Di-san junyi daxue xuebao (May 2021)
Screening urinary differential proteins in bladder urothelial carcinoma based on ITRAQ technique-MRM-R language strategy
Abstract
Objective To construct a urinary differential proteins dataset for bladder urothelial carcinoma (BUC) so as to screen the core differentially expressed proteins (DEPs) and then validate them. Methods Preoperative urine samples were collected from 78 hospitalized BUC patients (55 males, 23 females; 68.9±6.6 years old) in the Urology Department of the Second Hospital of Tianjin Medical University from January 2015 to November 2016. At the same time period, urine samples from 51 healthy volunteers were also collected for control group analysis (34 males, 17 females; 60.6±11.0 years old). Using isobaric tags for relative and absolute quantitation (iTRAQ) technology, comparative proteomics study was conducted on a total of 6 mixed urine samples, including 4 cases in the BUC group and 2 cases in the control group, to find out the DEPs between the 2 groups. The top 20 DEPs were selected for multiple reaction monitoring (MRM) verification; Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was carried out with R package, clusterProfiler, and Gene Ontology (GO) enrichment analysis was subsequently performed with org.Hs.eg.db package to preliminarily select the meaningful differential proteins for BUC urinary differential protein dataset. Then the Protein-Protein Interaction (PPI) map was plot using R language to screen the core differential proteins. Furthermore, enzyme-linked immunosorbent assay (ELISA) was applied to verify the difference of core differential proteins in the urine samples. Results A total of 101 BUC urinary DEPs were obtained, of which 37 proteins were up-regulated and 64 down-regulated. MRM detected 10 DEPs. KEGG analysis found that there were 11 DEPs significantly enriched in 2 metabolic pathways, namely other chitosan degradation and complement and blood coagulation cascade. GO analysis displayed that totally 54 DEPs were mainly involved in cell adhesion molecule binding, carboxylic acid binding, organic acid binding, glycosaminoglycan binding, endopeptidase activity and other biological processes. Combining the 3 results, the BUC urinary differential protein dataset was finally established, including 69 proteins in total. By protein interaction analysis, APOE and APOA4 were screened as the core differential proteins. ELISA results showed that the average concentration of APOE was 0.55 pg/mL and 0.30 pg/mL in the BUC group and the control group, respectively, and that of APOA4 was 3.33 ng/mL and 7.01 ng/mL respectively, both with significant increases (P < 0.05). Conclusion The application of iTRAQ-MRM-R language strategy can assist the construction of BUC urinary differential protein dataset, and further screen out the core DEPs, providing a new target for the diagnosis and treatment of BUC.
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