Primary immune thrombocytopenia (ITP) is the most common acquired autoimmune bleeding disorder among children. While glucocorticoids are the primary first-line treatment for ITP treatment, they prove ineffective in certain patients. The challenge of identifying biomarkers capable of early prediction regarding the response to glucocorticoid therapy in ITP persists. This study aimed to identify ideal biomarkers for predicting glucocorticoid efficacy in patients with ITP using plasma proteomics.
A four-dimensional data-independent acquisition approach was performed to determine the differentially expressed proteins in plasma samples collected from glucocorticoid-sensitive (GCS) (n=18) and glucocorticoid-resistant (GCR) (n=17) children with ITP treated with prednisone. The significantly differentially expressed proteins were selected for enzyme-linked immunosorbent assay validation in a cohort conprising 65 samples(30 healthy controls, 18 GCS and 17 GCR children with ITP). Receiver operating characteristics curves, calibration curves, and clinical decision curve analysis were used to determine the diagnostic efficacy of this method.
47 differentially expressed proteins (36 up-regulated and 11 down-regulated) were identified in the GCR group compared with the GCS group. The significantly differentially expressed proteins myosin heavy chain 9 (MYH9) and fetuin B (FETUB) were selected for enzyme-linked immunosorbent assay validation. The validation results were consistent with the proteomics analyses. Compared with the GCS group, the GCR group exhibited a significantly reduced the plasma concentration of MYH9 and elevated the plasma concentration of FETUB. Furthermore, the receiver operating characteristics curves, calibration curves, and clinical decision curve analysis demonstrated good diagnostic efficacy of these validated biomarkers.
This study contributes to the establishment of objective biological indicators for precision therapy in children with ITP. More importantly, the proteins MYH9 and FETUB hold potential as a foundation for making informed decisions regarding alternative treatments for drugresistant patients, thereby preventing treatment delays.