Prognosis Of Low-grade Glioma Patients Research Articles

Potential value of expression of receptor accessory protein 4 for evaluating the prognosis of lower-grade glioma patients.

REEP4 is involved in the regulation of the biological process of mitosis. Lower grade glioma (LGG), as a malignant tumor, is accompanied by abnormalities in mitosis, but there have been no reports of REEP4 so far. We collected transcriptome data, DNA methylation data and the clinical characteristics of thousands of patients with LGG. Various big data analysis methods and molecular biology experiments were employed to reveal the impact of REEP4 on the pathological process of LGG. It was found that the expression of REEP4 was significantly elevated and negatively regulated by its methylation site. Therefore, both the high expression of REEP4 and low methylation state of cg16311504 showed that the patients are correlated with lower patient survival rate. In addition, high REEP4 expression participates in the regulation of various cancer-related cellular signaling pathways, such as the cell cycle, MAPK signaling pathway, NOD-like receptor signaling pathway, etc. More importantly, the level of immune cell infiltration significantly increased in the high expression group of REEP4 in the LGG tumor microenvironment and REEP4 has a high positive correlation with PD-L1 and other immune checkpoints. In brief, this study is the first to introduce REEP4 in malignant tumors, which can be used as an independent risk factor that participates in the malignant process of LGG. More importantly, REEP4 has the potential to become a new star in the field of anti-tumor treatment.

NUDT21 alters glioma migration through differential alternative polyadenylation of LAMC1.

Gliomas and their surrounding microenvironment constantly interact to promote tumorigenicity, yet the underlying posttranscriptional regulatory mechanisms that govern this interplay are poorly understood. Utilizing our established PAC-seq approach and PolyAMiner bioinformatic analysis pipeline, we deciphered the NUDT21-mediated differential APA dynamics in glioma cells. We identified LAMC1 as a critical NUDT21 alternative polyadenylation (APA) target, common in several core glioma-driving signaling pathways. qRT-PCR analysis confirmed that NUDT21-knockdown in glioma cells results in the preferred usage of the proximal polyA signal (PAS) of LAMC1. Functional studies revealed that NUDT21-knockdown-induced 3'UTR shortening of LAMC1 is sufficient to cause translational gain, as LAMC1 protein is upregulated in these cells compared to their respective controls. We demonstrate that 3'UTR shortening of LAMC1 after NUDT21 knockdown removes binding sites for miR-124/506, thereby relieving potent miRNA-based repression of LAMC1 expression. Remarkably, we report that the knockdown of NUDT21 significantly promoted glioma cell migration and that co-depletion of LAMC1 with NUDT21 abolished this effect. Lastly, we observed that LAMC1 3'UTR shortening predicts poor prognosis of low-grade glioma patients from The Cancer Genome Atlas. This study identifies NUDT21 as a core alternative polyadenylation factor that regulates the tumor microenvironment through differential APA and loss of miR-124/506 inhibition of LAMC1. Knockdown of NUDT21 in GBM cells mediates 3'UTR shortening of LAMC1, contributing to an increase in LAMC1, increased glioma cell migration/invasion, and a poor prognosis.

A radiomics feature-based nomogram to predict telomerase reverse transcriptase promoter mutation status and the prognosis of lower-grade gliomas

