Prognosis Of Low-grade Gliomas Research Articles

Establishment and Validation of a Ferroptosis-Related lncRNA Signature for Prognosis Prediction in Lower-Grade Glioma.

BackgroundThe prognosis of lower-grade glioma (LGG) is highly variable, and more accurate predictors are still needed. The aim of our study was to explore the prognostic value of ferroptosis-related long non-coding RNAs (lncRNAs) in LGG and to develop a novel risk signature for predicting survival with LGG.MethodsWe first integrated multiple datasets to screen for prognostic ferroptosis-related lncRNAs in LGG. A least absolute shrinkage and selection operator (LASSO) analysis was then utilized to develop a risk signature for prognostic prediction. Based on the results of multivariate Cox analysis, a prognostic nomogram model for LGG was constructed. Finally, functional enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), immunity, and m6A correlation analyses were conducted to explore the possible mechanisms by which these ferroptosis-related lncRNAs affect survival with LGG.ResultsA total of 11 ferroptosis-related lncRNAs related to the prognosis of LGG were identified. Based on prognostic lncRNAs, a risk signature consisting of 8 lncRNAs was constructed and demonstrated good predictive performance in both the training and validation cohorts. Correlation analysis suggested that the risk signature was closely linked to clinical features. The nomogram model we constructed by combining the risk signature and clinical parameters proved to be more accurate in predicting the prognosis of LGG. In addition, there were differences in the levels of immune cell infiltration, immune-related functions, immune checkpoints, and m6A-related gene expression between the high- and low-risk groups.ConclusionIn summary, our ferroptosis-related lncRNA signature exhibits good performance in predicting the prognosis of LGG. This study may provide useful insight into the treatment of LGG.

DNA methylation-regulated YTHDF2 correlates with cell migration and immune cell infiltration in glioma.

Background: Glioma is a lethal malignant brain tumor, it comprises about 80% of all malignant brain tumours. Mounting evidence has reported that YTHDF2 plays a significant role in the cancer progression. However, the effects of YTHDF2 on the prognosis of low-grade gliomas (LGGs) and its correlation with tumor immune infiltration are unclear. The present study was designed to determine the biological functions of YTHDF2 in glioma and to evaluate the association of YTHDF2 expression with glioma progression.Methods: Clinical data on patients with glioma were obtained from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), the Gene Expression Omnibus (GEO), as well as the Rembrandt and Gravendeel databases. The correlations among YTHDF2 expression, pathological characteristics, glioma progression and clinical outcome were evaluated. In addition, the correlation of YTHDF2 expression with immune cell infiltration was analyzed too.Results: We found that YTHDF2 was significantly up-regulated in LGGs which correlated with tumor grade and poor prognosis. Interestingly, we showed that YTHDF2 expression in LGG was associated with copy number variation, DNA hypomethylation, and induced transcription factor YY1. Besides, KEGG pathway analysis shows that YTHDF2 mainly participates in the immune response and oncogenic signaling pathway. Additionally, YTHDF2 is positively associated with diverse immune cells infiltration, immune cells, and multiple immune checkpoint molecules. Finally, we confirmed that YTHDF2 was highly expressed in LGGs tissues and correlated with the tumor grade with immunohistochemistry assay. More importantly, our results demonstrated that YTHDF2 was elevated in GBM cells. Knockdown of YTHDF2 significantly inhibits the proliferation and migration of GBM cells.Conclusion: YTHDF2 correlates with glioma progression and immune cell infiltration, suggesting that YTHDF2 may be a useful prognostic biomarker for glioma.

