Quantitative proteomics identifies and validates urinary biomarkers of rhabdomyosarcoma in children

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.

Keywords: Biomarker; Mass spectrometry; Pediatric; Rhabdomyosarcoma; Urinary proteomics.

Fig. 1

Study design of the two-stage workflow. RMS rhabdomyosarcoma, HC healthy control, RN newly diagnosed RMS patients at the time points of admission, RS RMS patients underwent surgery at the time points of admission, T0 Timepoint of the first day of admission, T1 Timepoint after 4 cycles of chemotherapy, DIA data-independent acquisition, PRM parallel reaction monitoring analysis, DP differential proteins, ROC receiver operating characteristic curve

Fig. 2

Clinical characteristics of subjects and urine proteome changes. A Gender distribution of cohort 1 and cohort 2. B Age distribution of cohort 1 and cohort 2. CAnalysis of albumin proportion to total proteins in urine samples. D Analysis of urine samples contaminated with erythrocytes. E The correlation analysis of 12 QC samples by Pearson correlation coefficient. F The number of identified proteins in RN0, RN1, RS0, RS1, HC, and QC groups. RMS: rhabdomyosarcoma. HC healthy control, QC quality control samples, RN0 newly diagnosed RMS at the time points of admission, RN1 newly diagnosed RMS at the time points after 4 cycles of chemotherapy, RS0 RMS underwent surgery at the time points of admission, RS1 RMS underwent surgery at the time points after 4 cycles of chemotherapy

Fig. 3

The global proteomic profiles using DIA LC–MS/MS quantitative proteomics strategy in cohort 1. A The heatmap of global proteomic profiles and the clustering results of the samples from five different groups. B Venn diagram of proteome distribution between five groups. C Up-regulated differential proteins between Groups 1, 2, and 3. D Down-regulated differential proteins between Groups 1, 2, and 3. RN0: newly diagnosed RMS at the time points of admission. RN1 newly diagnosed RMS at the time points after 4 cycles of chemotherapy, RS0 RMS underwent surgery at the time points of admission, RS1 RMS underwent surgery at the time points after 4 cycles of chemotherapy, HC healthy control

Fig. 4

Pattern recognition analysis (OPLS-DA) of 251 differential proteins among RN0, RN1, RS0, RS1, and HC identified by DIA strategy. OPLS-DA orthogonal projection to latent structures discriminant analysis, RN0 newly diagnosed RMS at the time points of admission, RN1 newly diagnosed RMS at the time points after 4 cycles of chemotherapy, RS0 RMS underwent surgery at the time points of admission, RS1 RMS underwent surgery at the time points after 4 cycles of chemotherapy, HC healthy control

Fig. 5

Annotation and functional characterization of differential proteins. A Biological process analysis of differential proteins. B Bubble diagram of pathway analysis between differential proteins. C The distribution of the tissue-enriched proteins

Fig. 6

Comparison of the effects of pre-and post-chemotherapy (Group 1: RN0 vs RN1 vs HC), pre-and post-surgery (Group 2: RN0 vs RS0 vs HC), and surgery plus chemotherapy (Group 3: RN0 vs RS0 vs RS1 vs HC) on the distribution of urine protein identified by DIA and PRM. RMS: rhabdomyosarcoma. HC healthy control, RN0 newly diagnosed RMS at the time points of admission, RN1 newly diagnosed RMS at the time points after 4 cycles of chemotherapy, RS0 RMS underwent surgery at the time points of admission, RS1 RMS underwent surgery at the time points after 4 cycles of chemotherapy, DIA data-independent acquisition, PRM parallel reaction monitoring analysis, DP differential proteins

Fig. 7

Proteome distribution and diagnostic panel construction for RMS and HC using PRM-based targeted proteomic method. A Up-regulated differential proteins between groups 1, 2, and 3 using PRM. B Down-regulated differential proteins between groups 1, 2, and 3 using PRM. C ROC curve of the diagnostic panel for RMS and HC from the tenfold cross-validation