本研究旨在通过元分析与深度学习方法相结合，鉴定血液中可靠的精神分裂症差异表达miRNA(DEMs)。首先，我们对已发表的DEMs进行了元分析。然后，我们使用两种计算学习方法丰富了精神分裂症相关miRNA的候选生物标志物，并在外部数据集中验证了结果。总计，找到27个具有统计学显著性(p < 0.05)的DEMs。通过计算学习方法，我们找到了10个候选的精神分裂症相关miRNA。血液-miRNA数据集(GSE54578)上，我们利用随机森林(RF)模型验证了诊断效能，并取得了0.83 ± 0.14的曲线下面积(AUC)。此外，我们检索了这些候选miRNA的855个实验证实的靶基因，并且鉴定出了11个中心基因。富集分析揭示了这些靶基因富集于与细胞信号传导、产前感染、癌症、细胞死亡、氧化应激、内分泌紊乱、转录调控以及激酶活性相关的主要功能。中心基因的诊断能力在外部数据集(GSE38484)中通过平均AUC值为0.77 ± 0.09体现出来。将计算和数学方法相结合的元分析为鉴定精神分裂症候选生物标志物提供了可靠的工具。

This study aimed at identifying reliable differentially expressed miRNAs (DEMs) for schizophrenia in blood via meta-analyses combined with deep learning methods.First, we meta-analysed published DEMs. Then, we enriched the pool of schizophrenia-associated miRNAs by applying two computational learning methods to identify candidate biomarkers and verified the results in external datasets.In total, 27 DEMs were found to be statistically significant (p < 0.05). Ten candidate schizophrenia-associated miRNAs were identified through computational learning methods. The diagnostic efficiency was verified on a blood-miRNA dataset (GSE54578) with a random forest (RF) model and achieved an area under the curve (AUC) of 0.83 ± 0.14. Moreover, 855 experimentally validated target genes for these candidate miRNAs were retrieved, and 11 hub genes were identified. Enrichment analysis revealed that the main functions in which the target genes were enriched were those related to cell signalling, prenatal infections, cancers, cell deaths, oxidative stress, endocrine disorders, transcription regulation, and kinase activities. The diagnostic ability of the hub genes was reflected in a comparably good average AUC of 0.77 ± 0.09 for an external dataset (GSE38484).A meta-analysis that combines computational and mathematical methods provides a reliable tool for identifying candidate biomarkers of schizophrenia.