To explore the predictive value of the radiomics feature-based nomogram for predicting telomerase reverse transcriptase (TERT) promoter mutation status and prognosis of lower-grade gliomas (LGGs) non-invasively. One hundred and seventy-six LGG patients (123 in the training cohort and 53 in the validation cohort) were enrolled retrospectively. A total of 851 radiomics features were extracted from contrast-enhanced magnetic resonance imaging (MRI) images. The radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) method and a rad-score was calculated. Multivariate logistic regression analysis was used to build a radiomics signature based on rad-score, participant's age, and gender, and a radiomics nomogram was used to represent this signature. The performance of the signature was evaluated by receiver operating characteristic (ROC) curve analysis, and the patient prognosis was stratified based on the TERT promoter mutation status and the radiomics signature. Seven robust radiomics features were selected by LASSO and the radiomics signature showed good performance for predicting the TERT promoter mutation status, with an area under the curve (AUC) of 0.900 (0.832-0.946) and 0.873 (0.753-0.948) in the training and validation datasets. With a median overall survival time of 28.5 months, the radiomics signature stratified the LGG patients into two risk groups with significantly different prognosis (log-rank=47.531, p<0.001). The radiomics feature-based nomogram is a promising approach for predicting the TERT promoter mutation status preoperatively and evaluating the prognosis of lower-grade glioma patients non-invasively.

An integrative multi-omics analysis based on liquid\u2013liquid phase separation delineates distinct subtypes of lower-grade glioma and identifies a prognostic signature

BackgroundEmerging evidences have indicated that the aberrant liquid–liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG).MethodsWe integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment.ResultsBased on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4.We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent.ConclusionsThis study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.

Molecular and Clinical Characterization of a Novel Prognostic and Immunologic Biomarker GPSM3 in Low-Grade Gliomas.

Background: as the most common malignancy of the central nervous system, low-grade glioma (LGG) patients suffered a poor prognosis. Tumor microenvironment, especially immune components, plays an important role in the progression of tumors. Thus, it is critical to explore the key immune-related genes, a comprehensive understanding of the TME in LGG helps us find novel cancer biomarkers and therapeutic targets. Methods: the GPSM3 expression level and the correlations between clinical characteristics and GPSM3 levels were analyzed with the data from CGGA and TCGA dataset. Univariate and multivariate cox regression model were built to predict the prognosis of LGG patients with multiple factors. Then the correlation between GPSM3 with immune cell infiltration was explored by ESTIMATE, CIBERSORT and TIMER2.0. At last, the correlation analyzed between GPSM3 expression and immune checkpoint related genes were also analyzed. Results: GPSM3 expression was overexpressed in LGG and negatively correlated to the GPSM3 DNA methylation. Univariate and multivariate Cox analysis demonstrated that GPSM3 expression was an independent prognostic factor in LGG patients. Functional characterization of GPSM3 revealed that it was associated with many immune processes to tumor cells. GPSM3 expression was positive related to the immune score, Stromal scores and ESTIMATE scores, but negative related to the Tumor purity. Immune features in the TME of GPSM3-high LGG group is characterized by a higher infiltrating of regulatory T cells, neutrophils, macrophages M2, and a lower proportion of monocytes than to the GPSM3-low group. Furthermore, GPSM3 expression exhibited significant correlations with the immune checkpoint-related genes, especially PD-1, PD-L1, PD-L2, CTLA4 and TIM3. Conclusions: these findings proved that GPSM3 could serve as a prognostic biomarker and potential immunotherapy target for LGG.

A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients.

BackgroundAs immunotherapy has received attention as new treatments for brain cancer, the role of inflammation in the process of glioma is of particular importance. Increasing studies have further shown that long non-coding RNAs (lncRNAs) are important factors that promote the development of glioma. However, the relationship between inflammation-related lncRNAs and the prognosis of glioma patients remains unclear. The purpose of this study is to construct and validate an inflammation-related lncRNA prognostic signature to predict the prognosis of low-grade glioma patients.MethodsBy downloading and analyzing the gene expression data and clinical information of the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) patients with low-grade gliomas, we could screen for inflammatory gene-related lncRNAs. Furthermore, through Cox and the Least Absolute Shrinkage and Selection Operator regression analyses, we established a risk model and divided patients into high- and low-risk groups based on the median value of the risk score to analyze the prognosis. In addition, we analyzed the tumor mutation burden (TMB) between the two groups based on somatic mutation data, and explored the difference in copy number variations (CNVs) based on the GISTIC algorithm. Finally, we used the MCPCounter algorithm to study the relationship between the risk model and immune cell infiltration, and used gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to explore the enrichment pathways and biological processes of differentially expressed genes between the high- and low-risk groups.ResultsA novel prognostic signature was constructed including 11 inflammatory lncRNAs. This risk model could be an independent prognostic predictor. The patients in the high-risk group had a poor prognosis. There were significant differences in TMB and CNVs for patients in the high- and low-risk groups. In the high-risk group, the immune system was activated more significantly, and the expression of immune checkpoint-related genes was also higher. The GSEA, GO, and KEGG analyses showed that highly expressed genes in the high-risk group were enriched in immune-related processes, while lowly expressed genes were enriched in neuromodulation processes.ConclusionThe risk model of 11 inflammation-related lncRNAs can serve as a promising prognostic biomarker for low-grade gliomas patients.

