Dataset Information

Identification of an epigenetic prognostic signature for patients with lower-grade gliomas.

ABSTRACT:

Introduction

Glioma is the most common malignant primary brain tumor with survival outcome for patients with lower-grade gliomas (LGGs) being quite variable. Epigenetic modifications in LGGs appear tightly linked to patient clinical outcomes but are not commonly used as clinical tools.

Aims

We aimed to derive an epigenetic enzyme gene signature for LGGs that would allow for improved clinical risk stratification.

Results

The study employed transcriptomic data of 711 lower-grade gliomas from three publically available data sets. Based on least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we discovered a 13-gene epigenetic signature that strongly predicts poor overall survival in LGGs. The robust prediction ability for survival was further verified in two independent validation cohorts. The signature was also significantly associated with malignant molecular signatures including wild-type IDH, unmethylated MGMT promoter, and non-codeletion of 1p19q together with linkage to multiple oncogenic pathways. Interestingly, our results showed that immune infiltration of MDSCs together with mRNA expression of immune inhibition biomarkers was also positively correlated with the epigenetic signature. Lastly, we confirmed the oncogenic role of SMYD2 in glioma tumor cells in functional assays.

Conclusions

We report a novel epigenetic gene signature that harbors robust survival prediction value for LGG patients that is tightly linked to activation of multiple oncogenic pathways.

SUBMITTER: Yu H

PROVIDER: S-EPMC7941239 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Publications

Identification of an epigenetic prognostic signature for patients with lower-grade gliomas.

Yu Hai H Zhang Duanni D Lian Minxue M

CNS neuroscience & therapeutics 20210118 4

<h4>Introduction</h4>Glioma is the most common malignant primary brain tumor with survival outcome for patients with lower-grade gliomas (LGGs) being quite variable. Epigenetic modifications in LGGs appear tightly linked to patient clinical outcomes but are not commonly used as clinical tools.<h4>Aims</h4>We aimed to derive an epigenetic enzyme gene signature for LGGs that would allow for improved clinical risk stratification.<h4>Results</h4>The study employed transcriptomic data of 711 lower-gr ...[more]

PMID: 33459509

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Project description: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.

Project description:BACKGROUNDLower-grade gliomas (LGGs) vary widely in terms of the patient's overall survival (OS). There is no current, valid method that could exactly predict the survival. The effects of intratumoral immune infiltration on clinical outcome have been widely reported. Thus, we aim to develop an immune infiltration signature to predict the survival of LGG patients.METHODSWe analyzed 1216 LGGs from 5 public data sets, including 2 RNA sequencing data sets and 3 microarray data sets. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select an immune infiltration signature and build a risk score. The performance of the risk score was assessed in the training set (329 patients), internal validation set (140 patients), and 4 external validation sets (405, 118, 88, and 136 patients).RESULTSAn immune infiltration signature consisting of 20 immune metagenes was used to generate a risk score. The performance of the risk score was thoroughly verified in the training and validation sets. Additionally, we found that the risk score was positively correlated with the expression levels of TGF-β and PD-L1, which were important targets of combination immunotherapy. Furthermore, a nomogram incorporating the risk score, patient's age, and tumor grade was developed to predict the OS, and it performed well in all the training and validation sets (C-index: 0.873, 0.881, 0.781, 0.765, 0.721, and 0.753).CONCLUSIONThe risk score based on the immune infiltration signature has reliable prognostic and predictive value for patients with LGGs and is a potential biomarker for the cotargeting immunotherapy.FUNDINGThis work was supported by The National Natural Science Foundation of China (grant nos. 81472370 and 81672506), the Natural Science Foundation of Beijing (grant no. J180005), the National High Technology Research and Development Program of China (863 Program, grant no. 2014AA020610), and the National Basic Research Program of China (973 Program, grant no. 2014CB542006).

Project description:BackgroundCuproptosis is a newly discovered unique non-apoptotic programmed cell death distinguished from known death mechanisms like ferroptosis, pyroptosis, and necroptosis. However, the prognostic value of cuproptosis and the correlation between cuproptosis and the tumor microenvironment (TME) in lower-grade gliomas (LGGs) remain unknown.MethodsIn this study, we systematically investigated the genetic and transcriptional variation, prognostic value, and expression patterns of cuproptosis-related genes (CRGs). The CRG score was applied to quantify the cuproptosis subtypes. We then evaluated their values in the TME, prognostic prediction, and therapeutic responses in LGG. Lastly, we collected five paired LGG and matched normal adjacent tissue samples from Sun Yat-sen University Cancer Center (SYSUCC) to verify the expression of signature genes by quantitative real-time PCR (qRT-PCR) and Western blotting (WB).ResultsTwo distinct cuproptosis-related clusters were identified using consensus unsupervised clustering analysis. The correlation between multilayer CRG alterations with clinical characteristics, prognosis, and TME cell infiltration were observed. Then, a well-performed cuproptosis-related risk model (CRG score) was developed to predict LGG patients' prognosis, which was evaluated and validated in two external cohorts. We classified patients into high- and low-risk groups according to the CRG score and found that patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P<0.001). A high CRG score implies higher TME scores, more significant TME cell infiltration, and increased mutation burden. Meanwhile, the CRG score was significantly correlated with the cancer stem cell index, chemoradiotherapy sensitivity-related genes and immune checkpoint genes, and chemotherapeutic sensitivity, indicating the association with CRGs and treatment responses. Univariate and multivariate Cox regression analyses revealed that the CRG score was an independent prognostic predictor for LGG patients. Subsequently, a highly accurate predictive model was established for facilitating the clinical application of the CRG score, showing good predictive ability and calibration. Additionally, crucial CRGs were further validated by qRT-PCR and WB.ConclusionCollectively, we demonstrated a comprehensive overview of CRG profiles in LGG and established a novel risk model for LGG patients' therapy status and prognosis. Our findings highlight the potential clinical implications of CRGs, suggesting that cuproptosis may be the potential therapeutic target for patients with LGG.

Dataset Information

Identification of an epigenetic prognostic signature for patients with lower-grade gliomas.

Introduction

Aims

Results

Conclusions

Publications

Identification of an epigenetic prognostic signature for patients with lower-grade gliomas.

Similar Datasets

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