New biomarker: the gene HLA-DRA associated with low-grade glioma prognosis

BackgroundLow-grade gliomas (LGG) are WHO grade II tumors presenting as the most common primary malignant brain tumors in adults. Currently, LGG treatment involves either or a combination of surgery, radiation therapy, and chemotherapy. Despite the knowledge of constitutive genetic risk factors contributing to gliomas, the role of single genes as diagnostic and prognostic biomarkers is limited. The aim of the current study is to discover the predictive and prognostic genetic markers for LGG.MethodsTranscriptome data and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We first performed the tumor microenvironment (TME) survival analysis using the Kaplan-Meier method. An analysis was undertaken to screen for differentially expressed genes. The function of these genes was studied by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Following which a protein-protein interaction network (PPI) was constructed and visualized. Univariate and multivariate COX analyses were performed to obtain the probable prognostic genes. The key genes were selected by an intersection of core and prognostic genes. A clinical correlation analysis of single-gene expression was undertaken. GSEA enrichment analysis was performed to identify the function of key genes. Finally, a single gene-related correlation analysis was performed to identify the core immune cells involved in the development of LGG.ResultsA total of 529 transcriptome data and 515 clinical samples were obtained from the TCGA. Immune cells and stromal cells were found to be significantly increased in the LGG microenvironment. The top five core genes intersected with the top 38 prognostically relevant genes and two key genes were identified. Our analysis revealed that a high expression of HLA-DRA was associated with a poor prognosis of LGG. Correlation analysis of immune cells showed that HLA-DRA expression level was related to immune infiltration, positively related to macrophage M1 phenotype, and negatively related to activation of NK cells.ConclusionsHLA-DRA may be an independent prognostic indicator and an important biomarker for diagnosing and predicting survival in LGG patients. It may also be associated with the immune infiltration phenotype in LGG.

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.

Construction of a Risk Model to Predict the Prognosis and Immunotherapy of Low-Grade Glioma Ground on 7 Ferroptosis-Related Genes.

PurposeFerroptosis is closely associated with tumors. The purpose of this study was to investigate the correlation between ferroptosis and prognosis of low grade glioma (LGG) via construction and verification of a risk model.Patients and MethodsThe data of LGG were downloaded from public databases. Through LASSO analysis of characteristic genes, a gene signature was constructed. Patients into were divided two groups based on risk score. Subsequently, survival, clinical phenotype, functional enrichment, immune cell infiltration and somatic mutation analysis were performed. In addition, whether ferroptosis-related genes (FRGs) signature can predict the patient’s response to anti-PD-1/PD-L1 immunotherapy was also investigated.ResultsFRGs signature had strong prognostic assessment ability, and high risk score was associated with poor overall survival (OS) of LGG. The high risk score group had higher degree of immune cell infiltration, stronger stromal activity, higher immune score, and high expression of immune checkpoint. In low risk score group anti-PD-1/PD-L1 immunotherapy has significant therapeutic advantages and clinical response. Genes and frequency of somatic mutations and clinical phenotypes in the high and low risk score groups were significantly different.ConclusionA prognostic model based on 7 FRGs can be used to predict the prognosis and immunotherapeutic response of LGG.

CLSPN is a potential biomarker associated with poor prognosis in low-grade gliomas based on a multi-database analysis

BackgroundThe oncogene CLSPN, also known as claspin, has regulatory effects in a variety of tumours; however, it is not clear whether CLSPN is a therapeutic target in low-grade gliomas (LGG). In this study, the prognostic value of CLSPN in LGG and its role as an immunotherapeutic target were evaluated. MethodsTranscriptome and methylation data for thousands of patients with glioma were collected from various databases, including The Cancer Genome Atlas, Chinese Glioma Genome Atlas, and Gene Expression Omnibus. Subsequently, a series of bioinformatics methods were used to evaluate the relationships between CLSPN and prognosis, clinical features, methylation status, immune cells, and molecular signaling pathways in LGG. ResultsCLSPN expression levels were positively correlated with major malignant characteristics of LGG, and low expression of CLSPN was associated with a better prognosis. The methylation sites cg04263115 and cg06100291 negatively regulated the expression of CLSPN, and increased methylation levels at these sites were related to a longer survival time in patients with LGG. CLSPN was positively correlated with tumour-infiltrating immune cells and showed high copy number variation in these cells. There was a positive regulatory relationship between CLSPN expression and programmed death-1 (PD-1) and programmed cell death ligand 1 (PD-L1). A gene set enrichment analysis revealed that CLSPN activates a variety of cancer signaling pathways. ConclusionCLSPN was identified as an independent risk factor for LGG with excellent prognostic value.

