Urinary endogenous peptides as biomarkers for prostate cancer

De Souza Dutra,Cristine; Da Cruz Schafhauser,Deborah; Hentz,Mariana; Mayer,Nicole Raupp; Pinheiro,Raiane Medeiros; Baierle,Gabriele; Kist,Djulia Rafaella; Bullé,Danielly Joani; Donaduzzi,Rodrigo Cattelan; Bohmgahren,Marcus Falcão; Zaha,Arnaldo; Ferreira,Henrique Bunselmeyer; Possuelo,Lia Gonçalves; Monteiro,Karina Mariante

doi:10.3892/ol.2023.13759

Urinary endogenous peptides as biomarkers for prostate cancer

Authors:
- Cristine De Souza Dutra
- Deborah Da Cruz Schafhauser
- Mariana Hentz
- Nicole Raupp Mayer
- Raiane Medeiros Pinheiro
- Gabriele Baierle
- Djulia Rafaella Kist
- Danielly Joani Bullé
- Rodrigo Cattelan Donaduzzi
- Marcus Falcão Bohmgahren
- Arnaldo Zaha
- Henrique Bunselmeyer Ferreira
- Lia Gonçalves Possuelo
- Karina Mariante Monteiro
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Affiliations: Laboratory of Structural and Functional Genomics, Biotechnology Center, Federal University of Rio Grande do Sul, Porto Alegre, RS, 91501-970, Brazil, Department of Life Sciences, University of Santa Cruz do Sul, Santa Cruz do Sul, RS, 96815-900, Brazil, Integrated Oncology Center, Ana Nery Hospital, Santa Cruz do Sul, RS, 96835-090, Brazil, Urology Service, Ernesto Dornelles Hospital, Porto Alegre, RS, 90160-092, Brazil
Published online on: March 14, 2023 https://doi.org/10.3892/ol.2023.13759
Article Number: 173
Copyright: © De Souza Dutra et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Prostate cancer (PCa) is one of the most prevalent types of cancer in men worldwide; however, the main diagnostic tests available for PCa have limitations and a biopsy is required for histopathological confirmation of the disease. Prostate‑specific antigen (PSA) is the main biomarker used for the early detection of PCa, but an elevated serum concentration is not cancer‑specific. Therefore, there is a need for the discovery of new non‑invasive biomarkers that can accurately diagnose PCa. The present study used trichloroacetic acid‑induced protein precipitation and liquid chromatography‑mass spectrometry to profile endogenous peptides in urine samples from patients with PCa (n=33), benign prostatic hyperplasia (n=25) and healthy individuals (n=28). Receiver operating characteristic curve analysis was performed to evaluate the diagnostic performance of urinary peptides. In addition, Proteasix tool was used for in silico prediction of protease cleavage sites. Five urinary peptides derived from uromodulin were revealed to be significantly altered between the study groups, all of which were less abundant in the PCa group. This peptide panel showed a high potential to discriminate between the study groups, resulting in area under the curve (AUC) values between 0.788 and 0.951. In addition, urinary peptides outperformed PSA in discriminating between malignant and benign prostate conditions (AUC=0.847), showing high sensitivity (81.82%) and specificity (88%). From in silico analyses, the proteases HTRA2, KLK3, KLK4, KLK14 and MMP25 were identified as potentially involved in the degradation of uromodulin peptides in the urine of patients with PCa. In conclusion, the present study allowed the identification of urinary peptides with potential for use as non‑invasive biomarkers in PCa diagnosis.

