Construction of an immune-related gene signature for the prognosis and diagnosis of glioblastoma multiforme

Prognostic Value of Pentraxin3 Protein Expression in Human Malignancies: A Systematic Review and Meta-Analysis

BACKGROUND/OBJECTIVES

Pentraxin 3 (PTX3), a member of the pentraxin superfamily, plays diverse roles in immunity and inflammation. Its dual role in tumorigenesis, exhibiting both protumoral and antitumoral effects, has been the subject of conflicting reports. High PTX3 expression levels in serum and tumor tissues have been associated with poor prognosis in various malignancies, suggesting its potential as a prognostic biomarker. Through this meta-analysis, we aim to comprehensively assess the prognostic significance of PTX3 protein expression in human malignancies and evaluate its potential as a pan-cancer prognostic marker.

METHODS

A systematic literature search was conducted across the PubMed, Embase, Web of Science, MEDLINE, and Cochrane Library databases. Studies were included if they assessed the association between PTX3 protein expression and overall survival (OS) in cancer patients. Hazard ratios (HRs) were pooled using a random-effects model. Subgroup analyses were performed based on the method of PTX3 assessment, and publication bias was evaluated using Egger's and Begg's tests.

RESULTS

Nine studies encompassing 1215 patients were included in the analysis. High PTX3 expression was significantly associated with poorer OS (HR = 1.89, 95% CI = 1.55-2.32, p < 0.01) with no significant heterogeneity (I2 = 0%). Subgroup analysis revealed consistent results across different assessment methods (immunohistochemistry: HR = 1.93, p < 0.01; immunoassay: HR = 1.86, p < 0.01). However, publication bias was detected (Egger's test, p = 0.03).

CONCLUSIONS

High PTX3 protein expression is associated with a poor prognosis in various malignancies, supporting its potential as a prognostic biomarker.

Ferroptosis mediated by TNFSF9 interferes in acute ischaemic stroke reperfusion injury with the progression of acute ischaemic stroke

In this study, we investigated the potential involvement of TNFSF9 in reperfusion injury associated with ferroptosis in acute ischaemic stroke patients, mouse models and BV2 microglia. We first examined TNFSF9 changes in peripheral blood from stroke patients with successful reperfusion, and constructed oxygen-glucose deprivation-reperfusion (OGD-R) on BV2 microglia, oxygen-glucose deprivation for 6 h followed by reoxygenation and re-glucose for 24 h, and appropriate over-expression or knockdown of TNFSF9 manipulation on BV2 cells and found that in the case of BV2 cells encountering OGD-R over-expression of TNFSF9 resulted in increased BV2 apoptosis. Still, the knockdown of TNFSF9 ameliorated apoptosis and ferroptosis. In an in vivo experiment, we constructed TNFSF9 over-expression or knockout mice by intracerebral injection of TNFSF9-OE or sh-TNFSF9 adenovirus. We performed the middle cerebral artery occlusion (MCAO) model on day four, 24 h after ligation of the proximal artery, for half an hour to recanalize. As luck would have it, over-expression of TNFSF9 resulted in increased brain infarct volumes, neurological function scores and abnormalities in TNFSF9-related TRAF1 and ferroptosis-related pathways, but knockdown of TNFSF9 improved brain infarcts in mice as well as reversing TNFSF9-related signalling pathways. In conclusion, our data provide the first evidence that TNFSF9 triggers microglia activation by activating the ferroptosis signalling pathway following ischaemic stroke, leading to brain injury and neurological deficits.

Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes' biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Gliomas are one of the most frequent types of nervous system tumours and have significant morbidity and mortality rates. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research was to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in the TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO and KEGG pathway analyses revealed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LASSO was performed to construct prognostic signatures in the TCGA cohort, and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate Cox regression analyses showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk score and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was found to be closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. And we found that knockdown of RPL39 inhibited the polarization and infiltration of M2 phenotype macrophages. In conclusion, our new prognosis-related model provides more potential therapeutic strategies for glioma patients.