An Ensemble Deep Learning Model with a Gene Attention Mechanism for Estimating the Prognosis of Low-Grade Glioma.

Simple SummaryThis paper proposes a deep learning model for prognosis estimation and an attention mechanism for gene expression. In the prognosis estimation of low-grade glioma (LGG), the proposed model, Gene Attention Ensemble NETwork (GAENET), demonstrated superior performance compared to conventional models, where GAENET exhibited an improvement of 7.2% compared to the second-best model. By the proposed gene attention, HILS1 was discovered as the most significant prognostic gene for LGG. While HILS1 is classified as a pseudogene, it functions as a biomarker for predicting the prognosis of LGG and has been shown to have the ability to regulate the expression of other prognostic genes.While estimating the prognosis of low-grade glioma (LGG) is a crucial problem, it has not been extensively studied to introduce recent improvements in deep learning to address the problem. The attention mechanism is one of the significant advances; however, it is still unclear how attention mechanisms are used in gene expression data to estimate prognosis because they were designed for convolutional layers and word embeddings. This paper proposes an attention mechanism called gene attention for gene expression data. Additionally, a deep learning model for prognosis estimation of LGG is proposed using gene attention. The proposed Gene Attention Ensemble NETwork (GAENET) outperformed other conventional methods, including survival support vector machine and random survival forest. When evaluated by C-Index, the GAENET exhibited an improvement of 7.2% compared to the second-best model. In addition, taking advantage of the gene attention mechanism, HILS1 was discovered as the most significant prognostic gene in terms of deep learning training. While HILS1 is known as a pseudogene, HILS1 is a biomarker estimating the prognosis of LGG and has demonstrated a possibility of regulating the expression of other prognostic genes.

Expression of Glutathione Peroxidases and Its Effect on Clinical Prognosis in Glioma Patients

Objective To investigate the relationship between the expression of glutathione peroxidase(GPX)genes and the clinical prognosis in glioma patients,and to construct and evaluate the model for predicting the prognosis of glioma. Methods The clinical information and GPX expression of 663 patients,including 153 patients of glioblastoma(GBM)and 510 patients of low-grade glioma(LGG),were obtained from The Cancer Genome Atlas(TCGA)database.The relationship between GPX expression and patient survival was analyzed.The key GPX affecting the prognosis of glioma was screened out by single- and multi-factor Cox's proportional-hazards regression models and validated by least absolute shrinkage and selection operator(Lasso)regression.Finally,we constructed the model for predicting the prognosis of glioma with the screening results and then used concordance index and calibration curve respectively to evaluate the discrimination and calibration of model. Results Compared with those in the control group,the expression levels of GPX1,GPX3,GPX4,GPX7,and GPX8 were up-regulated in glioma patients(all P<0.001).Moreover,the expression levels of other GPX except GPX3 were higher in GBM patients than in LGG patients(all P<0.001).The Kaplan-Meier curves showed that the progression-free survival of GBM with high expression of GPX1(P=0.013)and GPX4(P=0.040),as well as the overall survival,disease-specific survival,and progression-free survival of LGG with high expression of GPX1,GPX7,and GPX8,was shortened(all P<0.001).GPX7 and GPX8 were screened out as the key factors affecting the prognosis of LGG.The results were further used to construct a nomogram model,which suggested GPX7 was the most important variable.The concordance index of the model was 0.843(95%CI=0.809-0.853),and the calibration curve showed that the predicted and actual results had good consistency. Conclusion GPX7 is an independent risk factor affecting the prognosis of LGG,and the nomogram model constructed with it can be used to predict the survival rate of LGG.

Identification of a novel immune-related lncRNA signature to predict prognostic outcome and therapeutic efficacy of LGG.

