J Pathol Transl Med.  2023 Jan;57(1):67-74. 10.4132/jptm.2022.10.24.

The proteomic landscape shows oncologic relevance in cystitis glandularis

• Kim JY
• Han D
• Kim H
• Jung M
• Ryu HS

• KMID: 2539038
• DOI: http://doi.org/10.4132/jptm.2022.10.24

Abstract

Background
The relationship between cystitis glandularis (CG) and bladder malignancy remains unclear.
Methods
We identified the oncologic significance of CG at the molecular level using liquid chromatography-tandem mass spectrometry-based proteomic analysis of 10 CG, 12 urothelial carcinoma (UC), and nine normal urothelium (NU) specimens. Differentially expressed proteins (DEPs) were identified based on an analysis of variance false discovery rate < 0.05, and their functional enrichment was analyzed using a network model, Gene Set Enrichment Analysis, and Gene Ontology annotation.
Results
We identified 9,890 proteins across all samples and 1,139 DEPs among the three entities. A substantial number of DEPs overlapped in CG/NU, distinct from UC. Interestingly, we found that a subset of DEP clusters (n = 53, 5%) was differentially expressed in NU but similarly between CG and UC. This “UC-like signature” was enriched for reactive oxygen species (ROS) and energy metabolism, growth and DNA repair, transport, motility, epithelial-mesenchymal transition, and cell survival. Using the top 10 shortlisted DEPs, including SOD2, PRKCD, CYCS, and HCLS1, we identified functional elements related to ROS metabolism, development, and transport using network analysis. The abundance of these four molecules in UC/CG than in NU was consistent with the oncologic functions in CG.
Conclusions
Using a proteomic approach, we identified a predominantly non-neoplastic landscape of CG, which was closer to NU than to UC. We also confirmed a small subset of common DEPs in UC and CG, suggesting that altered ROS metabolism might imply potential cancerous risks in CG.

Keyword

Cystitis; Urinary bladder neoplasms; Carcinoma; transitional cell; Proteomics; Tandem mass spectrometry

Figure

• 

  Fig. 1 Schematic outline of the study. (Top) Liquid chromatography-tandem mass spectrometry (LC-MS/MS)–based proteomic analysis of 12 urothelial carcinoma (UC),10 cystitis glandularis (CGs), and nine normal urothelial (NU) specimens identified 9,890 proteins. Formalin-fixed paraffin-embedded (FFPE) slides were scrapped and proteins were isolated. After sonication and precipitation of samples, proteins were digested according to the filter-aided sample preparation (FASP) procedure. (Bottom) Differentially expressed proteins (DEPs) were discovered based on analysis of variance (false discovery rate < 0.05), and DEPs with UC-like signatures were revealed in CG. Functional enrichment analysis was performed for the DEPs and top 10 DEPs, respectively.

• 

  Fig. 2 Unsupervised hierarchical clustering of 1,139 differentially expressed proteins identified by an analysis of variance test of urothelial carcinoma (UC), cystitis glandularis (CG), and normal urothelium (NU). Arrows indicate a “UC-like signature” that showed disparity between UC/CG and NU. Asterisks indicate CG samples showing intestinal metaplasia.

• 

  Fig. 3 Pathobiologic characteristics of the “urothelial carcinoma (UC)-like signature”. (A) The UC-like signature proteins. Asterisks denote the 10 top-listed proteins by false discovery rate (FDR). (B) Protein-protein interaction networks of the “UC-like signature” proteins with their common functions. (C) Gene Set Enrichment Analysis of the “UC-like signature” shows enrichment of response to ultraviolet in molecular hallmark function. CG, cystitis glandularis; EMT, epithelial-mesenchymal transition; FWER, family wise error rate; NES, normalized enrichment score; NU, normal urothelium; ROS, reactive oxygen species.

• 

  Fig. 4 Imperative functions enriched in the “urothelial carcinoma-like signature”. (A) Gene Ontology-biologic processes (GOBPs) represented by the top 10 shortlisted proteins of “urothelial carcinoma-like signature”. (B) Network analysis of the GOBPs identifies reactive oxygen species (ROS) metabolism, structure development, and transport as common functional themes. (C) SOD2, PRKCD, CYCS, and HCLS1 were comparable between cystitis glandularis with and without intestinal metaplasia (IM). The value is expressed with z-scores of the proteome abundance. FDR, false discovery rate.

Reference

References

1. Williamson SR, Lopez-Beltran A, Montironi R, Cheng L. Glandular lesions of the urinary bladder: clinical significance and differential diagnosis. Histopathology. 2011; 58:811–34.

2. Samaratunga H, Martignoni G, Egevad L, Delahunt B. Premalignant lesions of the urinary bladder. Pathology. 2013; 45:243–50.

3. Song YS, Jang KS, Jang SH, et al. The intestinal type of florid cystitis glandularis mimics bladder tumor: a case report. Korean J Pathol. 2007; 41:116–8.

4. Morton MJ, Zhang S, Lopez-Beltran A, et al. Telomere shortening and chromosomal abnormalities in intestinal metaplasia of the urinary bladder. Clin Cancer Res. 2007; 13:6232–6.

5. Srivastava S, Yan B, Chin SY, Muliana T, Salto-Tellez M, Teh M. Nuclear p53 expression is associated with allelic imbalance (TP53) in glandular dysplasia and typical cystitis glandularis: a LCM-based molecular analysis. Clin Genitourin Cancer. 2012; 10:57–9.

6. Bryan RT, Nicholls JH, Harrison RF, Jankowski JA, Wallace DM. The role of beta-catenin signaling in the malignant potential of cystitis glandularis. J Urol. 2003; 170:1892–6.

