their role in the OS, clinicopathological characteristics, TME and immune cell infiltration of glioma. Our results may help in assessing the OS of glioma and developing better immunotherapeutic strategies.
BackgroundSynapse-associated proteins (SAPs) play important roles in central nervous system (CNS) tumors. Recent studies have reported that γ-aminobutyric acidergic (GABAergic) synapses also play critical roles in the development of gliomas. However, biomarkers of GABAergic synapses in low-grade gliomas (LGGs) have not yet been reported.MethodsmRNA data from normal brain tissue and gliomas were obtained from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases, respectively. A validation dataset was also obtained from the Chinese Glioma Genome Atlas (CGGA) database. The expression patterns of GABAergic synapse-related genes (GSRGs) were evaluated with difference analysis in LGGs. Then, a GABAergic synapse-related risk signature (GSRS) was constructed with least absolute shrinkage and selection operator (LASSO) Cox regression analysis. According to the expression value and coefficients of identified GSRGs, the risk scores of all LGG samples were calculated. Univariate and multivariate Cox regression analyses were conducted to evaluate related risk scores for prognostic ability. Correlations between characteristics of the tumor microenvironment (TME) and risk scores were explored with single-sample gene set enrichment analysis (ssGSEA) and immunity profiles in LGGs. The GSRS-related pathways were investigated by gene set variation analysis (GSVA). Real-time PCR and the Human Protein Atlas (HPA) database were applied to explore related expression of hub genes selected in the GSRS.ResultsCompared with normal brain samples, 25 genes of 31 GSRGs were differentially expressed in LGG samples. A constructed five-gene GSRS was related to clinicopathological features and prognosis of LGGs by the LASSO algorithm. It was shown that the risk score level was positively related to the infiltrating level of native CD4 T cells and activated dendritic cells. GSVA identified several cancer-related pathways associated with the GSRS, such as P53 pathways and the JAK-STAT signaling pathway. Additionally, CA2, PTEN, OXTR, and SLC6A1 (hub genes identified in the GSRS) were regarded as the potential predictors in LGGs.ConclusionA new five-gene GSRS was identified and verified by bioinformatics methods. The GSRS provides a new perspective in LGG that may contribute to more accurate prediction of prognosis of LGGs.
Ischemic stroke (IS) is associated with changes in gene expression patterns in the ischemic penumbra and extensive neurovascular inflammation. However, the key molecules related to the inflammatory response in the acute phase of IS remain unclear. To address this knowledge gap, conducted a study using Gene Set Enrichment Analysis (GSEA) on two gene expression profiles, GSE58720 and GSE202659, downloaded from the GEO database. We screened differentially expressed genes (DEGs) using GEO2R and analyzed 170 differentially expressed intersection genes for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and Gene Ontology (GO) analysis. We also used Metascape, DAVID, STRING, Cytoscape, and TargetScan to identify candidate miRNAs and genes. The targeted genes and miRNA molecule were clarified using the mice middle cerebral artery occlusion-reperfusion (MCAO/R) model. Our findings revealed that 170 genes were correlated with cytokine production and inflammatory cell activation, as determined by GO and KEGG analyses. Cluster analysis identified 11 hub genes highly associated with neuroinflammation: Ccl7, Tnf, Ccl4, Timp1, Ccl3, Ccr1, Sele, Ccr2, Tlr4, Ptgs2, and Il6. TargetScan results suggested that Ptgs2, Tlr4, and Ccr2 might be regulated by miR-202-3p. In the MCAO/R model, the level of miR-202-3p decreased, while the levels of Ptgs2, Tlr4, and Ccr2 increased compared to the sham group. Knockdown of miR-202-3p exacerbated ischemic reperfusion injury (IRI) through neuroinflammation both in vivo and in vitro. Our study also demonstrated that mRNA and protein levels of Ptgs2, Tlr4, and Ccr2 increased in the MCAO/R model with miR-202-3p knockdown. These findings suggest that differentially expressed genes, including Ptgs2, Tlr4, and Ccr2 may play crucial roles in the neuroinflammation of IS, and their expression may be negatively regulated by miR-202-3p. Our study provides new insights into the regulation of neuroinflammation in IS.
Fas apoptosis inhibitory molecule 2 (FAIM2) is an important member of the transmembrane BAX inhibitor motif containing (TMBIM) family. However, the role of FAIM2 in tumor prognosis and immune infiltration has rarely been studied. Here, we conducted a pan-cancer analysis to explore the role of FAIM2 in various tumors and further verified the results in glioma through molecular biology experiment. FAIM2 expression and clinical stages in tumor samples and para-cancerous samples were analyzed by TIMER2 database, GEPIA database, and the TISIDB database. The role of FAIM2 on prognosis was analyzed via GEPIA2. We utilized the ESTIMATE algorithm to evaluate the ImmuneScore and StromalScore of various tumors. In addition, we explored the correlation between FAIM2 expression and tumor immune cell infiltration by the TIMER2 database. The immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methylation related to FAIM2 were analyzed based on the TCGA database. The correlation between FAIM2 expression with Copy number variations (CNV) and methylation is explored by GSCA database. Protein-Protein Interaction (PPI) analysis was obtained from the STRING database and the CellMiner database was used to explore the association between FAIM2 expression and drug response. FAIM2 co-expression genes were studied by the LinkedOmics database. Immunohistochemistry, Western Blotting Analysis, Cell Viability Assay, Colony Formation Assay, and Edu staining assay were used in the molecular biology experiments section. The FAIM2 expression was down-regulated in most tumors and highly expressed FAIM2 was associated with a better prognosis in several cancers. FAIM2 plays an essential role in the tumor microenvironment and is closely associated with immune Infiltration in various tumors. The expression of FAIM2 was closely correlated to TMB, MSI, MMR, CNV, and DNA methylation. Furthermore, FAIM2 related genes in the PPI network and its co-expression genes in glioma are involved in a large number of immune-related pathways. Molecular biology experiments verified a cancer suppressor role for FAIM2 in glioma. FAIM2 may serve as a potential pan-cancer biomarker for prognosis and immune infiltration, especially in glioma. Moreover, this study might provide a potential target for tumor immunotherapy.
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