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Development and validation of GMI signature based random survival forest prognosis model to predict clinical outcome in acute myeloid leukemia

机译:基于GMI签名的随机生存森林预后模型的开发和验证,以预测急性髓性白血病的临床结局

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Acute myeloid leukemia (AML) is a disease with marked molecular heterogeneity and a high early death rate. Our aim was to investigate an integrated Gene expression, Mirna and miRNA-mRNA Interactions (GMI) signature for improving risk stratification of AML. We identified differentially expressed genes by pooling a large number of 861 human AML patients and 75 normal cases. We then used miRWalk to identify the functional miRNA-mRNA regulatory module. The GMI signature based random survival forest (RSF) prognosis model was developed from training data set and evaluated in independent patient cohorts from The Cancer Genome Atlas (TCGA) dataset (N?=?147). Univariate and multivariate Cox proportional hazards regression analyses were applied to evaluate the prognostic value of GMI signature. We identified 139 differentially expressed genes between normal and abnormal AML samples. We discovered the functional miRNA-mRNA regulatory module which participate in the network of cancer progression. We named 23 differentially expressed genes and 16 validated target miRNAs as the GMI signature. The RSF model-based scores separated independent patient cohorts into two groups with significantly different overall survival (C-index?=?0.59, hazard ratio [HR], 2.12; 95% confidence interval [CI], 1.11–4.03; p?=?0.019). Similar results were obtained with reversed training and testing datasets (C-index?=?0.58, hazard ratio [HR], 2.08; 95% confidence interval [CI], 1.02–4.24; p?=?0.038). The GMI signature score contributed more information about recurrence than standard clinical covariates. The GMI signature based RSF prognosis model not only reflects regulatory relationships from identified miRNA-mRNA module but also informs patient prognosis. While in the TCGA data set the GMI signature score contributed additional information about recurrence in comparison to standard clinical covariates, further studies are needed to determine its clinical significance.
机译:急性髓细胞性白血病(AML)是一种具有明显的分子异质性和较高的早期死亡率的疾病。我们的目的是研究整合的基因表达,Mirna和miRNA-mRNA相互作用(GMI)签名,以改善AML的风险分层。我们通过汇集861例人类AML患者和75例正常病例来鉴定差异表达的基因。然后,我们使用miRWalk来识别功能性miRNA-mRNA调节模块。基于GMI签名的随机生存森林(RSF)预后模型是根据训练数据集开发的,并在来自癌症基因组图谱(TCGA)数据集的独立患者队列中进行评估(N≥147)。单因素和多因素Cox比例风险回归分析用于评估GMI信号的预后价值。我们鉴定了正常和异常AML样本之间的139个差异表达基因。我们发现了功能性miRNA-mRNA调节模块,该模块参与了癌症进展网络。我们将23个差异表达的基因和16个经过验证的靶标miRNA命名为GMI签名。基于RSF模型的评分将独立的患者队列分为两组,这些两组的总生存期显着不同(C指数= 0.59,危险比[HR]为2.12; 95%置信区间[CI]为1.11-4.03; p = 0.019)。反向训练和测试数据集也获得了类似的结果(C指数≥0.58,危险比[HR]为2.08; 95%置信区间[CI]为1.02-4.24; P = 0.038)。与标准临床协变量相比,GMI签名得分贡献了更多有关复发的信息。基于GMI签名的RSF预后模型不仅反映了已鉴定的miRNA-mRNA模块的调控关系,而且还告知了患者的预后。尽管在TCGA数据集中,与标准临床协变量相比,GMI签名评分为复发提供了更多信息,但仍需进一步研究以确定其临床意义。

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