MEGF10, a Glioma Survival-Associated Molecular Signature, Predicts IDH Mutation Status

Exploring scavenger receptor class F member 2 and the importance of scavenger receptor family in prediagnostic diseases

Scavenger Receptor Class F Member 2 (SCARF2), also known as the Type F Scavenger Receptor Family gene, encodes for Scavenger Receptor Expressed by Endothelial Cells 2 (SREC-II). This protein is a crucial component of the scavenger receptor family and is vital in protecting mammals from infectious diseases. Although research on SCARF2 is limited, mutations in this protein have been shown to cause skeletal abnormalities in both SCARF2-deficient mice and individuals with Van den Ende-Gupta syndrome (VDEGS), which is also associated with SCARF2 mutations. In contrast, other scavenger receptors have demonstrated versatile responses and have been found to aid in pathogen elimination, lipid transportation, intracellular cargo transportation, and work in tandem with various coreceptors. This review will concentrate on recent progress in comprehending SCARF2 and the functions played by members of the Scavenger Receptor Family in pre-diagnostic diseases.

Identification of influential observations in high-dimensional survival data through robust penalized Cox regression based on trimming

Penalized Cox regression can efficiently be used for the determination of biomarkers in high-dimensional genomic data related to disease prognosis. However, results of Penalized Cox regression is influenced by the heterogeneity of the samples who have different dependent structure between survival time and covariates from most individuals. These observations are called influential observations or outliers. A robust penalized Cox model (Reweighted Elastic Net-type maximum trimmed partial likelihood estimator, Rwt MTPL-EN) is proposed to improve the prediction accuracy and identify influential observations. A new algorithm AR-Cstep to solve Rwt MTPL-EN model is also proposed. This method has been validated by simulation study and application to glioma microarray expression data. When there were no outliers, the results of Rwt MTPL-EN were close to the Elastic Net (EN). When outliers existed, the results of EN were impacted by outliers. And whenever the censored rate was large or low, the robust Rwt MTPL-EN performed better than EN. and could resist the outliers in both predictors and response. In terms of outliers detection accuracy, Rwt MTPL-EN was much higher than EN. The outliers who "lived too long" made EN perform worse, but were accurately detected by Rwt MTPL-EN. Through the analysis of glioma gene expression data, most of the outliers identified by EN were those "failed too early", but most of them were not obvious outliers according to risk estimated from omics data or clinical variables. Most of the outliers identified by Rwt MTPL-EN were those who "lived too long", and most of them were obvious outliers according to risk estimated from omics data or clinical variables. Rwt MTPL-EN can be adopted to detect influential observations in high-dimensional survival data.

Prognostic role of METTL1 in glioma

Background

Current treatment options for glioma are limited, and the prognosis of patients with glioma is poor as the available drugs show low therapeutic efficacy. Furthermore, the molecular mechanisms associated with glioma remain poorly understood. METTL1 mainly catalyzes the formation of N(7)-methylguanine at position 46 of the transfer RNA sequence, thereby regulating the translation process. However, the role of METTL1 in glioma has not been studied to date. The purpose of this study was to analyze the expression and prognosis of METTL1 in glioma, and to explore the potential analysis mechanism.

Methods

Data from five publicly available databases were used to analyze METTL1 expression across different tumor types and its differential expression between carcinoma and adjacent normal tissues. The expression of METTL1 in glioma was further validated using real-time polymerase chain reaction and immunohistochemistry. Meanwhile, siRNA was used to knockdown METTL1 in U87 glioma cells, and the resultant effect on glioma proliferation was verified using the Cell Counting Kit 8 (CCK8) assay. Furthermore, a nomogram was constructed to predict the association between METTL1 expression and the survival rate of patients with glioma.

Results

METTL1 expression increased with increasing glioma grades and was significantly higher in glioma than in adjacent noncancerous tissues. In addition, high expression of METTL1 promoted cell proliferation. Moreover, METTL1 expression was associated with common clinical risk factors and was significantly associated with the prognosis and survival of patients with glioma. Univariate and multivariate Cox regression analyses revealed that METTL1 expression may be used as an independent prognostic risk factor for glioma. Furthermore, results of functional enrichment and pathway analyses indicate that the mechanism of METTL1 in glioma is potentially related to the MAPK signaling pathway.

Conclusions

High METTL1 expression is significantly associated with poor prognosis of patients with glioma and may represent a valuable independent risk factor. In addition, high expression of METTL1 promotes glioma proliferation and may regulate mitogen-activated protein kinase (MAPK) signaling pathway. Thus, METTL1 may be a potential biomarker for glioma. Further investigations are warranted to explore its clinical use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02346-4.

Identification of the Prognostic Signatures of Glioma With Different PTEN Status

The high-grade glioma is characterized by cell heterogeneity, gene mutations, and poor prognosis. The deletions and mutations of the tumor suppressor gene PTEN (5%-40%) in glioma patients are associated with worse survival and therapeutic resistance. Characterization of unique prognosis molecular signatures by PTEN status in glioma is still unclear. This study established a novel risk model, screened optimal prognostic signatures, and calculated the risk score for the individual glioma patients with different PTEN status. Screening results revealed fourteen independent prognostic gene signatures in PTEN-wt and three in the -50PTEN-mut subgroup. Moreover, we verified risk score as an independent prognostic factor significantly correlated with tumor malignancy. Due to the higher malignancy of the PTEN-mut gliomas, we explored the independent prognostic signatures (CLCF1, AEBP1, and OS9) for a potential therapeutic target in PTEN-mut glioma. We further separated IDH wild-type glioma patients into GBM and LGG to verify the therapeutic target along with PTEN status, notably, the above screened therapeutic targets are also significant prognostic genes in both IDH-wt/PTEN-mut GBM and LGG patients. We further identified the small molecule compound (+)-JQ1 binds to all three targets, indicating a potential therapy for PTEN-mut glioma. In sum, gene signatures and risk scores in the novel risk model facilitate glioma diagnosis, prognosis prediction, and treatment.

