Long non-coding RNA signatures to classify multiple sclerosis

复发缓解型多发性硬化症(MS)的诊断依赖于临床症状和检查 在修订的麦当劳标准中概述，并有适当的磁共振成像结果和 其他实验室检测。2015年发表的一份立场文件明确强调了早期诊断的必要性 由MS Brain Health组织编写，名为《多发性硬化症中的大脑健康，时间很重要》，由 倡导多发性硬化症研究、提供者和患者的主要组织和基金会包括 加速治愈计划(ACP)，美国人致力于多发性硬化症的治疗和研究 (ACTRIMS)，多发性硬化中心联盟(CMSC)，欧洲脑理事会(EBC)，欧洲 欧洲多发性硬化症平台治疗和研究委员会(ECTRIMS) (EMSP)、国际神经免疫学会(ISNI)、国际多发性硬化症组织 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)。引用，引证引用 他们的执行摘要页面：(1)“最有可能保护大脑的治疗策略 而在病程早期的脊髓组织需要被广泛接受--并紧急采用。 (2)“在出现多发性硬化症症状的人去看神经科医生之前，通常会出现明显的延误 用于诊断和治疗。“(3)”早期干预至关重要。“(粗体字体是他们的，不是我们的)。 疾病分类器是否能够提供临床有用信息的问题可能是 建立在全血中疾病特异性mRNAs表达水平的基础上，一直是 好几年了。长非编码rna(LncRNA)是最近发现的不具有 蛋白质的密码，但影响着大量的生物过程。也有人认为，lncRNA驱动生物 在脊椎动物身上观察到的复杂性，也可能反映在更多复杂的特发性疾病上 是人类发展起来的。因此，我们在这项工作的第一阶段获得的数据支持这样的概念 与疾病相关的lncRNA在表达上的差异比与疾病相关的mRNA大得多。在……里面 在这一应用中，我们建议探索lncRNAs是更好的人类疾病生物标记物的假设 而不是mRNAs。在这里，我们将重点关注多发性硬化症作为一种疾病类别，并已识别和验证与多发性硬化症相关的 差异表达的lncRNA。人类自身免疫性疾病中的lncRNAs研究尚处于起步阶段和 将lncRNAs作为自身免疫性疾病的生物标记物的探索以前还没有解决过。我们建议 为了确定从包括以下对象的较大队列中获得的血液中靶LncRNA的表达水平 1)RRMS受试者，2)健康对照组，3)神经疾病对照组，包括炎症性和非炎症性 炎症性疾病，以及4)从美国不同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来 以最高的总体准确度将MS队列与健康队列和疾病控制队列区分开来。

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.