Burn and Shock Trauma Research Institute, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United States.
Department of Surgery, Stritch School of Medicine, Loyola University Chicago Health Sciences Division, Maywood, IL, United States.
Front Immunol. 2024 Jan 4;14:1342429. doi: 10.3389/fimmu.2023.1342429. eCollection 2023.
Sarcoidosis is a chronic granulomatous disorder characterized by unknown etiology, undetermined mechanisms, and non-specific therapies except TNF blockade. To improve our understanding of the pathogenicity and to predict the outcomes of the disease, the identification of new biomarkers and molecular endotypes is sorely needed. In this study, we systematically evaluate the biomarkers identified through Omics and non-Omics approaches in sarcoidosis. Most of the currently documented biomarkers for sarcoidosis are mainly identified through conventional "one-for-all" non-Omics targeted studies. Although the application of machine learning algorithms to identify biomarkers and endotypes from unbiased comprehensive Omics studies is still in its infancy, a series of biomarkers, overwhelmingly for diagnosis to differentiate sarcoidosis from healthy controls have been reported. In view of the fact that current biomarker profiles in sarcoidosis are scarce, fragmented and mostly not validated, there is an urgent need to identify novel sarcoidosis biomarkers and molecular endotypes using more advanced Omics approaches to facilitate disease diagnosis and prognosis, resolve disease heterogeneity, and facilitate personalized medicine.
结节病是一种慢性肉芽肿性疾病,其病因不明,发病机制尚不清楚,除 TNF 阻断剂外,尚无特异性治疗方法。为了提高对发病机制的认识,并预测疾病的结局,非常需要识别新的生物标志物和分子表型。在本研究中,我们系统地评估了通过组学和非组学方法在结节病中鉴定出的生物标志物。目前大多数已记录的结节病生物标志物主要是通过传统的“一刀切”非组学靶向研究来识别的。尽管应用机器学习算法从无偏的综合组学研究中识别生物标志物和表型仍处于起步阶段,但已经报道了一系列生物标志物,主要用于诊断以区分结节病与健康对照。鉴于目前结节病的生物标志物谱稀缺、分散且大多未经验证,因此迫切需要使用更先进的组学方法来识别新型结节病生物标志物和分子表型,以促进疾病诊断和预后、解决疾病异质性并促进个体化医学。