Nemours Biomedical Research, Nemours - Alfred I. duPont Hospital for Children, 1600 Rockland Rd, Wilmington, DE, 19803, USA.
Genome Profiling LLC, 4701 Ogletown Stanton Rd #4300, Newark, DE, 19713, USA.
BMC Bioinformatics. 2018 Jun 21;19(1):225. doi: 10.1186/s12859-018-2224-0.
Spastic cerebral palsy (CP) is a leading cause of physical disability. Most people with spastic CP are born with it, but early diagnosis is challenging, and no current biomarker platform readily identifies affected individuals. The aim of this study was to evaluate epigenetic profiles as biomarkers for spastic CP. A novel analysis pipeline was employed to assess DNA methylation patterns between peripheral blood cells of adolescent subjects (14.9 ± 0.3 years old) with spastic CP and controls at single CpG site resolution.
Significantly hypo- and hyper-methylated CpG sites associated with spastic CP were identified. Nonmetric multidimensional scaling fully discriminated the CP group from the controls. Machine learning based classification modeling indicated a high potential for a diagnostic model, and 252 sets of 40 or fewer CpG sites achieved near-perfect accuracy within our adolescent cohorts. A pilot test on significantly younger subjects (4.0 ± 1.5 years old) identified subjects with 73% accuracy.
Adolescent patients with spastic CP can be distinguished from a non-CP cohort based on DNA methylation patterns in peripheral blood cells. A clinical diagnostic test utilizing a panel of CpG sites may be possible using a simulated classification model. A pilot validation test on patients that were more than 10 years younger than the main adolescent cohorts indicated that distinguishing methylation patterns are present earlier in life. This study is the first to report an epigenetic assay capable of distinguishing a CP cohort.
痉挛性脑瘫（CP）是导致身体残疾的主要原因。大多数痉挛性 CP 患者都是天生的，但早期诊断具有挑战性，目前也没有任何生物标志物平台能够轻易识别出受影响的个体。本研究旨在评估表观遗传谱作为痉挛性 CP 的生物标志物。采用一种新的分析管道，以评估青少年患者（14.9±0.3 岁）外周血细胞的 DNA 甲基化模式，这些患者患有痉挛性 CP，而对照组在单个 CpG 位点分辨率下。
确定了与痉挛性 CP 相关的低甲基化和高甲基化 CpG 位点。非度量多维缩放法完全区分了 CP 组和对照组。基于机器学习的分类模型表明，该诊断模型具有很高的潜力，在我们的青少年队列中，252 个由 40 个或更少 CpG 位点组成的集合达到了近乎完美的准确率。对年龄更小的（4.0±1.5 岁）患者进行的初步测试，识别出了具有 73%准确率的患者。
基于外周血单个核细胞的 DNA 甲基化模式，青少年痉挛性 CP 患者可与非 CP 队列区分开来。使用模拟分类模型，可能可以利用一组 CpG 位点进行临床诊断测试。对比青少年队列年轻 10 岁以上的患者进行的初步验证测试表明，在生命早期就存在可区分的甲基化模式。这项研究首次报道了一种能够区分 CP 队列的表观遗传检测。