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1	Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A and Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 71:209–249. 2021. View Article : Google Scholar : PubMed/NCBI
2	Heidenreich A, Bellmunt J, Bolla M, Joniau S, Mason M, Matveev V, Mottet N, Schmid HP, Van Der Kwast T, Wiegel T, et al: EAU guidelines on prostate cancer. Part 1: Screening, diagnosis, and treatment of clinically localised disease. Eur Urol. 59:61–71. 2011. View Article : Google Scholar : PubMed/NCBI
3	Gretzer MB and Partin AW: PSA levels and the probability of prostate cancer on biopsy. Eur Urol. (Suppl 1):21–27. 2002. View Article : Google Scholar
4	Thompson IM, Pauler DK, Goodman PJ, Tangen CM, Lucia MS, Parnes HL, Minasian LM, Ford LG, Lippman SM, Crawford ED, et al: Prevalence of prostate cancer among men with a prostate-specific antigen level < or =4.0 ng per milliliter. N Engl J Med. 350:2239–2246. 2004. View Article : Google Scholar : PubMed/NCBI
5	Oshi M, Murthy V, Takahashi H, Huyser M, Okano M, Tokumaru Y, Rashid OM, Matsuyama R, Endo I and Takabe K: Urine as a source of liquid biopsy for cancer. Cancers (Basel). 13:26522021. View Article : Google Scholar : PubMed/NCBI
6	Wood SL, Knowles MA, Thompson D, Selby PJ and Banks RE: Proteomic studies of urinary biomarkers for prostate, bladder and kidney cancers. Nat Rev Urol. 10:206–218. 2013. View Article : Google Scholar : PubMed/NCBI
7	Jedinak A, Loughlin KR and Moses MA: Approaches to the discovery of non-invasive urinary biomarkers of prostate cancer. Oncotarget. 9:32534–32550. 2018. View Article : Google Scholar : PubMed/NCBI
8	Koo KM, Mainwaring PN, Tomlins SA and Trau M: Merging new-age biomarkers and nanodiagnostics for precision prostate cancer management. Nat Rev Urol. 16:302–317. 2019. View Article : Google Scholar : PubMed/NCBI
9	Tonry C, Finn S, Armstrong J and Pennington SR: Clinical proteomics for prostate cancer: Understanding prostate cancer pathology and protein biomarkers for improved disease management. Clin Proteomics. 17:412020. View Article : Google Scholar : PubMed/NCBI
10	Parker BL, Burchfield JG, Clayton D, Geddes TA, Payne RJ, Kiens B, Wojtaszewski JFP, Richter EA and James DE: Multiplexed temporal quantification of the exercise-regulated plasma peptidome. Mol Cell Proteomics. 16:2055–2068. 2017. View Article : Google Scholar : PubMed/NCBI
11	Perez-Riverol Y, Bai J, Bandla C, García-Seisdedos D, Hewapathirana S, Kamatchinathan S, Kundu DJ, Prakash A, Frericks-Zipper A, Eisenacher M, et al: The PRIDE database resources in 2022: A Hub for mass spectrometry-based proteomics evidences. Nucleic Acids Res. 50((D1)): D543–D552. 2022. View Article : Google Scholar : PubMed/NCBI
12	Schilling B, Rardin MJ, MacLean BX, Zawadzka AM, Frewen BE, Cusack MP, Sorensen DJ, Bereman MS, Jing E, Wu CC, et al: Platform-independent and label-free quantitation of proteomic data using MS1 extracted ion chromatograms in skyline: Application to protein acetylation and phosphorylation. Mol Cell Proteomics. 11:202–214. 2012. View Article : Google Scholar : PubMed/NCBI
13	Willforss J, Chawade A and Levander F: NormalyzerDE: Online tool for improved normalization of omics expression data and high-sensitivity differential expression analysis. J Proteome Res. 18:732–740. 2019. View Article : Google Scholar : PubMed/NCBI
14	Klein J, Eales J, Zürbig P, Vlahou A, Mischak H and Stevens R: Proteasix: A tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation. Proteomics. 13:1077–1082. 2013. View Article : Google Scholar : PubMed/NCBI
