Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis.
Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis.
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DOI：
10.1016/j.cellimm.2020.104219
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发表时间：
2020-12
影响因子：
4.3
通讯作者：
Forsthuber TG
中科院分区：
文献类型：
--
作者：
Chase Huizar C;Raphael I;Forsthuber TG
多发性硬化（MS）是一种以自身免疫介导的CNS炎症性病变为特征的神经炎性疾病，其导致髓鞘损伤和轴突缺失。MS是一种异质性疾病，具有可变和不可预测的病程。由于其复杂的性质，MS很难诊断，对特定治疗的反应可能因个体而异。因此，对于用于早期诊断、预测疾病恶化、监测疾病进展和用于测量对治疗的反应的生物标志物存在无可争议的需求。基因组学和蛋白质组学研究试图了解MS的分子基础并找到生物标志物候选者。下一代测序和质谱技术的进步已经产生了前所未有的基因组和蛋白质组数据量;然而，将结果转化为临床的效果并不明显。这促使了针对这些大型数据集开发新的数据科学技术，以识别生物相关关系，并最终指向有用的生物标志物。在此，我们讨论组学研究设计的优化，组学数据生成的进展，以及旨在改善MS生物标志物发现和临床转化的系统生物学方法。
Multiple sclerosis (MS) is a neuroinflammatory disorder characterized by autoimmune-mediated inflammatory lesions in CNS leading to myelin damage and axonal loss. MS is a heterogenous disease with variable and unpredictable disease course. Due to its complex nature, MS is difficult to diagnose and responses to specific treatments may vary between individuals. Therefore, there is an indisputable need for biomarkers for early diagnosis, prediction of disease exacerbations, monitoring the progression of disease and for measuring responses to therapy. Genomic and proteomic studies have sought to understand the molecular basis of MS and find biomarker candidates. Advances in next-generation sequencing and mass-spectrometry techniques have yielded an unprecedented amount of genomic and proteomic data; yet, translation of the results into the clinic has been underwhelming. This has prompted the development of novel data science techniques for these large datasets to identify biologically relevant relationships and ultimately point towards useful biomarkers. Herein we discuss optimization of omics study designs, advances in the generation of omics data, and systems biology approaches aimed at improving biomarker discovery and translation to the clinic for MS.
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影响因子：
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作者：
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DOI：
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