Long non-coding RNA signatures to distinguish fibromyalgia syndrome from rheumatic diseases

长非编码 RNA 特征可区分纤维肌痛综合征和风湿性疾病

• 批准号: 9555179
• 负责人: Thomas M. Aune
• 金额: $ 49.83万
• 依托单位: IQUITY LABS, INC
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-01-02 至 2020-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9555179
• 关键词: Address; Affect; Area; Autoimmune Diseases; Biological Markers; Biological Process; Blood; Blood specimen; Body Weight Changes; Cartilage; Categories; Clinical; Code; Complex; Data; Deformity; Diagnosis; Diagnostic; Differential Diagnosis; Disease; Early Diagnosis; Etiology; Europe; Exclusion; Exhibits; Fatigue; Fever; Fibromyalgia; Functional disorder; Gene Expression Profile; Genes; Geographic Distribution; Health Personnel; Healthcare; Human; Individual; Inflammatory; Invertebrates; Investigation; Joints; Judgment; Kidney; Laboratories; Life; Ligaments; Machine Learning; Measurement; Messenger RNA; Mixed Connective Tissue Disease; Muscle; Musculoskeletal Pain; Myalgia; Neuraxis; Organ; Organism; Outcome; Patient-Focused Outcomes; Patients; Pattern; Peripheral; Phase; Physicians; Polyarthritides; Population; Procedures; Process; Proteins; RNA; Research; Research Subjects; Rheumatism; Rheumatoid Arthritis; Sensitivity and Specificity; Site; Sjogren' s Syndrome; Specificity; System; Systemic Lupus Erythematosus; Tendon structure; Testing; Time; Untranslated RNA; Vertebrates; Whole Blood; Work; base; body system; bone erosion; care providers; cell type; cohort; cost; diagnostic biomarker; differential expression; disease diagnosis; disorder control; human disease; improved outcome; infancy; phase 1 study; rheumatologist; tool; virtual

No other group of diseases encompasses a greater pathophysiology than do the rheumatic diseases. Spanning multiple organ systems, clinical decisions often rely on coordinated efforts from primary care providers and rheumatologists to rule in or rule out differential diagnoses when treating inflammatory conditions such as rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). RA is a symmetric, inflammatory, peripheral polyarthritis leading to deformity of joints via erosion of bone and surrounding cartilage. SLE can affect virtually any organ leading to fatigue, fever, myalgia, weight change and complications associated with renal, central nervous system, and hematologic systems can be life-threatening. RA and SLE are diagnosed through clinical judgment after excluding alternative diagnoses. In the case of both diseases, individual laboratory tests are effective only in a portion of the disease population. Across these analyses, the sensitivity and specificity for these laboratory measurements may have high specificity to rule in SLE but lack sensitivity as these diagnostic markers can be found in other disorders. As a physician colleague pointed out, “it is difficult to diagnose a negative”. Diagnostic approaches for both RA and SLE often rely on multiple, independent laboratory tests combined with clinical observation. Distinguishing between these diseases is important since treatment procedures for these diseases are different. Time is a factor in diagnosis of these diseases and tools are required to facilitate earlier diagnosis as treatment for autoimmune diseases are highly effective and early initiation of therapy leads to the best outcomes. Misdiagnosis of these conditions is not uncommon. Another common disease seen by rheumatologists is fibromyalgia syndrome (FMS). FMS is a common cause of widespread musculoskeletal pain that affects tendons, ligaments, and muscle. FMS is difficult to diagnose and treat and a critical clinical point is that FMS is not explained by another rheumatic or systemic disorder. Thus, FMS is a diagnosis of exclusion once other etiologies have been considered and excluded. RA and SLE are two diseases that must be eliminated from the differential diagnosis. Given the complicated diagnostic process these patients are often forced to endure, recent studies have also suggested that healthcare dollars are saved post-diagnosis and patient outcomes improve. To date, there is no laboratory test that can determine presence or absence of these three conditions from a single blood sample. 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 FMS and the rheumatic diseases as disease categories and have identified and validated FMS and rheumatic disease-associated 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 fibromyalgia syndrome, 2) healthy controls, 3) rheumatoid arthritis, 4) systemic lupus erythematosus, and 5) 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 fibromyalgia syndrome and rheumatic diseases from healthy and disease control cohorts with greatest overall accuracy.

没有任何一组疾病比风湿性疾病包含更大的病理生理学。 跨越多个器官系统，临床决策往往依赖于初级保健提供者的协调努力 和风湿病学家在治疗炎症性疾病时， 类风湿性关节炎（RA）和系统性红斑狼疮（SLE）。RA是一种对称性、炎性、外周性 多发性关节炎，通过骨和周围软骨的侵蚀导致关节畸形。SLE几乎可以影响 任何器官导致疲劳、发热、肌痛、体重变化和与肾脏、中枢神经系统 神经系统和血液系统可能危及生命。RA和SLE通过临床诊断 排除其他诊断后的判断。在这两种疾病的情况下， 仅对一部分疾病人群有效。在这些分析中， 这些实验室测量可能对SLE的规则具有高特异性，但缺乏敏感性，因为这些诊断 在其他疾病中也可以找到标记。正如一位医生同事指出的那样，“很难诊断出一种 消极的”。RA和SLE的诊断方法通常依赖于多种独立的实验室检查 结合临床观察。区分这些疾病很重要，因为治疗 这些疾病的治疗方法是不同的。时间是诊断这些疾病的一个因素，需要工具 促进早期诊断，因为治疗自身免疫性疾病非常有效， 治疗会带来最好的结果这些情况的误诊并不罕见。另一个常见 风湿病学家发现的一种疾病是纤维肌痛综合征（FMS）。FMS是一种常见的原因， 影响肌腱、韧带和肌肉的肌肉骨骼疼痛。FMS很难诊断和治疗， 临床上的关键点是FMS不能用其他风湿性或全身性疾病来解释。因此，FMS是一种 一旦考虑并排除了其他病因，即可进行排除诊断。RA和SLE是两种疾病 必须从鉴别诊断中排除鉴于诊断过程复杂， 经常被迫忍受，最近的研究也表明，医疗费用节省后诊断 患者的治疗效果得到改善。到目前为止，还没有实验室测试可以确定是否存在 这三种情况的血液样本 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，我们将重点放在FMS和风湿性疾病的疾病类别，并已确定 并验证了FMS和风湿性疾病相关的差异表达lncRNA。研究 lncRNA在人类自身免疫性疾病中的作用尚处于起步阶段， 自身免疫性疾病以前没有被提及。我们建议测定靶基因的表达水平， 血液中的lncRNA获自较大的受试者队列，包括1）患有纤维肌痛综合征的受试者， 2)健康对照，3）类风湿性关节炎，4）系统性红斑狼疮，和5）外周自身免疫 从美国和欧洲的各个地点获得的疾病控制，以建立广泛的地理分布 并确定最佳机器学习分类器以区分纤维肌痛综合征和风湿性关节炎， 健康和疾病控制队列的疾病具有最大的总体准确性。