7. Corica FA, Husmann DA, Churchill BM, et al. Intestinal metaplasia is not a strong risk factor for bladder cancer: study of 53 cases with long-term follow-up. Urology. 1997; 50:427–31.

8. Smith AK, Hansel DE, Jones JS. Role of cystitis cystica et glandularis and intestinal metaplasia in development of bladder carcinoma. Urology. 2008; 71:915–8.

9. WHO Classification of Tumours Editorial Board. Urinary and male genital tumours. Lyon: International Agency for Research on Cancer;2022. p. 131–92.

10. Wisniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009; 6:359–62.

11. Han D, Moon S, Kim Y, Kim J, Jin J, Kim Y. In-depth proteomic analysis of mouse microglia using a combination of FASP and StageTip-based, high pH, reversed-phase fractionation. Proteomics. 2013; 13:2984–8.

12. Jung M, Lee C, Han D, et al. Proteomic-based machine learning analysis reveals PYGB as a novel immunohistochemical biomarker to distinguish inverted urothelial papilloma from low-grade papillary urothelial carcinoma with inverted growth. Front Oncol. 2022; 12:841398.

13. Kim B, Jung M, Moon KC, et al. Quantitative proteomics identifies TUBB6 as a biomarker of muscle-invasion and poor prognosis in bladder cancer. Int J Cancer. 2022. Sep. 10. [Epub]. https://doi.org/10.1002/ijc.34265 .

14. Tyanova S, Temu T, Cox J. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc. 2016; 11:2301–19.

15. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011; 10:1794–805.

16. Schwanhausser B, Busse D, Li N, et al. Global quantification of mammalian gene expression control. Nature. 2011; 473:337–42.

17. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019; 47:D607–13.

18. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009; 37:W305–311.

19. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015; 1:417–25.

20. Supek F, Bosnjak M, Skunca N, Smuc T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One. 2011; 6:e21800.

21. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003; 13:2498–504.

22. Mendes F, Pereira E, Martins D, et al. Oxidative stress in bladder cancer: an ally or an enemy? Mol Biol Rep. 2021; 48:2791–802.

23. Herrmann PC, Herrmann EC. Oxygen metabolism and a potential role for cytochrome c oxidase in the Warburg effect. J Bioenerg Biomembr. 2007; 39:247–50.

24. Talior I, Tennenbaum T, Kuroki T, Eldar-Finkelman H. PKC-delta-dependent activation of oxidative stress in adipocytes of obese and insulin-resistant mice: role for NADPH oxidase. Am J Physiol Endocrinol Metab. 2005; 288:E405–11.

25. Bywater MJ, Pearson RB, McArthur GA, Hannan RD. Dysregulation of the basal RNA polymerase transcription apparatus in cancer. Nat Rev Cancer. 2013; 13:299–314.

26. Yuniati L, Lauriola A, Gerritsen M, et al. Ubiquitylation of the ER-shaping protein lunapark via the CRL3 (KLHL12) ubiquitin ligase complex. Cell Rep. 2020; 31:107664.

27. Chukkapalli S, Amessou M, Dekhil H, et al. Ehd3, a regulator of vesicular trafficking, is silenced in gliomas and functions as a tumor suppressor by controlling cell cycle arrest and apoptosis. Carcinogenesis. 2014; 35:877–85.

28. Nakamura N, Yamamoto A, Wada Y, Futai M. Syntaxin 7 mediates endocytic trafficking to late endosomes. J Biol Chem. 2000; 275:6523–9.

29. Wang G, Li Y, Li J, et al. microRNA-199a-5p suppresses glioma progression by inhibiting MAGT1. J Cell Biochem. 2019; 120:15248–54.

30. Moamer A, Hachim IY, Binothman N, Wang N, Lebrun JJ, Ali S. A role for kinesin-1 subunits KIF5B/KLC1 in regulating epithelial mesenchymal plasticity in breast tumorigenesis. EBioMedicine. 2019; 45:92–107.

31. Sharma A, Almasan A. USP14 regulates DNA damage response and is a target for radiosensitization in non-small cell lung cancer. Int J Mol Sci. 2020; 21:6383.

32. Wang L, Li Z, Sievert D, et al. Loss of NARS1 impairs progenitor proliferation in cortical brain organoids and leads to microcephaly. Nat Commun. 2020; 11:4038.

33. Bosch R, Philips N, Suarez-Perez JA, et al. Mechanisms of photoaging and cutaneous photocarcinogenesis, and photoprotective strategies with phytochemicals. Antioxidants (Basel). 2015; 4:248–68.

34. Tikoo A, Shakri R, Connolly L, et al. Treatment of ras-induced cancers by the F-actin-bundling drug MKT-077. Cancer J. 2000; 6:162–8.

35. O’Farrell NJ, Phelan JJ, Feighery R, et al. Differential expression profiles of oxidative stress levels, 8-oxo-dG and 4-HNE, in Barrett’s esophagus compared to esophageal adenocarcinoma. Int J Mol Sci. 2019; 20:4449.

36. Wigner P, Grebowski R, Bijak M, Saluk-Bijak J, Szemraj J. The interplay between oxidative stress, inflammation and angiogenesis in bladder cancer development. Int J Mol Sci. 2021; 22:4483.

37. Garg R, Benedetti LG, Abera MB, Wang H, Abba M, Kazanietz MG. Protein kinase C and cancer: what we know and what we do not. Oncogene. 2014; 33:5225–37.

38. Sanchez-Carbayo M, Socci ND, Lozano J, Saint F, Cordon-Cardo C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J Clin Oncol. 2006; 24:778–89.