Home Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer
Article
Licensed
Unlicensed Requires Authentication

Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer

  • Yinling Wang , Jun Zheng , Yang Liu , Dongqi Li , Danning Jin and Hong Luan EMAIL logo
Published/Copyright: September 23, 2024
Become an author with De Gruyter Brill
Submit Manuscript Author Information
Clinical Chemistry and Laboratory Medicine (CCLM)
From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 63 Issue 2

Abstract

Objectives

Atypical cells (Atyp.C), as a new parameter determined by an automated urine analyzer, can be suspected of being malignant tumor cells. We evaluated the extent to which the Atyp.C can predict the existence of malignant tumor cells.

Methods

A total of 3,315 patients (1,751 in the training cohort and 1,564 in the testing cohort) were recruited and divided into five groups, namely, primary bladder cancer (BCa), recurrent BCa, post-treatment monitoring of BCa, other urological tumors, and controls. Urine Atyp. C, bacteria, white blood cell, and red blood cell were measured by a Sysmex UF-5000 analyzer. We compared the Atyp.C values across the different groups, sexes, and tumor stages. The diagnostic performance of Atyp.C alone and in combination with other parameters for detecting BCa was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

The Atyp.C value of the primary BCa group was significantly higher than that in the other groups, except recurrent BCa group. The Atyp.C value was closely related to tumor staging. Atyp.C combined with bacteria had the highest diagnostic performance for primary BCa [training cohort AUC: 0.781 (95 % CI: 0.761–0.801); testing cohort AUC: 0.826 (95 % CI: 0.806–0.845)]. The AUC value of diagnosed recurrent BCa by Atyp.C plus bacteria for the training cohort was 0.784 (95 % CI: 0.762–0.804).

Conclusions

Atyp.C was high in primary BCa patients and the combination of bacteria and Atyp.C showed high predictive value for primary BCa, suggesting that Atyp.C may be a useful objective indicator for the early detection of BCa.

Keywords: bladder cancer; atypical cell; bacteria; UF-5000; urinalysis
Corresponding author: Hong Luan, National Clinical Research Center for Laboratory Medicine, Department of Laboratory Medicine, The First Hospital of China Medical University, No. 155, Nanjingbei Street, Heping District, 110001, Shenyang, Liaoning, P.R. China; and Research Unit of Medical Laboratory, Chinese Academy of Medical Sciences, Shenyang, P.R. China, E-mail:
Yinling Wang, Jun Zheng and Yang Liu contributed equally to this work.

Funding source: CAMS Innovation Fund for Medical Sciences

Award Identifier / Grant number: 2019-I2M-5-027

  1. Research ethics: This study was reviewed and approved by the Ethics Committee of the First Hospital of China Medical University (Reference No. 2024–121).

  2. Informed consent: Not applicable.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.Hong Luan conceived the ideas. Yinling Wang, Jun Zheng, Yang Liu collected the samples, analyzed the data, and wrote the draft; Dongqi Li, Danning Jin, Hong Luan reviewed the manuscript. All authors have read and approved the final manuscript.

  4. Competing interests: The authors state no conflict of interest.

  5. Research funding: This study was supported by grants from CAMS Innovation Fund for Medical Sciences (2019-I2M-5–027).

  6. Data availability: The raw data can be obtained on request from the corresponding author.

References

1. Sung, H, Ferlay, J, Siegel, RL, Laversanne, M, Soerjomataram, I, Jemal, A, et al.. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Ca-Cancer J Clin 2021;71:209–49. https://doi.org/10.3322/caac.21660.Search in Google Scholar PubMed

2. Siegel, RL, Giaquinto, AN, Jemal, A. Cancer statistics, 2024. Ca-Cancer J Clin 2024;1:12–49. https://doi.org/10.3322/caac.21820.Search in Google Scholar PubMed

3. Babjuk, M, Burger, M, Capoun, O, Cohen, D, Compérat, EM, Escrig, JLD, et al.. European association of urology guidelines on non-muscle-invasive bladder cancer (Ta, T1, and carcinoma in situ). Eur Urol 2022;81:75–94. https://doi.org/10.1016/j.eururo.2021.08.010.Search in Google Scholar PubMed

4. Sylvester, RJ, van der Meijden, APM, Oosterlinck, W, Witjes, JA, Bouffioux, C, Denis, L, et al.. Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: a combined analysis of 2,596 patients from seven EORTC trials. Eur Urol 2006;49:466–77. https://doi.org/10.1016/j.eururo.2005.12.031.Search in Google Scholar PubMed

