Quantitative proteomics identifies and validates urinary biomarkers of rhabdomyosarcoma in children

Abstract

Abstract Background Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma with poor prognosis in children. The 5-year survival rate for early RMS has improved, whereas it remains unsatisfactory for advanced patients. Urine can rapidly reflect changes in the body and identify low-abundance proteins. Early screening of tumor markers through urine in RMS allows for earlier treatment, which is associated with better outcomes. Methods RMS patients under 18 years old, including those newly diagnosed and after surgery, were enrolled. Urine samples were collected at the time points of admission and after four cycles of chemotherapy during follow-up. Then, a two-stage workflow was established. (1) In the discovery stage, differential proteins (DPs) were initially identified in 43 RMS patients and 12 healthy controls (HCs) using a data-independent acquisition method. (2) In the verification stage, DPs were further verified as biomarkers in 54 RMS patients and 25 HCs using parallel reaction monitoring analysis. Furthermore, a receiver operating characteristic (ROC) curve was used to construct the protein panels for the diagnosis of RMS. Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software were used to perform bioinformatics analysis. Results A total of 251 proteins were significantly altered in the discovery stage, most of which were enriched in the head, neck and urogenital tract, consistent with the most common sites of RMS. The most overrepresented biological processes from GO analysis included immunity, inflammation, tumor invasion and neuronal damage. Pathways engaging the identified proteins revealed 33 common pathways, including WNT/β-catenin signaling and PI3K/AKT signaling. Finally, 39 proteins were confirmed as urinary biomarkers for RMS, and a diagnostic panel composed of 5 candidate proteins (EPS8L2, SPARC, HLA-DRB1, ACAN, and CILP) was constructed for the early screening of RMS (AUC: 0.79, 95%CI = 0.66 ~ 0.92). Conclusions These findings provide novel biomarkers in urine that are easy to translate into clinical diagnosis of RMS and illustrate the value of global and targeted urine proteomics to identify and qualify candidate biomarkers for noninvasive molecular diagnosis.