Yang Cing-Han, Huang Jhen-Li, Tsai Li-Kai, Taniar David, Pai Tun-Wen
Department of Computer Science and Engineering, National Taiwan Ocean University, Keelung City 202301, Taiwan.
Department of Neurology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei City 100229, Taiwan.
Bioengineering (Basel). 2024 Oct 12;11(10):1020. doi: 10.3390/bioengineering11101020.
This study used epigenomic methylation differential expression analysis to identify primary biomarkers in patients with amyotrophic lateral sclerosis (ALS). We combined electronic medical record datasets from MIMIC-IV (United States) and NHIRD (Taiwan) to explore ALS comorbidities in depth and discover any comorbidity-related biomarkers. We also applied word2vec to these two clinical diagnostic medical databases to measure similarities between ALS and other similar diseases and evaluated the statistical assessment of the odds ratio to discover significant comorbidities for ALS subjects. Important and representative DNA methylation biomarker candidates could be effectively selected by cross-comparing similar diseases to ALS, comorbidity-related genes, and differentially expressed methylation loci for ALS subjects. The screened epigenomic and comorbidity-related biomarkers were clustered based on their genetic functions. The candidate DNA methylation biomarkers associated with ALS were comprehensively discovered. Gene ontology annotations were then applied to analyze and cluster the candidate biomarkers into three different groups based on gene function annotations. The results showed that a potential testing kit for ALS detection can be composed of , , and for effective early screening of ALS using blood samples. By developing an effective DNA methylation biomarker screening mechanism, early detection and prophylactic treatment of high-risk ALS patients can be achieved.
本研究采用表观基因组甲基化差异表达分析来识别肌萎缩侧索硬化症（ALS）患者的主要生物标志物。我们结合了来自MIMIC-IV（美国）和NHIRD（中国台湾）的电子病历数据集，以深入探索ALS的合并症并发现任何与合并症相关的生物标志物。我们还将词向量模型（word2vec）应用于这两个临床诊断医学数据库，以测量ALS与其他相似疾病之间的相似性，并评估优势比的统计评估，以发现ALS患者的显著合并症。通过将与ALS相似的疾病、与合并症相关的基因以及ALS患者差异表达的甲基化位点进行交叉比较，可以有效地选择重要且有代表性的DNA甲基化生物标志物候选物。根据其基因功能对筛选出的表观基因组和与合并症相关的生物标志物进行聚类。全面发现了与ALS相关的候选DNA甲基化生物标志物。然后应用基因本体注释，根据基因功能注释将候选生物标志物分析并聚类为三个不同的组。结果表明，一种潜在的ALS检测试剂盒可以由 、 和 组成，用于使用血液样本对ALS进行有效的早期筛查。通过建立有效的DNA甲基化生物标志物筛选机制，可以实现对高危ALS患者的早期检测和预防性治疗。
