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Large-Scale Analysis Reveals Gene Signature for Survival Prediction in Primary Glioblastoma.
Molecular Neurobiology ( IF 5.1 ) Pub Date : 2020-09-01 , DOI: 10.1007/s12035-020-02088-w
Birbal Prasad 1 , Yongji Tian 2 , Xinzhong Li 1
Affiliation  

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.



中文翻译:

大规模分析揭示了原发性胶质母细胞瘤生存预测的基因特征。

多形胶质母细胞瘤(GBM)是最活跃,最常见的原发性中枢神经系统肿瘤。尽管进行了广泛的治疗,GBM患者通常预后较差,中位生存期为12-15个月。仍然迫切需要能够改善生存预测并帮助治疗策略的新型分子生物标记。在这里,我们旨在稳健地确定基因签名小组,以改善原发性GBM患者的生存预测。我们通过对包括955个样本(据我们所知的此类研究的最大的主要GBM队列)的微阵列数据集进行荟萃分析,鉴定了2166个差异表达基因(DEG),并从具有165个样本的RNA-seq数据集中提取了3368个DEG。基于1443个常见DEG,使用单变量Cox和具有多元Cox回归的最小绝对收缩和选择算子(LASSO),IGFBP2PTPRNSTEAP2SLC39A10然后建立了在生存预测中表现良好的风险评分模型。与低风险组相比,高风险组患者的生存期明显较差(1年预测的AUC = 0.766)。多变量分析表明4-基因签名小组的预测价值与其他临床和病理特征无关,因此是潜在的预后生物标志物。更重要的是,我们在三个独立的GBM队列中验证了此签名,以测试其通用性。总之,我们使用荟萃分析的综合分析可最大限度地利用可用的基因表达数据,并可靠地鉴定出4个基因的面板来预测原发性GBM的存活率。

更新日期:2020-10-07
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