Guang He, Yi Shi, Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, 1954 Huashan Road, Shanghai, 200030, China;
J Prev Alzheimers Dis. 2024;11(4):1055-1062. doi: 10.14283/jpad.2024.52.
Alzheimer's disease (AD) is a neurodegenerative disease and there is by far no effective treatment for it, especially in its late stage. Circular RNAs (circRNAs), known as a class of non-coding RNAs are widely observed in eukaryotic transcriptomes, and are reported to play an important role in neurodegenerative diseases including AD. circRNAs usually act as microRNA (miRNA) inhibitors or «sponges» to regulate the function of miRNAs, leading to subsequent changes in protein activities and functions. Accumulating evidence indicates that circRNAs can serve as potential biomarker in AD early prediction. The functional roles of circRNAs are very versatile including miRNAs binding - thus affecting downstream gene expression, generating abnormally translated protein peptides, and affecting epigenetic modifications which subsequently affect AD related gene expressions. Therefore, identifying AD-related circRNAs can contribute to AD early diagnosis and intervention. In this work, we collected and curated an AD-related circRNA dataset; by exploring the circRNAs' corresponding DNA loci distribution in chromatin 3D conformation (3D genome) and utilize the such 3D genome information, we were able to selected a concise yet predictively effective circRNA panel, based on which, significantly better AD prediction machine learning models were achieved.
阿尔茨海默病（AD）是一种神经退行性疾病，目前尚无有效的治疗方法，尤其是在疾病晚期。环状 RNA（circRNA）作为一类非编码 RNA，广泛存在于真核转录组中，据报道在包括 AD 在内的神经退行性疾病中发挥重要作用。circRNA 通常作为 microRNA（miRNA）的抑制剂或“海绵”来调节 miRNA 的功能，导致随后蛋白质活性和功能的变化。越来越多的证据表明，circRNA 可以作为 AD 早期预测的潜在生物标志物。circRNA 的功能作用非常多样化，包括与 miRNAs 结合——从而影响下游基因表达，产生异常翻译的蛋白肽，以及影响表观遗传修饰，从而影响 AD 相关基因的表达。因此，鉴定与 AD 相关的 circRNA 有助于 AD 的早期诊断和干预。在这项工作中，我们收集和整理了一个与 AD 相关的 circRNA 数据集；通过探索 circRNA 在染色质 3D 构象（3D 基因组）中相应 DNA 位置的分布，并利用这种 3D 基因组信息，我们能够选择一个简洁但预测效果良好的 circRNA 面板，在此基础上，我们实现了性能显著提升的 AD 预测机器学习模型。