5. Teoh, JYC, Kamat, AM, Black, PC, Grivas, P, Shariat, SF, Babjuk, M. Recurrence mechanisms of non-muscle-invasive bladder cancer – a clinical perspective. Nat Rev Urol 2022;19:280–94. https://doi.org/10.1038/s41585-022-00578-1.Search in Google Scholar PubMed

6. Lenis, AT, Lec, PM, Chamie, K, Mshs, MD. Bladder cancer: a review. JAMA 2020;324:1980–91. https://doi.org/10.1001/jama.2020.17598.Search in Google Scholar PubMed

7. Ritch, CR, Velasquez, MC, Kwon, D, Becerra, MF, Soodana-Prakash, N, Atluri, VS, et al.. Use and validation of the AUA/SUO risk grouping for nonmuscle invasive bladder cancer in a contemporary cohort. J Urol 2020;203:505–11. https://doi.org/10.1097/ju.0000000000000593.Search in Google Scholar PubMed

8. Compérat, E, Varinot, J, Moroch, J, Eymerit-Morin, C, Brimo, F. A practical guide to bladder cancer pathology. Nat Rev Urol 2018;15:143–54. https://doi.org/10.1038/nrurol.2018.2.Search in Google Scholar PubMed

9. Yafi, FA, Brimo, F, Steinberg, J, Aprikian, AG, Tanguay, S, Kassouf, W. Prospective analysis of sensitivity and specificity of urinary cytology and other urinary biomarkers for bladder cancer. Urol Oncol-Semin Ori 2015;33. https://doi.org/10.1016/j.urolonc.2014.06.008.Search in Google Scholar PubMed

10. Karakiewicz, PI, Benayoun, S, Zippe, C, Lüdecke, G, Boman, H, Sanchez-Carbayo, M, et al.. Institutional variability in the accuracy of urinary cytology for predicting recurrence of transitional cell carcinoma of the bladder. BJU Int 2006;97:997–1001. https://doi.org/10.1111/j.1464-410x.2006.06036.x.Search in Google Scholar

11. Schmitz-Drager, BJ, Droller, M, Lokeshwar, VB, Lotan, Y, Hudson, MA, van Rhijn, BW, et al.. Molecular markers for bladder cancer screening, early diagnosis, and surveillance: the WHO/ICUD consensus. Urol Int 2015;94:1–24. https://doi.org/10.1159/000369357.Search in Google Scholar PubMed

12. Monar, GVF, Reynolds, T, Gordon, M, Moon, D, Moon, C. Molecular markers for bladder cancer screening: an insight into bladder cancer and FDA-approved biomarkers. Int J Mol Sci 2023;24. https://doi.org/10.3390/ijms241814374.Search in Google Scholar PubMed PubMed Central

13. Liang, T, Oraa, SS, Rodríguez, NR, Bagade, T, Chao, J, Sinert, R. Predicting urinary tract infections with interval likelihood ratios. Pediatrics 2021;147. https://doi.org/10.1542/peds.2020-015008.Search in Google Scholar PubMed

14. Wang, H, Han, FF, Wen, JX, Yan, Z, Han, YQ, Hu, ZD, et al.. Accuracy of the Sysmex UF-5000 analyzer for urinary tract infection screening and pathogen classification. PLoS One 2023;18:e0281118. https://doi.org/10.1371/journal.pone.0281118.Search in Google Scholar PubMed PubMed Central

15. Cho, H, Yoo, J, Kim, H, Jang, H, Kim, Y, Chae, H. Diagnostic characteristics of urinary red blood cell distribution incorporated in UF-5000 for differentiation of glomerular and non-glomerular hematuria. Ann Lab Med 2022;42:160–8. https://doi.org/10.3343/alm.2022.42.2.160.Search in Google Scholar PubMed PubMed Central

16. Saha, MK, Massicotte-Azarniouch, D, Reynolds, ML, Mottl, AK, Falk, RJ, Jennette, JC, et al.. Glomerular hematuria and the utility of urine microscopy: a review. Am J Kidney Dis 2022;80:383–92. https://doi.org/10.1053/j.ajkd.2022.02.022.Search in Google Scholar PubMed

17. Aydin, O. Atypical cells parameter in Sysmex UN automated urine analyzer: feedback from the field. Diagn Pathol 2021;16. https://doi.org/10.1186/s13000-021-01068-5.Search in Google Scholar PubMed PubMed Central

