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. 2013 May 14;108(9):1854-61.
doi: 10.1038/bjc.2013.157. Epub 2013 Apr 16.

Combined proteome and transcriptome analyses for the discovery of urinary biomarkers for urothelial carcinoma

N J Shimwell  1 , R T Bryan, W Wei, N D James, K K Cheng, M P Zeegers, P J Johnson, A Martin, D G Ward
Affiliations

Combined proteome and transcriptome analyses for the discovery of urinary biomarkers for urothelial carcinoma

N J Shimwell et al. Br J Cancer. .

Abstract

Background: Proteomic discovery of cancer biomarkers in body fluids is challenging because of their low abundance in a complex background. Altered gene expression in tumours may not reflect protein levels in body fluids. We have tested combining gene expression profiling of tumours with proteomic analysis of cancer cell line secretomes as a strategy to discover urinary biomarkers for bladder cancer.

Methods: We used shotgun proteomics to identify proteins secreted by three bladder cancer cell lines. Secreted proteins with high mRNA levels in bladder tumours relative to normal urothelium were assayed by ELISA in urine samples from 642 patients.

Results: Midkine and HAI-1 were significantly increased in bladder cancer patients, with the highest levels in invasive disease (area under the receiver operating characteristic curve 0.89 vs non-cancer). The urinary concentration of both proteins was too high to be explained by bladder cancer associated haematuria and most likely arises by direct tumour secretion.

Conclusions: This 'dual-omic' strategy identified tumour secreted proteins whose urine concentrations are increased significantly by bladder cancer. Combined secretome-transcriptome analysis may be more useful than direct proteomic analysis of body fluids for biomarker discovery in both bladder cancer and other tumour types.

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Figures

Figure 1
Figure 1
Classification of the proteins identified in the conditioned medium of UCB cell lines. The proteins identified in the conditioned media and in the corresponding cells were divided into categories using the GO Cellular Components function in STRING 9.0 (www.string-db.org). The number of proteins in each analysis was: 5637=622/546, EJ28=649/717, HB-CLS-2=822/462 (cell pellet/secretome).
Figure 2
Figure 2
Identification of candidate biomarkers by combining microarray and secretome data. The three data sets shown are the genes upregulated at the RNA level in Ta UCB (GSE3167 and GSE7476) and proteins identified by two or more peptides in two or more secretomes.
Figure 3
Figure 3
Urinary levels of candidate biomarkers in UCB patients: correlation with stage. Panels A, B and C show levels of midkine, HAI-1 and ULBP-2 respectively in non-UCB controls and patients with stage Ta, T1 or T2+ UCB. Data are normalised to urinary creatinine (units=pg μg−1 creatinine).
Figure 4
Figure 4
Urinary levels of candidate biomarkers in UCB patients: correlation with grade. Panels A, B and C show urinary levels of midkine, HAI-1 and ULBP-2 respectively in non-cancer controls and patients with grade 1, 2 or 3 UCB. Data are normalised to urinary creatinine (units=pg μg−1 creatinine).
Figure 5
Figure 5
Receiver operating characteristic analysis of Midkine and HAI-1 for the detection of UCB. Both proteins have been normalised to urinary creatinine. Curves are shown for Ta UCB (solid line), T1 UCB (dashed line) and T2+ UCB (dotted line) vs non-UCB.
See this image and copyright information in PMC

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