Recent studies have shown that the prognosis of low-grade glioma (LGG) patients is closely correlated with the immune infiltration and the expression of long-stranded non-coding RNAs (lncRNAs). It's meaningful to find the immune-related lncRNAs (irlncRNAs). The Cancer Genome Atlas (TCGA) data was employed in the study to identify irlncRNAs and Cox regression model was applied to construct the risk proportional model based on irlncRNAs. In the study, we retrieved transcriptomic data of LGG from TCGA and identified 10 lncRNA signatures consisting of irlncRNAs by co-expression analysis. Then we plotted 1-year receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC). LGG patients were divided into high-risk and low-risk groups according to the risk model. We found there were differences in survival prognosis, clinical characteristics, degree of immune cell infiltration, expression of immune gene checkpoint genes, and sensitivity to the commonly used chemotherapeutic agents of high-risk and low-risk groups. IrlncRNA-based risk assessment model can be used as a prognostic tool to predict the survival outcome and clinical characteristics of LGG and to guide treatment options.

Siglec-9, a Putative Immune Checkpoint Marker for Cancer Progression Across Multiple Cancer Types.

Siglec-9, a cell surface transmembrane receptor mainly expressed on B cells, CD56+ NK cells, and CD4+ and CD8+ T cells, is strongly related to the tumor immune microenvironment. However, the expression pattern of Siglec-9 and its prognostic potential have not been investigated in a pan-cancer perspective. This study aimed to explore the association of Siglec-9 with prognosis, tumor stage, molecular subtype, and the immune microenvironment in pan-cancer. The mRNA expression of Siglec-9 was obtained from The Cancer Genome Atlas (TCGA), the Broad Institute Cancer Cell Line Encyclopedia (CCLE), and Genotype-Tissue Expression (GTEx). The relationship between Siglec-9 mRNA expression and prognosis was evaluated by the Kaplan–Meier analysis. The correlation between Siglec-9 and tumor-infiltrating immune cells, immune subtype, and molecular subtype was evaluated on Tumor Immune Estimation Resource (TIMER) and Integrated Repository Portal for Tumor-Immune System Interactions (TISIDB). The correlation between Siglec-9 expression and immune checkpoint, mismatch repair (MMR), DNA methyltransferase (DNMT), tumor mutation burden (TMB), and microsatellite instability (MSI) was also analyzed. It showed that Siglec-9 expression was significantly altered in most TCGA tumors. Siglec-9 expression was associated with the prognosis of patients with adrenocortical carcinoma (ACC), lung adenocarcinoma (LUSC), thymoma (THYM), colon adenocarcinoma (COAD), glioblastoma multiforme (GBM), prostate adenocarcinoma (PRAD), esophageal carcinoma (ESCA), and brain lower-grade glioma (LGG). Particularly, increased Siglec-9 expression was strongly correlated with poor prognosis in LGG. Correlation between Siglec-9 expression and tumor stage was also observed in various cancers. In addition, Siglec-9 was positively associated with infiltration of immune cells including neutrophils, dendritic cells (DCs), macrophage, and CD4+ and CD8+ T cells. Moreover, a significant correlation between Siglec-9 and MSI, TMB, MMR, DNMT, immune checkpoint, immune subtype, molecular subtype, and immunomodulators was observed in multiple cancers. Specifically, poor prognostic value and strong correlation to immune cell infiltration were verified with the LGG dataset from the Chinese Glioma Genome Atlas (CGGA). These findings indicated that Siglec-9 can be a novel biomarker and a potential target for cancer immunotherapy.

Tenascin-C can Serve as an Indicator for the Immunosuppressive Microenvironment of Diffuse Low-Grade Gliomas.

