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

没有其他疾病的病理生理学比风湿病包含的更多。 跨越多个器官系统的临床决策通常依赖于初级保健提供者的协调努力 和风湿科医生在治疗炎症性疾病时排除或排除鉴别诊断 类风湿关节炎(RA)和系统性红斑狼疮(SLE)。RA是一种对称性、炎症性、外周性 多发性关节炎通过侵蚀骨骼和周围的软骨而导致关节畸形。系统性红斑狼疮可以虚拟地影响 任何导致疲劳、发热、肌肉疼痛、体重变化和与肾脏、中枢性、中枢性疾病相关的并发症的器官 神经系统和血液系统可能危及生命。类风湿关节炎和系统性红斑狼疮的临床诊断 排除替代诊断后的判断。在这两种疾病中，单独的实验室测试是 仅在部分疾病人群中有效。在这些分析中，敏感性和特异性 这些实验室测量对SLE的诊断可能有很高的特异性，但缺乏敏感性 在其他疾病中也可以找到标记物。正如一位医生同事所指出的那样，“很难诊断出一种 否定的“。类风湿性关节炎和系统性红斑狼疮的诊断方法通常依赖于多种独立的实验室测试 结合临床观察。在治疗后，区分这些疾病很重要 这些疾病的治疗程序是不同的。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是高效的，早期启动 治疗会带来最好的结果。这些疾病的误诊并不少见。另一种常见的 风湿科医生看到的疾病是纤维肌痛综合征(FMS)。FMS是一种普遍存在的疾病 影响肌腱、韧带和肌肉的肌肉骨骼疼痛。FMS很难诊断和治疗，而且 关键的临床要点是，FMS不能用另一种风湿性或系统性疾病来解释。因此，FMS是一种 一旦考虑并排除了其他病因，则诊断为排斥。类风湿关节炎和系统性红斑狼疮是两种疾病 这一点必须从鉴别诊断中消除。鉴于复杂的诊断过程，这些患者 通常是被迫忍受的，最近的研究也表明，诊断后节省了医疗费用 患者的预后也得到了改善。到目前为止，还没有实验室测试可以确定是否存在 从一份血样中提取这三种情况。 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FMS和风湿性疾病作为疾病类别，并已确定 并验证了FMS和风湿病相关差异表达的LncRNAs。的研究 LncRNAs在人类自身免疫性疾病中的作用尚处于起步阶段，并探索其作为自身免疫性疾病的生物标志物。 自身免疫性疾病以前没有被解决过。我们建议确定靶基因的表达水平 从更大的受试者队列中获得的血液中的lncRNA，包括1)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，5)外周自身免疫 从美国和欧洲的不同地点获得疾病控制，以建立广泛的地理分布 并确定最佳的机器学习分类器来区分纤维肌痛综合征和风湿病 来自健康和疾病控制队列的疾病具有最大的总体准确性。

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.