Multi-Omics Data Integration Analysis of an Immune-Related Gene Signature in LGG Patients With Epilepsy.

BackgroundThe tumor immune microenvironment significantly affects tumor occurrence, progression, and prognosis, but its impact on the prognosis of low-grade glioma (LGG) patients with epilepsy has not been reported. Hence, the purpose of this study is to explore its effect on LGG patients with epilepsy.MethodsThe data of LGG patients derived from the TCGA database. The level of immune cell infiltration and the proportion of 22 immune cells were evaluated by ESTIMATE and CIBERSORT algorithms, respectively. The Cox and LASSO regression analysis was adopted to determine the DEGs, and further established the clustering and risk score models. The association between genomic alterations and risk score was investigated using CNV and somatic mutation data. GSVA was adopted to identify the immunological pathways, immune infiltration and inflammatory profiles related to the signature genes. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm and GDSC database were used to predict the patient’s response to immunotherapy and chemotherapy, respectively.ResultsThe prognosis of LGG patients with epilepsy was associated with the immune score. Three prognostic DEGs (ABCC3, PDPN, and INA) were screened out. The expression of signature genes was regulated by DNA methylation. The clustering and risk score models could stratify glioma patients into distinct prognosis groups. The risk score was an independent predictor in prognosis, with a high risk-score indicating a poor prognosis, more malignant clinicopathological and genomic aberration features. The nomogram had the better predictive ability. Patients at high risk had a higher level of macrophage infiltration and increased inflammatory activities associated with T cells and macrophages. While the higher percentage of NK CD56bright cell and more active inflammatory activity associated with B cell were present in the low-risk patients. The signature genes participated in the regulation of immune-related pathways, such as IL6-JAK-STAT3 signaling, IFN-α response, IFN-γ response, and TNFA-signaling-via-NFKB pathways. The high-risk patients were more likely to benefit from anti-PD1 and temozolomide (TMZ) treatment.ConclusionAn immune-related gene signature was established based on ABCC3, PDPN, and INA, which can be used to predict the prognosis, immune infiltration status, immunotherapy and chemotherapy response of LGG patients with epilepsy.

Integrative Analysis of a Novel Eleven-Small Nucleolar RNA Prognostic Signature in Patients With Lower Grade Glioma.