18. Karaburun, MC, Özkaya, MF, Ergüder, BI, Süer, E. Investigation of atypical cell parameter in the surveillance of patients with NMIBC; initial outcomes of a single center prospective study. J Med Syst 2023;47. https://doi.org/10.1007/s10916-023-01929-0.Search in Google Scholar PubMed

19. Ren, CY, Wang, X, Yang, CW, Li, SC, Liu, SC, Cao, HC. Investigation of Atyp.C using UF-5000 flow cytometer in patients with a suspected diagnosis of urothelial carcinoma: a single-center study. Diagn Pathol 2020;15. https://doi.org/10.1186/s13000-020-00993-1.Search in Google Scholar PubMed PubMed Central

20. Ahmadi, H, Duddalwar, V, Daneshmand, S. Diagnosis and staging of bladder cancer. Hematol Oncol Clin North Am 2021;35:531–41. https://doi.org/10.1016/j.hoc.2021.02.004.Search in Google Scholar PubMed

21. van Rhijn, BWG, Hentschel, AE, Brundl, J, Comperat, EM, Hernandez, V, Capoun, O, et al.. Prognostic value of the WHO1973 and WHO2004/2016 classification systems for grade in primary Ta/T1 non-muscle-invasive bladder cancer: a multicenter European association of urology non-muscle-invasive bladder cancer guidelines panel study. Eur Urol Oncol 2021;4:182–91. https://doi.org/10.1016/j.euo.2020.12.002.Search in Google Scholar PubMed

22. Barkan, GA, Wojcik, EM, Nayar, R, Savic-Prince, S, Quek, ML, Kurtycz, DFI, et al.. The Paris System for reporting urinary cytology: the quest to develop a standardized terminology. Adv Anat Pathol 2016;23:193–201. https://doi.org/10.1097/pap.0000000000000118.Search in Google Scholar PubMed

23. Bhat, A, Kwon, D, Soodana-Prakash, N, Mouzannar, A, Punnen, S, Gonzalgo, ML, et al.. Surveillance intensity in intermediate risk, nonmuscle invasive bladder cancer: revisiting the optimal timing and frequency of cystoscopy. J Urol 2021;206:22–8. https://doi.org/10.1097/ju.0000000000001689.Search in Google Scholar

24. Lotan, Y, Baky, FJ. Urine-based markers for detection of urothelial cancer and for the management of non-muscle-invasive bladder cancer. Urol Clin North Am 2023;50:53–67. https://doi.org/10.1016/j.ucl.2022.09.009.Search in Google Scholar PubMed

25. Chou, R, Gore, JL, Buckley, D, Fu, R, Gustafson, K, Griffin, JC, et al.. Urinary biomarkers for diagnosis of bladder cancer: a systematic review and meta-analysis. Ann Intern Med 2015;163:922–31. https://doi.org/10.7326/m15-0997.Search in Google Scholar PubMed

26. Soorojebally, Y, Neuzillet, Y, Roumiguie, M, Lamy, PJ, Allory, Y, Descotes, F, et al.. Urinary biomarkers for bladder cancer diagnosis and NMIBC follow-up: a systematic review. World J Urol 2023;41:345–59. https://doi.org/10.1007/s00345-022-04253-3.Search in Google Scholar PubMed

27. Chen, X, Zhang, J, Ruan, W, Huang, M, Wang, C, Wang, H, et al.. Urine DNA methylation assay enables early detection and recurrence monitoring for bladder cancer. J Clin Invest 2020;130:6278–89. https://doi.org/10.1172/jci139597.Search in Google Scholar PubMed PubMed Central

28. Yahyazadeh, R, Bashash, D, Ghaffari, P, Kord, S, Safaroghli-Azar, A, Ghaffari, SH. Evaluation of hTERT, KRT7, and survivin in urine for noninvasive detection of bladder cancer using real-time PCR. BMC Urol 2021;21:64. https://doi.org/10.1186/s12894-021-00838-z.Search in Google Scholar PubMed PubMed Central

29. Zhang, T, Zhu, JH, Li, ZX, Zhao, Y, Li, Y, Li, J, et al.. The UF-5000 Atyp.C parameter is an independent risk factor for bladder cancer. Sci Rep-Uk 2024;14. https://doi.org/10.1038/s41598-024-63572-0.Search in Google Scholar PubMed PubMed Central

30. Shang, D, Liu, Y, Xu, X, Chen, Z, Wang, D. Diagnostic value comparison of CellDetect, fluorescent in situ hybridization (FISH), and cytology in urothelial carcinoma. Cancer Cell Int 2021;21:465. https://doi.org/10.1186/s12935-021-02169-3.Search in Google Scholar PubMed PubMed Central

31. Haugum, K, Haugan, MS, Skage, J, Tetik, M, Jakovljev, A, Nilsen, HJS, et al.. Use of Sysmex UF-5000 flow cytometry in rapid diagnosis of urinary tract infection and the importance of validating carryover rates against bacterial count cut-off. J Med Microbiol 2021;70. https://doi.org/10.1099/jmm.0.001472.Search in Google Scholar PubMed PubMed Central

Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/cclm-2024-0650).