PurposeThe development and progression of glioma are associated with the tumor immune microenvironment. Diffuse low-grade gliomas (LGGs) with higher immunosuppressive microenvironment tend to have a poorer prognosis. The study aimed to find a biological marker that can reflect the tumor immune microenvironment status and predict prognosis of LGGs.MethodsThe target gene tenascin-C (TNC) was obtained by screening the Chinese Glioma Genome Atlas (CGGA) and the Cancer Genome Atlas (TCGA) databases. Then samples of LGGs were collected for experimental verification with immunohistochemistry, immunofluorescence, immunoblotting, quantitative real-time PCR. ELISA was employed to determine the content of TNC in serum and examine its relationship with the tumor immune microenvironment. Eventually, the sensitivity of immunotherapy was predicted on the basis of the content of TNC in LGGs.ResultsIn the high-TNC subgroup, the infiltration of immunosuppressive cells was increased (MDSC: r=0.4721, Treg: r=0.3154, etc.), and immune effector cells were decreased [NKT, γδT, etc. (p<0.05)], immunosuppressive factors were elevated [TGF-β, IL10, etc. (p<0.05)], immunostimulatory factors, such as NKG2D, dropped (p<0.05), hypoxia scores increased (p<0.001), and less benefit from immunotherapy (p<0.05). Serum TNC level could be used to assess the status of tumor immune microenvironment in patients with grade II (AUC=0.8571; 95% CI: 0.6541-1.06) and grade III (AUC=0.8333; 95% CI: 0.6334-1.033) glioma.ConclusionsOur data suggested that TNC could serve as an indicator for the immunosuppressive microenvironment status and the prognosis of LGGs. Moreover, it could also act as a predictor for the effect of immunotherapy on LGG patients.

The oligodendroglial histological features are not independently predictive of patient prognosis in lower-grade gliomas.

The relevance of oligodendroglial histological features to patient prognoses is controversial. 93 LrGGs resected for about 2 decades were re-assessed based on WHO2007 with special interest to pure oligodendroglial diagnosis (oligodendroglioma or anaplastic oligodendroglioma) and presence of CFO features. Those histological features, patients OS, and tumor chromosomal/genetic characteristics were correlated each other in each of the 3 IDH-1p/19q-based molecular groups. There was significant association between 1p19q status with the oligodendroglial histological diagnosis as well as presence of CFO in the entire cohort. The oligodendroglial diagnosis was associated with longer OS in IDHmut/codel group; however, this association was not significant in the multivariate analyses. In IDHmut/noncodel and IDH-wildtype groups, the oligodendroglial diagnosis was not associated with patient OS. Presence of CFO was not associated with patient OS in any molecular groups. Gain of 8q was associated with the oligodendroglial diagnosis in IDHmut/noncodel group. Neither the oligodendroglial diagnosis nor CFO was predictive for the methylation status of the MGMT gene in any molecular groups. The oligodendroglial histological features are not independently predictive of either patient prognosis or chemotherapeutic response in LrGGs, leaving the possibility of marginal favorable association only in IDHmut/codel tumors.

TP53 R273C Mutation Is Associated With Poor Prognosis in LGG Patients.

Purpose: With the progress of cancer immunotherapy, hotspot mutations of common oncogenes and tumor suppressors are becoming new potential therapeutic targets. TP53 R273C mutation is one of the hotspot mutations of TP53, and it has a higher frequency in low-grade glioma (LGG). However, the function of this mutation and its prognostic significance in LGG are not still clear. Methods: To address this question, RNA sequencing, clinical, and SNP data of LGG patients from the TCGA database were downloaded. The Kaplan–Meier (KM) method was used for survival analysis. Immune cell populations in this cohort were assessed via the MCP counter and CIBERSORT. DNA damage/repair scores were calculated by GSVA analysis. WGCNA was conducted to identify genes related to TMB. Results: In the context of IDH1/2 mutation, LGG patients with TP53 R273C mutation had worse prognosis than other mutation types and wild types. This conclusion is still valid in LGG patients who had received chemotherapy or radiotherapy. Considering the 1p19q codeletion status, it was found that patients with both R273C mutation and 1p19q non-codeletion had the worst prognosis. Further analysis showed that LGG patients with TP53 R273C mutation had higher M2 macrophage infiltration and tumor mutation burden (TMB) than that of TP53 wild-type LGG patients, and higher TMB indicates poor prognosis in LGG patients. Furthermore, we identified genes which could be associated with higher M2 macrophage infiltration and TMB in LGG patients with TP53 R273C mutation. Conclusion: The study indicates that TP53 R273C mutation is very likely oncogenic and may be used as an indicator of the prognosis of LGG.

Establishment and Validation of a 5 m6A RNA Methylation Regulatory Gene Prognostic Model in Low-Grade Glioma.

