Plasma Proteome Profiling of Patients With In-stent Restenosis by Tandem Mass Tag-Based Quantitative Proteomics Approach

BACKGROUND: Despite the widespread application of new drug-eluting stents, a considerable portion of patients experience in-stent restenosis (ISR). To date, the pathophysiologic mechanisms of ISR remain poorly understood. METHODS: In this study, we collected plasma samples from ISR patients (n = 29) and non-ISR patients (n = 36) after drug-eluting stent implantation, as well as from healthy controls (HCs) (n = 32). Our goal was to investigate differences in plasma protein profiles using tandem mass tag (TMT) labeling coupled with liquid chromatography and tandem mass spectrometry. The proteomic data were validated by enzyme-linked immunosorbent assay (ELISA). Bioinformatic analyses were conducted to analyze potential pathways and protein-protein interaction (PPI) involved in ISR. RESULTS: A total of 1,696 proteins were identified, of which 278 differed in protein abundance between non-ISR and HCs, 497 between ISR and HCs, and 387 between ISR and non-ISR, respectively. Bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and PPI, further demonstrated that differentially abundant proteins between ISR and non-ISR are involved in several crucial biological processes and signaling pathways, such as focal adhesion, platelet activation, Rap1 signaling, regulation of actin cytoskeleton, and cholesterol metabolism. Among the identified differentially abundant proteins in ISR, 170 were increased in abundance relative to both non-ISR patients and HCs. Some of these proteins were identified to have critical functions for atherosclerosis development and might be involved in ISR pathology. Among these proteins, 3 proteins with increased abundance including fetuin-B, apolipoprotein C-III (APOC3), and cholesteryl ester transfer protein (CETP) were confirmed by ELISA. CONCLUSIONS: This is the first study provided a comprehensive proteomic profile to understand ISR pathology, which may help identify early diagnostic biomarkers and therapeutic targets.