ObjectiveThe present study used the RNA sequencing (RNA-seq) dataset to identify prognostic snoRNAs and construct a prognostic signature of The Cancer Genome Atla (TCGA) lower grade glioma (LGG) cohort, and comprehensive analysis of this signature.MethodsRNA-seq dataset of 488 patients from TCGA LGG cohort were included in this study. Comprehensive analysis including function enrichment, gene set enrichment analysis (GSEA), immune infiltration, cancer immune microenvironment, and connectivity map (CMap) were used to evaluate the snoRNAs prognostic signature.ResultsWe identified 21 LGG prognostic snoRNAs and constructed a novel eleven-snoRNA prognostic signature for LGG patients. Survival analysis suggests that this signature is an independent prognostic risk factor for LGG, and the prognosis of LGG patients with a high-risk phenotype is poor (adjusted P = 0.003, adjusted hazard ratio = 2.076, 95% confidence interval = 1.290–3.340). GSEA and functional enrichment analysis suggest that this signature may be involved in the following biological processes and signaling pathways: such as cell cycle, Wnt, mitogen-activated protein kinase, janus kinase/signal transducer and activator of tran-ions, T cell receptor, nuclear factor-kappa B signaling pathway. CMap analysis screened out ten targeted therapy drugs for this signature: 15-delta prostaglandin J2, MG-262, vorinostat, 5155877, puromycin, anisomycin, withaferin A, ciclopirox, chloropyrazine and megestrol. We also found that high- and low-risk score phenotypes of LGG patients have significant differences in immune infiltration and cancer immune microenvironment.ConclusionsThe present study identified a novel eleven-snoRNA prognostic signature of LGG and performed a integrative analysis of its molecular mechanisms and relationship with tumor immunity.

Multi-omics landscape of circadian rhythm pathway alterations in Glioma

ABSTRACT Circadian rhythm pathway was demonstrated pathological functions in glioma on single-gene level. We aim to depict the multi-omics landscape of circadian rhythm pathway alteration in glioma using bioinformatic analyses. Multi-omics data were obtained from “cBioPortal” database. Comparisons were done regarding clinical parameters, differential-expressed genes and functional annotations. A pathway index was generated using the expression data from TCGA and GTEx to quantify the general alteration level of the pathway with clinical association of circadian rhythm pathway index explored. A total of 30 genes were mapped on the circadian rhythm pathway. Genomic profile ofcircadian rhythm pathway genes exhibited distinct characteristics on multiple levels between lower grade glioma (LGG) and glioblastoma multiforme (GBM) patients. LGG patients presented significantly higher frequencies of multi-omics mutations, as well as significant clinical relevance, on single-gene level. Differential-expressed genes between LGG and GBM patients revealed different functions between subtypes that related to the alteration of circadian rhythm pathway. LGG have significantly higher pathway index than normal brain tissue, while GBM significantly lower than normal tissue (P < 0.01), indicating distinctly altered circadian pathway in LGG. Circadian rhythm pathway index correlated with the prognosis of LGG, but not GBM, patients, with higher score indicating better survival outcome (LGG: HR = 0.39, 95% CI: 0.26 − 0.59, P < 0.001). In conclusion, LGG have more multi-omics alterations of circadian rhythm pathway than GBM. Quantification of circadian rhythm pathway using pathway index demonstrated hyperactivated pathway status in LGG and correlated with the prognosis of LGG patients.

Hypermethylation of LATS2 Promoter and Its Prognostic Value in IDH-Mutated Low-Grade Gliomas.

Mutations in the enzyme isocitrate dehydrogenase 1/2 (IDH1/2) are the most common somatic mutations in low-grade glioma (LGG). The Hippo signaling pathway is known to play a key role in organ size control, and its dysregulation is involved in the development of diverse cancers. Large tumor suppressor 1/2 (LATS1/2) are core Hippo pathway components that phosphorylate and inactivate Yes-associated protein (YAP), a transcriptional co-activator that regulates expression of genes involved in tumorigenesis. A recent report from The Cancer Genome Atlas (TCGA) has highlighted a frequent hypermethylation of LATS2 in IDH-mutant LGG. However, it is unclear if LATS2 hypermethylation is associated with YAP activation and prognosis of LGG patients. Here, we performed a network analysis of the status of the Hippo pathway in IDH-mutant LGG samples and determined its association with cancer prognosis. Combining TCGA data with our biochemical assays, we found hypermethylation of LATS2 promoter in IDH-mutant LGG. LATS2 hypermethylation, however, did not translate into YAP activation but highly correlated with IDH mutation. LATS2 hypermethylation may thus serve as an alternative for IDH mutation in diagnosis and a favorable prognostic factor for LGG patients.