Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gdańsk, Gen. J. Hallera, Gdańsk, Poland.
Department of Biology and Genetics, Medical University of Gdańsk, Dębinki, Gdańsk, Poland.
PLoS One. 2019 Aug 29;14(8):e0221764. doi: 10.1371/journal.pone.0221764. eCollection 2019.
In transcriptomics, micro RNAs (miRNAs) has gained much interest especially as potential disease indicators. However, apart from holding a great promise related to their clinical application, a lot of inconsistent results have been published. Our aim was to compare the miRNA expression levels in ovarian cancer and healthy subjects using the Bayesian multilevel model and to assess their potential usefulness in diagnosis. We have analyzed a case-control observational data on expression profiling of 49 preselected miRNA-based ovarian cancer indicators in 119 controls and 59 patients. A Bayesian multilevel model was used to characterize the effect of disease on miRNA levels controlling for differences in age and body weight. The difference between the miRNA level and health status of the patient on the scale of the data variability were discussed in the context of their potential usefulness in diagnosis. Additionally, the cross-validated area under the ROC curve (AUC) was used to assess the expected out-of-sample discrimination index of a different sets of miRNAs. The proposed model allowed us to describe the set of miRNA levels in patients and controls. Three highly correlated miRNAs: miR-101-3p, miR-142-5p, miR-148a-3p rank the highest with almost identical effect sizes that ranges from 0.45 to 1.0. For those miRNAs the credible interval for AUC ranged from 0.63 to 0.67 indicating their limited discrimination potential. A little benefit in adding information from other miRNAs was observed. There were several miRNAs in the dataset (miR-604, hsa-miR-221-5p) for which inferences were uncertain. For those miRNAs more experimental effort is needed to fully assess their effect in the context of new hits discovery and usefulness as disease indicators. The proposed multilevel Bayesian model can be used to characterize the panel of miRNA profile and to assess the difference in expression levels between healthy and cancer individuals.
在转录组学中，微小 RNA（miRNA）引起了广泛关注，尤其是作为潜在疾病标志物。然而，尽管它们在临床应用方面具有很大的应用前景，但也发表了很多不一致的结果。我们的目的是使用贝叶斯多层次模型比较卵巢癌患者和健康个体的 miRNA 表达水平，并评估它们在诊断中的潜在应用价值。我们分析了一项病例对照观察性数据，该数据涉及对 119 名对照和 59 名患者的 49 个预选 miRNA 标志物的表达谱进行分析。使用贝叶斯多层次模型来描述疾病对 miRNA 水平的影响，同时控制年龄和体重差异。在数据可变性的背景下，讨论了 miRNA 水平与患者健康状况之间的差异，以及它们在诊断中的潜在应用价值。此外，还使用交叉验证的 ROC 曲线下面积（AUC）来评估不同 miRNA 集的样本外判别指数的预期值。所提出的模型允许我们描述患者和对照人群中的 miRNA 水平集。三个高度相关的 miRNA：miR-101-3p、miR-142-5p 和 miR-148a-3p，其效应大小几乎相同，范围从 0.45 到 1.0。对于这些 miRNA，AUC 的置信区间范围从 0.63 到 0.67，表明其判别潜力有限。观察到添加其他 miRNA 信息会略有收益。数据集中有几个 miRNA（miR-604、hsa-miR-221-5p）的推断结果不确定。对于这些 miRNA，需要更多的实验努力来充分评估它们在新发现和作为疾病标志物的应用中的作用。所提出的多层次贝叶斯模型可用于描述 miRNA 谱的特征，并评估健康个体和癌症个体之间的表达水平差异。