Background: The prognosis of low-grade glioma (LGG) is different from that of other intracranial tumors. Although many markers of LGG have been established, few are used in clinical practice. M6A methylation significantly affects the biological behavior of LGG tumors. Therefore, establishment of an LGG prognostic model based on m6A methylation regulatory genes is of great interest. Methods: Data from 495 patients from The Cancer Genome Atlas (TCGA) and 172 patients from the Chinese Glioma Genome Atlas (CGGA) were analyzed. Univariate Cox analysis was used to identify methylation regulatory genes with prognostic significance. LASSO Cox regression was used to identify prognostic genes. Receiver operating characteristic (ROC) and Kaplan–Meier curves were used to verify the accuracy of the model. Gene Set Enrichment Analysis (GSEA) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify cellular pathways that were significantly associated with the prognosis of LGG. Results: A glioma prognostic model based on five methylation regulatory genes was established. Compared with low-risk patients, patients identified as high risk had a poorer prognosis. There was a high degree of consistency between the internal training and internal validation CGGA cohorts and the external validation TCGA cohort. Furthermore, KEGG and GSEA analyses showed that the focal adhesion and cell cycle pathways were significantly upregulated in high-risk patients. This signature could be used to distinguish among patients with different immune checkpoint gene expression levels, which may inform immune checkpoint inhibitor (ICI) immunotherapy. Conclusion: We comprehensively evaluated m6A methylation regulatory genes in LGG and constructed a prognostic model based on m6A methylation, which may improve prognostic prediction for patients with LGG.

RFC2: a prognosis biomarker correlated with the immune signature in diffuse lower-grade gliomas

Diffuse lower-grade gliomas (LGG) represent the highly heterogeneous and infiltrative neoplasms in the central nervous system (CNS). Replication factor C 2 (RFC2) is a subunit of the RFC complex that modulates DNA replication and repair. However, the prognosis value of RFC2 and its association with the immune signature of tumor microenvironment (TME) in LGG remains unknown. Based on Oncomine, TCGA, GTEx, TIMER, GEPIA, and HPA databases, we evaluated RFC2 expression levels and its clinical prognostic value in LGG and other cancers. Then we analyzed the correlations between RFC2 expression and tumor mutation burden (TMB), tumor microsatellite instability (MSI), and mismatch repair (MMR) genes across cancers. And CIBERSORT and ESTIMATE algorithms were conducted to estimate the association of RFC2 with immune cell infiltration of LGG. Additionally, we performed the functional enrichment analyses of RFC2 in LGG. Then functional experiments were employed to further validate the oncogenic role of RFC2 in LGG. Our results showed that RFC2 was widely highly expressed in most types of cancer. And its expression was closely related to the clinicopathological features and prognosis in LGG and other cancer types. RFC2 levels were also correlated with TMB and MSI across various cancers. Furthermore, RFC2 was positively associated with the infiltration levels of immune cells and immune checkpoint genes in LGG. Additionally, in vitro experiments revealed that RFC2 played an oncogenic role in LGG progression. In conclusion, our findings revealed that RFC2 could serve as a reliable biomarker to predict the prognosis and immune signature for LGG.

Phospholipase Cγ1 (PLCG1) overexpression is associated with tumor growth and poor survival in IDH wild-type lower-grade gliomas in adult patients