15	Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI
16	Loeb S and Partin AW: Review of the Literature: PCA3 for prostate cancer risk assessment and prognostication. Rev Urol. 13:e191–e195. 2011.PubMed/NCBI
17	Fujita K and Nonomura N: Urinary biomarkers of prostate cancer. Int J Urol. 25:770–779. 2018. View Article : Google Scholar : PubMed/NCBI
18	Zheng B, Zhang P, Wang H, Wang J, Liu ZH and Zhang DH: Advances in research on bladder cancer targeting peptides: A review. Cell Biochem Biophys. 79:711–718. 2021. View Article : Google Scholar : PubMed/NCBI
19	Mavrogeorgis E, Mischak H, Latosinska A, Siwy J, Jankowski V and Jankowski J: Reproducibility evaluation of urinary peptide detection using CE-MS. Molecules. 26:72602021. View Article : Google Scholar : PubMed/NCBI
20	Van JAD, Clotet-Freixas S, Zhou J, Batruch I, Sun C, Glogauer M, Rampoldi L, Elia Y, Mahmud FH, Sochett E, et al: Peptidomic analysis of urine from youths with early type 1 diabetes reveals novel bioactivity of uromodulin peptides in vitro. Mol Cell Proteomics. 19:501–517. 2020. View Article : Google Scholar : PubMed/NCBI
21	Nkuipou-Kenfack E, Bhat A, Klein J, Jankowski V, Mullen W, Vlahou A, Dakna M, Koeck T, Schanstra JP, Zürbig P, et al: Identification of ageing-associated naturally occurring peptides in human urine. Oncotarget. 6:34106–34117. 2015. View Article : Google Scholar : PubMed/NCBI
22	Good DM, Zürbig P, Argilés À, Bauer HW, Behrens G, Coon JJ, Dakna M, Decramer S, Delles C, Dominiczak AF, et al: Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease. Mol Cell Proteomics. 9:2424–2437. 2010. View Article : Google Scholar : PubMed/NCBI
23	Principe S, Kim Y, Fontana S, Ignatchenko V, Nyalwidhe JO, Lance RS, Troyer DA, Alessandro R, Semmes OJ, Kislinger T, et al: Identification of prostate-enriched proteins by in-depth proteomic analyses of expressed prostatic secretions in urine. J Proteome Res. 11:2386–2396. 2012. View Article : Google Scholar : PubMed/NCBI
24	Haffner MC, Zwart W, Roudier MP, True LD, Nelson WG, Epstein JI, De Marzo AM, Nelson PS and Yegnasubramanian S: Genomic and phenotypic heterogeneity in prostate cancer. Nat Rev Urol. 18:79–92. 2021. View Article : Google Scholar : PubMed/NCBI
25	Xiao K, Guo J, Zhang X, Feng X, Zhang H, Cheng Z, Johnson H, Persson JL and Chen L: Use of two gene panels for prostate cancer diagnosis and patient risk stratification. Tumour Biol. 37:10115–10122. 2016. View Article : Google Scholar : PubMed/NCBI
26	Alarcón-Zendejas AP, Scavuzzo A, Jiménez-Ríos MA, Álvarez-Gómez RM, Montiel-Manríquez R, Castro-Hernández C, Jiménez-Dávila MA, Pérez-Montiel D, González-Barrios R, Jiménez-Trejo F, et al: The promising role of new molecular biomarkers in prostate cancer: From coding and non-coding genes to artificial intelligence approaches. Prostate Cancer Prostatic Dis. 5:431–443. 2022. View Article : Google Scholar : PubMed/NCBI
27	Lone SN, Nisar S, Masoodi T, Singh M, Rizwan A, Hashem S, El-Rifai W, Bedognetti D, Batra SK, Haris M, et al: Liquid biopsy: A step closer to transform diagnosis, prognosis and future of cancer treatments. Mol Cancer. 21:792022. View Article : Google Scholar : PubMed/NCBI
28	Hessels D and Schalken JA: Urinary biomarkers for prostate cancer: A review. Asian J Androl. 15:333–339. 2013. View Article : Google Scholar : PubMed/NCBI
29	Padoan A, Basso D, Zambon CF, Prayer-Galetti T, Arrigoni G, Bozzato D, Moz S, Zattoni F, Bellocco R and Plebani M: MALDI-TOF peptidomic analysis of serum and post-prostatic massage urine specimens to identify prostate cancer biomarkers. Clin Proteomics. 15:232018. View Article : Google Scholar : PubMed/NCBI
