Dataset Information

A Risk Classification System With Five-Gene for Survival Prediction of Glioblastoma Patients.

ABSTRACT: Objective: Glioblastoma (GBM) is the most common and fatal primary brain tumor in adults. It is necessary to identify novel and effective biomarkers or risk signatures for GBM patients. Methods: Differentially expressed genes (DEGs) between GBM and low-grade glioma (LGG) in TCGA samples were screened out and weight correlation network analysis (WGCNA) was performed to confirm WHO grade-related genes. Five genes were selected via multivariate Cox proportional hazards regression analysis and were used to construct a risk signature. A nomogram composed of the risk signature and clinical characters (age, radiotherapy, and chemotherapy experience) was established to predict 1, 3, 5-year survival rate for GBM patients. Results: One hundred ninety-four DEGs in blue gene module were found to be positively related to WHO grade via WGCNA. Five genes (DES, RANBP17, CLEC5A, HOXC11, POSTN) were selected to construct a risk signature for GBM via R language. This risk signature was identified to independently predict the outcome of GBM patients, as well as stratified by IDH1 status, MGMT promoter status, and radio-chemotherapy. The nomogram was established which combined the risk signature with clinical factors. The results of c-index, ROC curve and calibration plot revealed the nomogram showing a good accuracy for predicting 1, 3, or 5-year survival of GBM patients. Conclusion: The risk signature with five genes could serve as an independent factor for predicting the prognosis of patients with GBM. Moreover, the nomogram with the risk signature and clinical traits proved to perform better for predicting 1, 3, 5-year survival rate.

SUBMITTER: Wang Y

PROVIDER: S-EPMC6646669 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Risk Classification System With Five-Gene for Survival Prediction of Glioblastoma Patients.

Wang Yulin Y Liu Xin X Guan Gefei G Zhao Weijiang W Zhuang Minghua M

Frontiers in neurology 20190716

<b>Objective:</b> Glioblastoma (GBM) is the most common and fatal primary brain tumor in adults. It is necessary to identify novel and effective biomarkers or risk signatures for GBM patients. <b>Methods:</b> Differentially expressed genes (DEGs) between GBM and low-grade glioma (LGG) in TCGA samples were screened out and weight correlation network analysis (WGCNA) was performed to confirm WHO grade-related genes. Five genes were selected via multivariate Cox proportional hazards regression anal ...[more]

PMID: 31379707

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Project description:IntroductionGlioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients.MethodsIn this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM.ResultsDepending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups.ConclusionThe 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

Project description:BackgroundBrain glioblastoma multiforme (GBM) is the most common primary malignant intracranial tumor. The prognosis of this disease is extremely poor. While the introduction of β-interferon (IFN-β) regimen in the treatment of gliomas has significantly improved the outcome of patients; The mechanism by which IFN-β induces increased TMZ sensitivity has not been described. Therefore, the main objective of the study was to elucidate the molecular mechanisms responsible for the beneficial effect of IFNβ in GBM.MethodsMessenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) GBM and GSE83300 dataset from the Gene Expression Omnibus. Univariate Cox regression analysis and lasso Cox regression model established a novel 4-gene IFN-β signature (peroxiredoxin 1, Sec61 subunit beta, X-ray repair cross-complementing 5, and Bcl-2-like protein 2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction experiments. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the long non-coding RNAs (lncRNAs) and IFN-β-associated genes. An lncRNA with a correlation coefficient |R2|>0.3 and P<0.05 was considered to be an IFN-β-associated lncRNA.ResultsPatients in the high-risk group had significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust 4-gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction for GBM patients.ConclusionsIn the present study, we established a novel IFN-β-associated gene signature to predict the overall survival of GBM patients, which may help in clinical decision making for individual treatment.

Project description:PurposeConstruction of radiomics models for the individualized estimation of multiple survival stratification in glioblastoma (GBM) patients using the multiregional information extracted from multiparametric MRI that could facilitate clinical decision-making for GBM patients.Materials and methodsA total of 134 eligible GBM patients were selected from The Cancer Genome Atlas. These patients were separated into the long-term and short-term survival groups according to the median of individual survival indicators: overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Then, the patients were divided into a training set and a validation set in a ratio of 2:1. Radiomics features (n = 5,152) were extracted from multiple regions of the GBM using multiparametric MRI. Then, radiomics signatures that are related to the three survival indicators were respectively constructed using the analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) regression for each patient in the training set. Based on a Cox proportional hazards model, the radiomics model was further constructed by combining the signature and clinical risk factors.ResultsThe constructed radiomics model showed a promising discrimination ability to differentiate in the training set and validation set of GBM patients with survival indicators of OS, PFS, and DSS. Both the four MRI modalities and five tumor subregions have different effects on the three survival indicators of GBM. The favorable calibration and decision curve analysis indicated the clinical decision value of the radiomics model. The performance of models of the three survival indicators was different but excellent; the best model achieved C indexes of 0.725, 0.677, and 0.724, respectively, in the validation set.ConclusionOur results show that the proposed radiomics models have favorable predictive accuracy on three survival indicators and can provide individualized probabilities of survival stratification for GBM patients by using multiparametric and multiregional MRI features.

Dataset Information

A Risk Classification System With Five-Gene for Survival Prediction of Glioblastoma Patients.

Publications

A Risk Classification System With Five-Gene for Survival Prediction of Glioblastoma Patients.

Similar Datasets

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