AbstractGliomas are the most common and recalcitrant intracranial tumors, approximately a quarter of which are classified as lower-grade gliomas (WHO II–III). Although the prognosis of lower-grade gliomas (LGGs) is significantly better than that of higher-grade gliomas, as a highly heterogeneous tumor type, the prognosis of LGGs varies greatly based on the molecular diagnosis. IDH wild-type used to be regarded as a dismal prognostic biomarker in LGGs; however, several studies revealed that IDH wild-type LGGs might not always be equivalent to glioblastoma (WHO IV). Hence, we hypothesize that underlying biological events in LGGs can result in different prognosis. In our study, transcriptome profiling was performed in 24 samples of LGG, and the results showed that the expression of phospholipase Cγ1 (PLCG1) was significantly correlated with IDH1/2 status and patients' clinical outcome. Furthermore, the cancer genome atlas (TCGA) and the Chinese glioma genome atlas (CGGA) databases verified that elevated PLCG1 expression was associated with tumor progression and poor survival in LGG patients. Moreover, PLCG1-targeted siRNA dramatically affected the growth, migration and invasiveness of IDH wild-type LGG cell lines. In in vitro and in vivo experiments, the PLC-targeted drug significantly suppressed the tumor growth of IDH wild-type LGG cell lines in vitro and tumors in mouse models. Taken together, our results demonstrated that higher PLCG1 expression was associated with tumor growth and worse prognosis in IDH wild-type LGGs and PLCG1 could serve as a potential therapeutic target for IDH wild-type LGG patients.The mRNA expression levels of phospholipase Cγ1 (PLCG1) are much higher in IDH wild-type (IDHwt) lower-grade gliomas (LGGs) than in that of IDH mutant (IDHmut) LGGs. Higher PLCG1expression in IDHwt LGGs indicates poor clinical outcome. PLCG1 amplification may act as the key mechanism of PLCG1 upregulation. Depletion of PLCG1 expression can inhibits proliferation and invasion of IDHwt LGG cell lines in vitro and in vivo. The PLC inhibitor U73122 may therefore be a potential therapeutical agent for IDHwt LGGs.

CCNB2 is a novel prognostic factor and a potential therapeutic target in low-grade glioma.

Background: Cyclin B2 (CCNB2) is an important component of the cyclin pathway and plays a key role in the occurrence and development of cancer. However, the correlation between prognosis of low-grade glioma (LGG), CCNB2, and tumor infiltrating lymphocytes is not clear. Methods: The expression of CCNB2 in LGG was queried in Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and TIMER databases. The relationships between CCNB2 and the clinicopathological features of LGG were analyzed using the Chinese Glioma Genome Atlas (CGGA) database. The relationship between CCNB2 expression and overall survival (OS) was evaluated by GEPIA2. The correlation between CCNB2 and LGG immune infiltration was analyzed by the TIMER database. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to detect CCNB2 expression. Results: The expression of CCNB2 differed across different tumor tissues, but was higher in LGG than in normal tissues. LGG patients with high expression of CCNB2 have poorer prognosis. The expression of CCNB2 was correlated with age, WHO grade, IDH mutational status, 1p/19q codeletion status, and other clinicopathological features. The expression of CCNB2 in LGG was positively correlated with the infiltration level of B cells, dendritic cells, and macrophages. qRT-PCR results revealed that the expression of CCNB2 in LGG tissues was higher than normal tissues and higher expression of CCNB2 was associated with worse prognosis. Conclusion: CCNB2 may be used as a potential biomarker to determine the prognosis of LGG and is also related to immune infiltration.

Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma.

BackgroundIsocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors.MethodsA total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built.ResultsThe 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively.ConclusionThe combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

DNA Methylation-Driven Genes for Developing Survival Nomogram for Low-Grade Glioma.

Low-grade gliomas (LGG) are heterogeneous, and the current predictive models for LGG are either unsatisfactory or not user-friendly. The objective of this study was to establish a nomogram based on methylation-driven genes, combined with clinicopathological parameters for predicting prognosis in LGG. Differential expression, methylation correlation, and survival analysis were performed in 516 LGG patients using RNA and methylation sequencing data, with accompanying clinicopathological parameters from The Cancer Genome Atlas. LASSO regression was further applied to select optimal prognosis-related genes. The final prognostic nomogram was implemented together with prognostic clinicopathological parameters. The predictive efficiency of the nomogram was internally validated in training and testing groups, and externally validated in the Chinese Glioma Genome Atlas database. Three DNA methylation-driven genes, ARL9, CMYA5, and STEAP3, were identified as independent prognostic factors. Together with IDH1 mutation status, age, and sex, the final prognostic nomogram achieved the highest AUC value of 0.930, and demonstrated stable consistency in both internal and external validations. The prognostic nomogram could predict personal survival probabilities for patients with LGG, and serve as a user-friendly tool for prognostic evaluation, optimizing therapeutic regimes, and managing LGG patients.

Genome‐wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.