Received: 2024-05-29
Accepted: 2024-08-11
Published Online: 2024-09-23
Published in Print: 2025-01-29

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. CD34+ progenitor cells meet metrology
  4. Reviews
  5. Venous blood collection systems using evacuated tubes: a systematic review focusing on safety, efficacy and economic implications of integrated vs. combined systems
  6. The correlation between serum angiopoietin-2 levels and acute kidney injury (AKI): a meta-analysis
  7. Opinion Papers
  8. Advancing value-based laboratory medicine
  9. Clostebol and sport: about controversies involving contamination vs. doping offence
  10. Direct-to-consumer testing as consumer initiated testing: compromises to the testing process and opportunities for quality improvement
  11. Perspectives
  12. An improved implementation of metrological traceability concepts is needed to benefit from standardization of laboratory results
  13. Genetics and Molecular Diagnostics
  14. Comparative analysis of BCR::ABL1 p210 mRNA transcript quantification and ratio to ABL1 control gene converted to the International Scale by chip digital PCR and droplet digital PCR for monitoring patients with chronic myeloid leukemia
  15. General Clinical Chemistry and Laboratory Medicine
  16. IVDCheckR – simplifying documentation for laboratory developed tests according to IVDR requirements by introducing a new digital tool
  17. Analytical performance specifications for trace elements in biological fluids derived from six countries federated external quality assessment schemes over 10 years
  18. The effects of drone transportation on routine laboratory, immunohematology, flow cytometry and molecular analyses
  19. Accurate non-ceruloplasmin bound copper: a new biomarker for the assessment and monitoring of Wilson disease patients using HPLC coupled to ICP-MS/MS
  20. Construction of platelet count-optical method reflex test rules using Micro-RBC#, Macro-RBC%, “PLT clumps?” flag, and “PLT abnormal histogram” flag on the Mindray BC-6800plus hematology analyzer in clinical practice
  21. Evaluation of serum NFL, T-tau, p-tau181, p-tau217, Aβ40 and Aβ42 for the diagnosis of neurodegenerative diseases
  22. An immuno-DOT diagnostic assay for autoimmune nodopathy
  23. Evaluation of biochemical algorithms to screen dysbetalipoproteinemia in ε2ε2 and rare APOE variants carriers
  24. Reference Values and Biological Variations
  25. Allowable total error in CD34 cell analysis by flow cytometry based on state of the art using Spanish EQAS data
  26. Clinical utility of personalized reference intervals for CEA in the early detection of oncologic disease
  27. Agreement of lymphocyte subsets detection permits reference intervals transference between flow cytometry systems: direct validation using established reference intervals
  28. Cancer Diagnostics
  29. Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer
  30. External quality assessment-based tumor marker harmonization simulation; insights in achievable harmonization for CA 15-3 and CEA
  31. Cardiovascular Diseases
  32. Evaluation of the analytical and clinical performance of a high-sensitivity troponin I point-of-care assay in the Mersey Acute Coronary Syndrome Rule Out Study (MACROS-2)
  33. Analytical verification of the Atellica VTLi point of care high sensitivity troponin I assay
  34. Infectious Diseases
  35. Synovial fluid D-lactate – a pathogen-specific biomarker for septic arthritis: a prospective multicenter study
  36. Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19: a retrospective study
  37. Letters to the Editor
  38. Generative artificial intelligence (AI) for reporting the performance of laboratory biomarkers: not ready for prime time
  39. Urgent need to adopt age-specific TSH upper reference limit for the elderly – a position statement of the Belgian thyroid club
  40. Sigma metric is more correlated with analytical imprecision than bias
  41. Utility and limitations of monitoring kidney transplants using capillary sampling
  42. Simple flow cytometry method using a myeloma panel that easily reveals clonal proliferation of mature B-cells
  43. Is sweat conductivity still a relevant screening test for cystic fibrosis? Participation over 10 years
  44. Hb D-Iran interference on HbA1c measurement
https://doi.org/10.1515/cclm-2024-0650
Keywords for this article
bladder cancer; atypical cell; bacteria; UF-5000; urinalysis

