Identification of novel protein biomarkers from the blood and urine for the early diagnosis of bladder cancer via proximity extension analysis

Abstract

Background: Bladder cancer (BC) is a very common urinary tract malignancy that has a high incidence and lethality. In this study, we identified BC biomarkers and described a new noninvasive detection method using serum and urine samples for the early detection of BC.

Methods: Serum and urine samples were retrospectively collected from patients with BC (n = 99) and healthy controls (HC) (n = 50), and the expression levels of 92 inflammation-related proteins were examined via the proximity extension analysis (PEA) technique. Differential protein expression was then evaluated by univariate analysis (p < 0.05). The expression of the selected potential marker was further verified in BC and adjacent tissues by immunohistochemistry (IHC) and single-cell sequencing. A model was constructed to differentiate BC from HC by LASSO regression and compared to the detection capability of FISH.

Results: The univariate analysis revealed significant differences in the expression levels of 40 proteins in the serum (p < 0.05) and 17 proteins in the urine (p < 0.05) between BC patients and HC. Six proteins (AREG, RET, WFDC2, FGFBP1, ESM-1, and PVRL4) were selected as potential BC biomarkers, and their expression was evaluated at the protein and transcriptome levels by IHC and single-cell sequencing, respectively. A diagnostic model (a signature) consisting of 14 protein markers (11 in serum and three in urine) was also established using LASSO regression to distinguish between BC patients and HC (area under the curve = 0.91, PPV = 0.91, sensitivity = 0.87, and specificity = 0.82). Our model showed better diagnostic efficacy than FISH, especially for early-stage, small, and low-grade BC.

Conclusion: Using the PEA method, we identified a panel of potential protein markers in the serum and urine of BC patients. These proteins are associated with the development of BC. A total of 14 of these proteins can be used to detect early-stage, small, low-grade BC. Thus, these markers are promising for clinical translation to improve the prognosis of BC patients.

Keywords: Biomarkers; Bladder cancer; Diagnosis; Diagnostic model; Prognosis proximity extension assay (PEA).

Fig. 1

Strategies for serum and urinary supernatant Olink proteomics studies. Serum and urine supernatants were collected from BC patients (n = 99) and HC controls (n = 50) for the Olink-Oncology II panel. Bioinformatics analysis was subsequently performed to identify and validate potential bladder cancer liquid biopsy biomarkers. Early diagnosis of bladder cancer was achieved by the LASSO method, and model evaluation was performed. B bladder cancer, HC healthy control; AUC area under the curve

Fig. 2

Volcano plots of the differential expression of all the proteins detected in the serum and urine supernatant samples and ROC curves of the differentially expressed proteins. a The overall serum data. b In the serum, significant differences in biomarkers occurred between the BC and HC. c ROC curves of individual biomarkers in serum. d The overall data of the urine supernatant. e In the urine supernatant, significant differences in biomarkers occurred between the BC and HC. f ROC curves of individual biomarkers in the urine supernatant; P < 0.05, two-tailed t test

Fig. 3

Proteins with significant differences were subjected to GO and KEGG enrichment analyses. a KEGG enrichment analysis of urinary supernatant biomarkers (bar plot). b GO enrichment analysis of urinary supernatant biomarkers. c KEGG enrichment analysis of the serum biomarkers (bar plot). d GO enrichment analysis of the serum biomarkers. PPI network analysis of e serum and f urine supernatants

Fig. 4

Changes in the serum protein markers according to a stage, b grade, and c tumor size. Changes in protein markers in the urine supernatant with d stage, e grade, and f tumor size

Fig. 5

The results of immunohistochemical analysis of potential biomarkers of bladder cancer. a–f Immunohistochemical analysis of potential biomarkers for bladder cancer and adjacent tissues; ns: p ≥ 0.05, * p < 0.05, ** p < 0.01, and **** p < 0.0001 (two-tailed t test)

Fig. 6

Results of single-cell histology of potential biomarkers for bladder cancer. a–f The expression of potential biomarkers in the urinary supernatant of bladder cancer patients and adjacent tissues. g–i Sample classification of bladder cancer tissue and adjacent tissue

Fig. 7

Comparison between the results of the prediction model and FISH. a The area under the curve of the serum and urine supernatant integration model. b The number of features that the model features changes with the shrinkage parameter. c The characteristics of the proteins and coefficient sizes in the model. d Comparison of the AUC, sensitivity, specificity, PPV, NPV, and accuracy among serum, urine supernatant, and integrated models. e Comparison between the sensitivity of the integration model and that of FISH for PUNLMP, LG, and HG tumors. f Comparison between the sensitivity of the integration model and that of FISH at the Ta, T1, and T2-T4 stages. g Comparison between the sensitivity of the integrated model and FISH in NMIBC, MIBC, and all other patients. h Model comparison of FISH for tumor size; large (≥ 30 mm) and small (< 30 mm). i–l Enhanced CT, FISH, tissue sectioning, and cystoscopy findings in typical patients with small tumors (< 30 mm); p < 0.05; chi-square test