Long non-coding RNA signatures to classify multiple sclerosis

复发缓解型多发性硬化症（MS）的诊断取决于临床症状和检查， 在修订后的麦当劳标准中概述了适当的磁共振成像结果支持， 其他实验室测试。2015年发表的立场文件明确强调了早期诊断的必要性 由MS脑健康组织称为“脑健康，时间在多发性硬化症中很重要”， 倡导MS研究的主要组织和基金会，提供者和患者，包括 加速治愈项目（ACP），美国致力于治疗和研究多发性硬化症 （ACTRIMS）、多发性硬化中心联盟（CMSC）、欧洲脑理事会（EBC）、欧洲脑神经科学协会（EBC）、 多发性硬化症治疗和研究委员会（ECTRIMS），欧洲多发性硬化症平台 （EMSP）、国际神经免疫学会（ISNI）、国际多发性硬化组织 护士（IOMSN），国家多发性硬化症协会（NMSS）和多发性硬化症信托基金（MS）。引自 他们的执行摘要页：（1）“一种治疗策略，提供了最好的机会，保留大脑 而脊髓组织在病程早期需要被广泛接受--并紧急采用。” (2)“在有MS症状的人去看神经科医生之前， 进行诊断和治疗。”(3)“早期干预至关重要。”(bold“这是他们的脸，不是我们的脸。 疾病分类器是否能够提供临床有用信息的问题可能是 基于全血中mRNA的疾病特异性表达水平建立的免疫调节系统已经成为研究的主题， 几年长链非编码RNA（lncRNA）是最近发现的调节RNA分子，其不 编码蛋白质，但影响了大量的生物过程。也有人认为lncRNA驱动生物 在脊椎动物中观察到的复杂性也可能反映在更大范围的复杂特发性疾病中 是人类发展的。因此，我们在这项工作的第一阶段获得的数据支持以下观点： 疾病相关lncRNA表现出比疾病相关mRNA大得多的表达差异。在 在本申请中，我们提出探索lncRNA是人类疾病更好的生物标志物的假设。 比mRNA。在这里，我们将把重点放在MS作为一种疾病类别，并已确定和验证MS相关 差异表达的lncRNA。人类自身免疫性疾病中lncRNA的研究尚处于起步阶段， lncRNA作为自身免疫性疾病的生物标志物的探索以前没有涉及。我们提出 为了确定从较大的受试者群体获得的血液中靶lncRNA的表达水平，所述受试者群体包括 1)RRMS受试者，2）健康对照，3）神经系统疾病对照，包括炎性和非炎性 炎性病症，和4）从美国的各个地点获得的外周自身免疫性疾病对照。 和欧洲建立广泛的地理分布，并确定最佳的机器学习分类器， 以最大的总体准确性区分MS队列与健康和疾病控制队列。

Diagnosis of relapsing remitting multiple sclerosis (MS) rests on clinical symptoms and examinations as outlined in the revised McDonald’s criteria supported by appropriate magnetic resonance imaging findings and other laboratory tests. The need for early diagnosis is clearly emphasized in a position paper produced in 2015 by MS Brain Health organization called “Brain health, Time matters in multiple sclerosis’ which is endorsed by the major organizations and foundations that advocate for MS research, providers and patients including Accelerated Cure Project (ACP), Americans Committed for Treatment and Research in Multiple Sclerosis (ACTRIMS), The Consortium of Multiple Sclerosis Centers (CMSC), European Brain Council (EBC), European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS), European Multiple Sclerosis Platform (EMSP), International Society of Neuroimmunology (ISNI), International Organization of Multiple Sclerosis Nurses (IOMSN), National Multiple Sclerosis Society (NMSS), and Multiple Sclerosis Trust (MS). To cite from their executive summary page: (1) “A therapeutic strategy that offers the best chance of preserving brain and spinal cord tissue early in the disease course needs to be widely accepted – and urgently adopted.” (2) “Significant delays often occur before a person with symptoms suggestive of MS sees a neurologist for diagnosis and treatment.” (3) “Early intervention is vital.” (bold type face is theirs, not ours). The question of whether or not disease classifiers capable of providing clinically useful information could be built based upon disease-specific expression levels of mRNAs in whole blood has been a subject of research for several years. Long non-coding RNAs (lncRNA) are recently discovered regulatory RNA molecules that do not code for proteins but influence a vast array of biological processes. It is also thought that lncRNAs drive biologic complexity observed in vertebrates that may also be reflected by the greater array of complex idiopathic diseases that humans develop. As such, our data obtained in the phase 1 portion of this work, support the notion that disease-associated lncRNAs exhibit far greater differences in expression than disease-associated mRNAs. In this application, we propose to explore the hypothesis that lncRNAs are better biomarkers of human disease than mRNAs. Here, we will focus on MS as a disease category and have identified and validated MS associated differentially expressed lncRNAs. Study of lncRNAs in human autoimmune disease is in its infancy and exploration of lncRNAs as biomarkers of autoimmune disease has not been previously addressed. We propose to determine expression levels of target lncRNAs in blood obtained from larger cohorts of subjects that include 1) subjects with RRMS, 2) healthy controls, 3) neurologic disease controls including both inflammatory and non- inflammatory disorders, and 4) peripheral autoimmune disease controls obtained from various sites in the U.S. and Europe to establish a wide geographic distribution and to identify optimum machine learning classifiers to distinguish the MS cohorts from healthy and disease control cohorts with greatest overall accuracy.