Articles in the same Issue

  1. Frontmatter
  2. Editorial
  3. CD34+ progenitor cells meet metrology
  4. Reviews
  5. Venous blood collection systems using evacuated tubes: a systematic review focusing on safety, efficacy and economic implications of integrated vs. combined systems
  6. The correlation between serum angiopoietin-2 levels and acute kidney injury (AKI): a meta-analysis
  7. Opinion Papers
  8. Advancing value-based laboratory medicine
  9. Clostebol and sport: about controversies involving contamination vs. doping offence
  10. Direct-to-consumer testing as consumer initiated testing: compromises to the testing process and opportunities for quality improvement
  11. Perspectives
  12. An improved implementation of metrological traceability concepts is needed to benefit from standardization of laboratory results
  13. Genetics and Molecular Diagnostics
  14. Comparative analysis of BCR::ABL1 p210 mRNA transcript quantification and ratio to ABL1 control gene converted to the International Scale by chip digital PCR and droplet digital PCR for monitoring patients with chronic myeloid leukemia
  15. General Clinical Chemistry and Laboratory Medicine
  16. IVDCheckR – simplifying documentation for laboratory developed tests according to IVDR requirements by introducing a new digital tool
  17. Analytical performance specifications for trace elements in biological fluids derived from six countries federated external quality assessment schemes over 10 years
  18. The effects of drone transportation on routine laboratory, immunohematology, flow cytometry and molecular analyses
  19. Accurate non-ceruloplasmin bound copper: a new biomarker for the assessment and monitoring of Wilson disease patients using HPLC coupled to ICP-MS/MS
  20. Construction of platelet count-optical method reflex test rules using Micro-RBC#, Macro-RBC%, “PLT clumps?” flag, and “PLT abnormal histogram” flag on the Mindray BC-6800plus hematology analyzer in clinical practice
  21. Evaluation of serum NFL, T-tau, p-tau181, p-tau217, Aβ40 and Aβ42 for the diagnosis of neurodegenerative diseases
  22. An immuno-DOT diagnostic assay for autoimmune nodopathy
  23. Evaluation of biochemical algorithms to screen dysbetalipoproteinemia in ε2ε2 and rare APOE variants carriers
  24. Reference Values and Biological Variations
  25. Allowable total error in CD34 cell analysis by flow cytometry based on state of the art using Spanish EQAS data
  26. Clinical utility of personalized reference intervals for CEA in the early detection of oncologic disease
  27. Agreement of lymphocyte subsets detection permits reference intervals transference between flow cytometry systems: direct validation using established reference intervals
  28. Cancer Diagnostics
  29. Atypical cells in urine sediment: a novel biomarker for early detection of bladder cancer
  30. External quality assessment-based tumor marker harmonization simulation; insights in achievable harmonization for CA 15-3 and CEA
  31. Cardiovascular Diseases
  32. Evaluation of the analytical and clinical performance of a high-sensitivity troponin I point-of-care assay in the Mersey Acute Coronary Syndrome Rule Out Study (MACROS-2)
  33. Analytical verification of the Atellica VTLi point of care high sensitivity troponin I assay
  34. Infectious Diseases
  35. Synovial fluid D-lactate – a pathogen-specific biomarker for septic arthritis: a prospective multicenter study
  36. Targeted MRM-analysis of plasma proteins in frozen whole blood samples from patients with COVID-19: a retrospective study
  37. Letters to the Editor
  38. Generative artificial intelligence (AI) for reporting the performance of laboratory biomarkers: not ready for prime time
  39. Urgent need to adopt age-specific TSH upper reference limit for the elderly – a position statement of the Belgian thyroid club
  40. Sigma metric is more correlated with analytical imprecision than bias
  41. Utility and limitations of monitoring kidney transplants using capillary sampling
  42. Simple flow cytometry method using a myeloma panel that easily reveals clonal proliferation of mature B-cells
  43. Is sweat conductivity still a relevant screening test for cystic fibrosis? Participation over 10 years
  44. Hb D-Iran interference on HbA1c measurement
Sign up now to receive a 20% welcome discount
Institutional Access
How does access work?
Have an idea on how to improve our website?
Please write us.
© 2025 De Gruyter Brill
Downloaded on 10.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/cclm-2024-0650/html
Scroll to top button