应用iTRAQ-MRM-R语言策略筛选膀胱尿路上皮癌尿液差异蛋白

王琳瑶; 李常颖; 李建民; 李宏杰

doi:10.16016/j.1000-5404.202011204

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应用iTRAQ-MRM-R语言策略筛选膀胱尿路上皮癌尿液差异蛋白

临床医学 | 更新时间：2026-01-09

- 应用iTRAQ-MRM-R语言策略筛选膀胱尿路上皮癌尿液差异蛋白
- Screening urinary differential proteins in bladder urothelial carcinoma based on ITRAQ technique-MRM-R language strategy
- 第三军医大学学报 2021年43卷第10期页码：982-988
- 作者机构：
- 作者简介：
- 基金信息：
  
  河北省省级科技计划(H2015209164)
- DOI：10.16016/j.1000-5404.202011204
  中图分类号：
- 收稿：2020-11-23，
  
  纸质出版：2021-05-30
- 稿件说明：
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王琳瑶, 李常颖, 李建民, 等. 应用iTRAQ-MRM-R语言策略筛选膀胱尿路上皮癌尿液差异蛋白[J]. 第三军医大学学报, 2021,43(10):982-988.

Linyao WANG, Changying LI, Jianmin LI, et al. Screening urinary differential proteins in bladder urothelial carcinoma based on ITRAQ technique-MRM-R language strategy[J]. Journal of Third Military Medical University, 2021, 43(10): 982-988.
王琳瑶, 李常颖, 李建民, 等. 应用iTRAQ-MRM-R语言策略筛选膀胱尿路上皮癌尿液差异蛋白[J]. 第三军医大学学报, 2021,43(10):982-988. DOI： 10.16016/j.1000-5404.202011204.

Linyao WANG, Changying LI, Jianmin LI, et al. Screening urinary differential proteins in bladder urothelial carcinoma based on ITRAQ technique-MRM-R language strategy[J]. Journal of Third Military Medical University, 2021, 43(10): 982-988. DOI： 10.16016/j.1000-5404.202011204.

摘要

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 (

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.

关键词

Keywords

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