Department of Molecular Pathology and Neuropathology, Medical University of Lodz, Pomorska 251, 92-216, Lodz, Poland.
Department of Biostatistics and Translational Medicine, Medical University of Lodz, Mazowiecka 15, 92-215, Lodz, Poland.
BMC Cancer. 2019 Jun 6;19(1):544. doi: 10.1186/s12885-019-5739-5.
The understanding of the molecular biology of pediatric neuronal and mixed neuronal-glial brain tumors is still insufficient due to low frequency and heterogeneity of those lesions which comprise several subtypes presenting neuronal and/or neuronal-glial differentiation. Important is that the most frequent ganglioglioma (GG) and dysembryoplastic neuroepithelial tumor (DNET) showed limited number of detectable molecular alterations. In such cases analyses of additional genomic mechanisms seem to be the most promising. The aim of the study was to evaluate microRNA (miRNA) profiles in GGs, DNETs and pilocytic asytrocytomas (PA) and test the hypothesis of plausible miRNA connection with histopathological subtypes of particular pediatric glial and mixed glioneronal tumors.
The study was designed as the two-stage analysis. Microarray testing was performed with the use of the miRCURY LNA microRNA Array technology in 51 cases. Validation set comprised 107 samples used during confirmation of the profiling results by qPCR bioinformatic analysis.
Microarray data was compared between the groups using an analysis of variance with the Benjamini-Hochberg procedure used to estimate false discovery rates. After filtration 782 miRNAs were eligible for further analysis. Based on the results of 10 × 10-fold cross-validation J48 algorithm was identified as the most resilient to overfitting. Pairwise comparison showed the DNETs to be the most divergent with the largest number of miRNAs differing from either of the two comparative groups. Validation of array analysis was performed for miRNAs used in the classification model: miR-155-5p, miR-4754, miR-4530, miR-628-3p, let-7b-3p, miR-4758-3p, miRPlus-A1086 and miR-891a-5p. Model developed on their expression measured by qPCR showed weighted AUC of 0.97 (95% CI for all classes ranging from 0.91 to 1.00). A computational analysis was used to identify mRNA targets for final set of selected miRNAs using miRWalk database. Among genomic targets of selected molecules ZBTB20, LCOR, PFKFB2, SYNJ2BP and TPD52 genes were noted.
Our data showed the existence of miRNAs which expression is specific for different histological types of tumors. miRNA expression analysis may be useful in in-depth molecular diagnostic process of the tumors and could elucidate their origins and molecular background.
由于儿科神经元和混合神经元-神经胶质脑肿瘤的发病率低且具有异质性，因此对其分子生物学的理解仍然不足。这些病变包括几种具有神经元和/或神经元-神经胶质分化的亚型，重要的是，最常见的神经节细胞瘤（GG）和发育不良性神经上皮肿瘤（DNET）显示出有限数量的可检测到的分子改变。在这种情况下，分析其他基因组机制似乎是最有前途的。本研究的目的是评估 GG、DNET 和毛细胞星形细胞瘤（PA）中的 microRNA（miRNA）谱，并检验 miRNA 与特定儿科神经胶质和混合神经胶质肿瘤的组织病理学亚型之间存在潜在联系的假设。
该研究设计为两阶段分析。使用 miRCURY LNA microRNA 阵列技术进行微阵列检测，共 51 例。验证集由 107 例样本组成，用于通过 qPCR 生物信息学分析确认分析结果。
使用方差分析对各组数据进行比较，采用 Benjamini-Hochberg 程序估计假发现率。经过过滤后，有 782 个 miRNA 适合进一步分析。基于 10×10 折交叉验证的结果，J48 算法被确定为最能抵抗过拟合的算法。两两比较显示 DNET 与另外两个比较组相比差异最大，具有最多差异的 miRNA。对分类模型中使用的 miRNA 进行微阵列分析的验证：miR-155-5p、miR-4754、miR-4530、miR-628-3p、let-7b-3p、miR-4758-3p、miRPlus-A1086 和 miR-891a-5p。通过 qPCR 测量其表达而开发的模型显示加权 AUC 为 0.97（所有类别 95%CI 范围为 0.91 至 1.00）。使用 miRWalk 数据库对最终选定 miRNA 集进行计算分析，以鉴定 mRNA 靶标。在所选择的分子 ZBTB20、LCOR、PFKFB2、SYNJ2BP 和 TPD52 基因中注意到基因组靶标。
我们的数据表明，不同组织学类型的肿瘤存在表达特异性的 miRNA。miRNA 表达分析可用于肿瘤的深入分子诊断过程，并阐明其起源和分子背景。