30	Clarke RA, Schirra HJ, Catto JW, Lavin MF and Gardiner RA: Markers for detection of prostate cancer. Cancers (Basel). 2:1125–1154. 2010. View Article : Google Scholar : PubMed/NCBI
31	Good DM, Thongboonkerd V, Novak J, Bascands JL, Schanstra JP, Coon JJ, Dominiczak A and Mischak H: Body fluid proteomics for biomarker discovery: Lessons from the past hold the key to success in the future. J Proteome Res. 6:4549–4555. 2007. View Article : Google Scholar : PubMed/NCBI
32	Schönemeier B, Metzger J, Klein J, Husi H, Bremer B, Armbrecht N, Dakna M, Schanstra JP, Rosendahl J, Wiegand J, et al: Urinary peptide analysis differentiates pancreatic cancer from chronic pancreatitis. Pancreas. 45:1018–1026. 2016. View Article : Google Scholar : PubMed/NCBI
33	Theodorescu D, Fliser D, Wittke S, Mischak H, Krebs R, Walden M, Ross M, Eltze E, Bettendorf O, Wulfing C and Semjonow A: Pilot study of capillary electrophoresis coupled to mass spectrometry as a tool to define potential prostate cancer biomarkers in urine. Electrophoresis. 26:2797–2808. 2005. View Article : Google Scholar : PubMed/NCBI
34	Okamoto A, Yamamoto H, Imai A, Hatakeyama S, Iwabuchi I, Yoneyama T, Hashimoto Y, Koie T, Kamimura N, Mori K, et al: Protein profiling of post-prostatic massage urine specimens by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry to discriminate between prostate cancer and benign lesions. Oncol Rep. 21:73–79. 2009.PubMed/NCBI
35	M'Koma AE, Blum DL, Norris JL, Koyama T, Billheimer D, Motley S, Ghiassi M, Ferdowsi N, Bhowmick I, Chang SS, et al: Detection of pre-neoplastic and neoplastic prostate disease by MALDI profiling of urine. Biochem Biophys Res Commun. 353:829–834. 2007. View Article : Google Scholar : PubMed/NCBI
36	Serafini-Cessi F, Malagolini N and Cavallone D: Tamm-Horsfall glycoprotein: Biology and clinical relevance. Am J Kidney Dis. 42:658–676. 2003. View Article : Google Scholar : PubMed/NCBI
37	Fuhrman-Luck RA, Loessner D and Clements JA: Kallikrein-Related peptidases in prostate cancer: From molecular function to clinical application. EJIFCC. 25:2692014.PubMed/NCBI
38	Hong SK: Kallikreins as biomarkers for prostate cancer. Biomed Res Int. 2014:5263412014. View Article : Google Scholar : PubMed/NCBI
39	Riddick ACP, Shukla CJ, Pennington CJ, Bass R, Nuttall RK, Hogan A, Sethia KK, Ellis V, Collins AT, Maitland NJ, et al: Identification of degradome components associated with prostate cancer progression by expression analysis of human prostatic tissues. Br J Cancer. 92:2171–2180. 2005. View Article : Google Scholar : PubMed/NCBI
40	Shimoda M and Khokha R: Metalloproteinases in extracellular vesicles. Biochim Biophys Acta Mol Cell Res. 1864((11 Pt A)): 1989–2000. 2017. View Article : Google Scholar : PubMed/NCBI
41	Mary S, Small HY, Siwy J, Mullen W, Giri A and Delles C: Polymerization-incompetent uromodulin in the pregnant stroke-prone spontaneously hypertensive rat. Hypertens. 69:910–918. 2017. View Article : Google Scholar : PubMed/NCBI
42	Yu Y, Sikorski P, Smith M, Bowman-Gholston C, Cacciabeve N, Nelson KE and Pieper R: Comprehensive metaproteomic analyses of urine in the presence and absence of neutrophil-associated inflammation in the urinary tract. Theranostics. 7:238–252. 2017. View Article : Google Scholar : PubMed/NCBI
43	Wei P, Hao L, Ma F, Yu Q, Buchberger AR, Lee S, Bushman W and Li L: Urinary metabolomic and proteomic analyses in a mouse model of prostatic inflammation. Urine (Amst). 1:17–23. 2019. View Article : Google Scholar : PubMed/NCBI