[Serum proteomics and machine learning unveil new diagnostic biomarkers for tuberculosis in adolescents and young adults].

Abstract

Adolescents and young adults (AYAs) are one of the major populations susceptible to tuberculosis. However, little is known about the unique characteristics and diagnostic biomarkers of tuberculosis in this population. In this study, 81 AYAs were recruited, and the high-quality serum proteome of the AYAs with tuberculosis was profiled by quantitative proteomics. The data of serum proteomics indicated that the relative abundance of hemoglobin and apolipoprotein was significantly reduced in the patients with active tuberculosis (ATB). The pathway enrichment analysis showed that the downregulated proteins in the ATB group were mainly involved in the antioxidant and cell detoxification pathways, indicating extensive oxidative stress damage. Random forest (RF) and extreme gradient boosting (XGBoost) were employed to evaluate protein importance, which yielded a set of candidate proteins that can distinguish between ATB and non-ATB. The analysis with the support vector machine algorithm (recursive feature elimination) suggested that the combination of apolipoprotein A-I (APOA1), hemoglobin subunit beta (HBB), and hemoglobin subunit alpha-1 (HBA1) had the highest accuracy and sensitivity in diagnosing ATB. Meanwhile, the levels of hemoglobin (HGB) and albumin (ALB) can be used as blood biochemical indicators to evaluate changes in the protein levels of APOA1 and HBB. This study established the serum proteome landscape of AYAs with tuberculosis and identified new biomarkers for the diagnosis of tuberculosis in this population.