Cancer Res Treat.  2022 Jul;54(3):882-893. 10.4143/crt.2021.642.

Next-generation Proteomics-Based Discovery, Verification, and Validation of Urine Biomarkers for Bladder Cancer Diagnosis

• Suh J
• Han D
• Ku JH
• Kim HH
• Kwak C
• Jeong CW

• KMID: 2531334
• DOI: http://doi.org/10.4143/crt.2021.642

Abstract

Purpose
We aimed to identify, verify, and validate a multiplex urinary biomarker-based prediction model for diagnosis and surveillance of urothelial carcinoma of bladder, using high-throughput proteomics methods.
Materials and Methods
Label-free quantification of data-dependent and data-independent acquisition of 12 and 24 individuals was performed in each of the discovery and verification phases using mass spectrometry, simultaneously using urinary exosome and proteins. Based on five scoring system based on proteomics data and statistical methods, we selected eight proteins. Enzyme-linked immunosorbent assay on urine from 120 patients with bladder mass lesions used for validation. Using multivariable logistic regression, we selected final candidate models for predicting bladder cancer.
Results
Comparing the discovery and verification cohorts, 38% (50/132 exosomal differentially expressed proteins [DEPs]) and 44% (109/248 urinary DEPs) are consistent at statistically significance, respectively. The 20 out of 50 exosome proteins and 27 out of 109 urinary proteins were upregulated in cancer patients. From eight selected proteins, we developed two diagnostic models for bladder cancer. The area under the receiver operating characteristic curve (AUROC) of two models were 0.845 and 0.842, which outperformed AUROC of urine cytology.
Conclusion
The results showed that the two diagnostic models developed here were more accurate than urine cytology. We successfully developed and validated a multiplex urinary protein-based prediction, which will have wide applications for the rapid diagnosis of urothelial carcinoma of the bladder. External validation for this biomarker panel in large population is required.

Keyword

Proteomics; Diagnosis; Urine biomarkers; Urinary bladder neoplasms

Figure

• 

  Fig. 1 The overall workflow of urine protein biomarkers development. ELISA, enzyme-linked immunosorbent assay; LC-DIA/MS, liquid-chromatography data independent acquisition mass spectrometry; LC-MS/MS, liquid chromatography-tandem mass spectrometry.

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  Fig. 2 Results of label-free quantification in the discovery stage. (A) Proteomic workflow of label-free quantification. (B) Number of Identification and quantification in urine and exosome. (C) Volcano plots. (D) Principal component analysis plots. FASP, filter-aided sample preparation; LC-MS/MS, liquid chromatography-tandem mass spectrometry.

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  Fig. 3 Results of label-free quantification in the discovery stage. (A) Proteomic workflow of label-free quantification. (B) Flowchart of verification process using data-independent acquisition approach. (C) Correlation of protein control/urothelial carcinoma fold changes between the discovery and verification cohorts. DEP, differentially expressed protein; FASP, filter-aided sample preparation; LC-DIA/MS, liquid-chromatography data independent acquisition mass spectrometry.

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  Fig. 4 Receiver operating characteristic for diagnosis of bladder cancer by each candidate proteins (A) and developed multiplex biomarker models (B). Model 1 for selected proteins and model 2 for all protein-based model. A2M, alpha-2 macroglobulin; AFM, afamin; APOA1, apolipoprotein A-I; AUROC, area under the receiver operating characteristic curve; CD5L, CD5 antigen-like protein; CDC5L, cell division cycle 5-like protein; CFL1, cofilin-1; CI, confidence interval; FGA, fibrinogen alpha chain; ITIH2, inter-alpha-trypsin inhibitor heavy chain H2.

Reference

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