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Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma.

ABSTRACT: Glioblastoma multiforme (GBM) is the most aggressive and common primary central nervous system tumour. Despite extensive therapy, GBM patients usually have poor prognosis with a median survival of 12-15 months. Novel molecular biomarkers that can improve survival prediction and help with treatment strategies are still urgently required. Here we aimed to robustly identify a gene signature panel for improved survival prediction in primary GBM patients. We identified 2166 differentially expressed genes (DEGs) using meta-analysis of microarray datasets comprising of 955 samples (biggest primary GBM cohort for such studies as per our knowledge) and 3368 DEGs from RNA-seq dataset with 165 samples. Based on the 1443 common DEGs, using univariate Cox and least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, we identified a survival associated 4-gene signature panel including IGFBP2, PTPRN, STEAP2 and SLC39A10 and thereafter established a risk score model that performed well in survival prediction. High-risk group patients had significantly poorer survival as compared with those in the low-risk group (AUC = 0.766 for 1-year prediction). Multivariate analysis demonstrated that predictive value of the 4-gene signature panel was independent of other clinical and pathological features and hence is a potential prognostic biomarker. More importantly, we validated this signature in three independent GBM cohorts to test its generality. In conclusion, our integrated analysis using meta-analysis approach maximizes the use of the available gene expression data and robustly identified a 4-gene panel for predicting survival in primary GBM.

SUBMITTER: Prasad B

PROVIDER: S-EPMC7541357 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma.

Prasad Birbal B Tian Yongji Y Li Xinzhong X

Molecular neurobiology 20200901 12

Glioblastoma multiforme (GBM) is the most aggressive and common primary central nervous system tumour. Despite extensive therapy, GBM patients usually have poor prognosis with a median survival of 12-15 months. Novel molecular biomarkers that can improve survival prediction and help with treatment strategies are still urgently required. Here we aimed to robustly identify a gene signature panel for improved survival prediction in primary GBM patients. We identified 2166 differentially expressed g ...[more]

PMID: 32875483

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Project description:BackgroundWith the improvement of clinical treatment outcomes in diffuse large B cell lymphoma (DLBCL), the high rate of relapse in DLBCL patients is still an established barrier, as the therapeutic strategy selection based on potential targets remains unsatisfactory. Therefore, there is an urgent need in further exploration of prognostic biomarkers so as to improve the prognosis of DLBCL.MethodsThe univariable and multivariable Cox regression models were employed to screen out gene signatures for DLBCL overall survival (OS) prediction. The differential expression analysis was used to identify representative genes in high-risk and low-risk groups, respectively, where student t test and fold change were implemented. The functional difference between the high-risk and low-risk groups was identified by the gene set enrichment analysis.ResultsWe conducted a systematic data analysis to screen the candidate genes significantly associated with OS of DLBCL in three NCBI Gene Expression Omnibus (GEO) datasets. To construct a prognostic model, five genes (CEBPA, CYP27A1, LST1, MREG, and TARP) were then screened and tested using the multivariable Cox model and the stepwise regression method. Kaplan-Meier curve confirmed the good predictive performance of this five-gene Cox model. Thereafter, the prognostic model and the expression levels of the five genes were validated by means of an independent dataset. High expression levels of these five genes were significantly associated with favorable prognosis in DLBCL, both in training and validation datasets. Additionally, further analysis revealed the independent value and superiority of this prognostic model in risk prediction. Functional enrichment analysis revealed some vital pathways responsible for unfavorable outcome and potential therapeutic targets in DLBCL.ConclusionWe developed a five-gene Cox model for the clinical outcome prediction of DLBCL patients. Meanwhile, potential drug selection using this model can help clinicians to improve the clinical practice for the benefit of patients.

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.

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Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma.

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Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma.

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