The Promising IgSF11 Immune Checkpoint Is Highly Expressed in Advanced Human Gliomas and Associates to Poor Prognosis

Glioma is the most prevalent primary brain tumor. Immune checkpoint blockade has made a great stride in mending patient’s clinical outcome for multiple types of cancers. However, PD-1, CTLA-4, or VEGF blockade exhibited only poor outcome in glioma patients. This study aimed to explore the expression and role of IgSF11, an emerging immune checkpoint and a ligand of VISTA, in human gliomas. IgSF11 mRNA expression was assessed in human glioma patients at different grades using 2 independent cohorts, a set of 52 Moroccan samples, including 20 glioma tissues, 22 PBMC samples taken before and 10 PBMC samples taken after surgery; and a series of 667 patients from TCGA. In parallel, immunohistochemistry was performed to evaluate IgSF11 protein staining. IgSF11 gene expression was significantly upregulated in high grade glioma tissues, compared to low grade. IgSF11 protein also showed a significant expression in low and high-grade gliomas. Interestingly, IgSF11 expression seemed to correlate positively with other critical immune checkpoints such as PD1, PDL-1, VISTA, and surprisingly negatively with CTLA-4. Although, T cell markers appeared higher in advanced gliomas, T cell-produced pro-inflammatory genes showed similar expression levels, highly likely because of the potent immunosuppressive microenvironment. Indeed, increased expression of IgSF11 in advanced human gliomas associated with a poor overall survival. Our data strongly suggest that IgSF11 is an immune checkpoint, which is upregulated in advanced human gliomas and contributes to the immunosuppressive state resulting in a poor clinical outcome in glioma patients. IgSF11 could be considered as a possible promising therapeutic target in advanced human gliomas.

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

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We identified ten hub genes which were driving IDH status in GBM and LGG.

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We constructed a prognostic model for IDH-mutant patients.

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Our findings have important clinical implications for accurate treatment in glioma.

Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.

Generation of induced neural stem cells with inducible IDH1R132H for analysis of glioma development and drug testing

Mutation in isocitrate dehydrogenase 1 (IDH1R132H) occurs in various types of cancer, including low and high grade gliomas. Despite high incidence indicating its central role in tumor initiation and progression there are no targeted therapies directed against this oncogene available in the clinic. This is due to the limited understanding of the role of IDH1R132H in carcinogenesis, which is further propagated by the lack of appropriate experimental models. Moreover, proper in vitro models for analysis of gliomagenesis are required. In this study, we employed a Tet On system to generate human induced neural stem cells with doxycycline-inducible IDH1R132H. Equivalent expression of both forms of IDH1 in the presented model remains similar to that described in tumor cells. Additional biochemical analyses further confirmed tightly controlled gene regulation at protein level. Formation of a functional mutant IDH1 enzyme was supported by the production of D-2-hydroxyglutarate (D2HG). All samples tested for MGMT promoter methylation status, including parental cells, proved to be partially methylated. Analysis of biological effect of IDH1R132H revealed that cells positive for oncogene showed reduced differentation efficiency and viability. Inhibition of mutant IDH1 with selective inhibitor efficiently suppressed D2HG production as well as reversed the effect of mutant IDH1 protein on cell viability. In summary, our model constitutes a valuable platform for studies on the molecular basis and the cell of origin of IDH-mutant glioma (e.g. by editing P53 in these cells and their derivatives), as well as a reliable experimental model for drug testing.

Galectin-9: A Predictive Biomarker Negatively Regulating Immune Response in Glioma Patients

BACKGROUND

Glioma is the most frequent primary brain tumor. Immunotherapy is one of the most promising therapeutic approaches for gliomas. T cell immunoglobulin domain and mucin domain-3 can induce the malignancy of gliomas. The function of galectin-9 (GAL-9), as one of the ligands of T cell immunoglobulin domain and mucin domain-3, in glioma has remained elusive. The aim of this study was to characterize the expression of GAL-9 in patients with glioma.

METHODS

This study enrolled 1292 patients with glioma from the GSE 16011 array set, the Chinese Glioma Genome Atlas, and The Cancer Genome Atlas datasets. Kaplan-Meier analysis was undertaken to explore the prognostic value of GAL-9. Graphpad software and R language were used for statistical analysis.

RESULTS

Expression of GAL-9 was highly correlated with major clinical and molecular features. Patients with high expression of GAL-9 were more susceptible to development of malignant tumors. Gene Ontology analysis revealed that expression of GAL-9 was closely associated with function of immune response in glioma. Clinically, the results of Kaplan-Meier analysis showed that expression of GAL-9 was negatively associated with overall survival in all grades of glioma including high-grade gliomas. High expression of GAL-9 was an independent indicator of poor prognosis.

CONCLUSIONS

Our results highlight the pivotal role of GAL-9 in regulation of immune suppressive features of gliomas and indicate that GAL-9 is a promising target for cancer immunotherapy and may lead to development of further therapies.