Skip to main content

Cell-Free DNA, MicroRNAs, Proteins, and Peptides as Liquid Biopsy Biomarkers in Prostate Cancer and Bladder Cancer

  • Protocol
  • First Online:
Liquid Biopsies

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2695))

Abstract

Liquid biopsy, as a novel noninvasive tool for biomarker discovery, has gained a lot of attention and represents a significant innovation in precision medicine. Due to its minimally invasive nature, liquid biopsy has fewer complications and can be scheduled more frequently to provide individualized snapshots of the disease at successive time points. This is particularly valuable in providing simultaneous measurements of tumor burden during treatment and early detection of tumor recurrence or drug resistance. Blood-based liquid biopsy is an attractive, minimally invasive alternative, which has shown promise in diagnosis, risk stratification, disease monitoring, and more. Urine has gained popularity due to its less invasive sampling, the ability to easily repeat samples, and the ability to track tumor evolution in real time, making it a powerful tool for diagnosis and treatment monitoring, especially in urologic cancers. In this review, we provide a detailed discussion on the potential clinical applications of prostate cancer (PCa) and bladder cancer (BCa), with cell-free DNA (cfDNA), microRNAs (miRNAs), proteins, and peptides as liquid biopsy biomarkers.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Mandel P, Metais P (1948) Les acides nucléiques du plasma sanguin chez l’homme [Nuclear Acids In Human Blood Plasma]. C R Seances Soc Biol Fil 142(3–4):241–243

    CAS  PubMed  Google Scholar 

  2. Chan KCA et al (2013) Cancer genome scanning in plasma: detection of tumour-associated copy number aberrations, single-nucleotide variants, and tumoral heterogeneity by massively parallel sequencing. Clin Chem 59(1):211–224. https://doi.org/10.1373/clinchem.2012.196014

    Article  CAS  PubMed  Google Scholar 

  3. Pisetsky David S, Anna-Marie F (2007) The origin of extracellular DNA during the clearance of dead and dying cells. Autoimmunity 40(4). https://doi.org/10.1080/08916930701358826

  4. Schwarzenbach H et al (2011) Cell-free nucleic acids as biomarkers in cancer patients. Nat Rev Cancer 11(6):426–437. https://doi.org/10.1038/nrc3066

    Article  CAS  PubMed  Google Scholar 

  5. Szilágyi M et al (2020) Circulating cell-free nucleic acids: main characteristics and clinical application. Int J Mol Sci 21(18):6827. https://doi.org/10.3390/ijms21186827

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Viorritto Irene CB et al (2007) Autoimmunity versus tolerance: can dying cells tip the balance? Clin Immunol 122(2). https://doi.org/10.1016/j.clim.2006.07.012

  7. Suzuki N et al (2008) Characterization of circulating DNA in healthy human plasma. Clin Chim Acta 387:1–2. https://doi.org/10.1016/j.cca.2007.09.001

    Article  CAS  Google Scholar 

  8. Stroun M et al (1987) Isolation and characterization of DNA from the plasma of cancer patients. Eur J Cancer Clin Oncol 23(6):707–712. https://doi.org/10.1016/0277-5379(87)90266-5

    Article  CAS  PubMed  Google Scholar 

  9. Meyerson M, Pellman D (2011) Cancer genomes evolve by pulverizing single chromosomes. Cell 144(1). https://doi.org/10.1016/j.cell.2010.12.025

  10. Thierry Alain R et al (2010) Origin and quantification of circulating DNA in mice with human colorectal cancer xenografts. Nucleic Acids Res 38(18). https://doi.org/10.1093/nar/gkq421

  11. Julia B et al (2009) Profile of the circulating DNA in apparently healthy individuals. Clin Chem 55(4). https://doi.org/10.1373/clinchem.2008.113597

  12. Taback B et al (2004) Quantification of circulating DNA in the plasma and serum of cancer patients. Ann N Y Acad Sci 1022:17–24. https://doi.org/10.1196/annals.1318.004

    Article  CAS  PubMed  Google Scholar 

  13. Philippe A et al (2003) Circulating nucleic acids in plasma and serum as a noninvasive investigation for cancer: time for large-scale clinical studies? Int J Cancer 103(2). https://doi.org/10.1002/ijc.10791

  14. Jahr S et al (2001) DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res 61(4):1659–1665

    CAS  PubMed  Google Scholar 

  15. Allen CKC et al (2013) Cancer genome scanning in plasma: detection of tumour-associated copy number aberrations, single-nucleotide variants, and tumoral heterogeneity by massively parallel sequencing. Clin Chem 59(1). https://doi.org/10.1373/clinchem.2012.196014

  16. Muhammed M et al (2013) Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497(7447). https://doi.org/10.1038/nature12065

  17. Newman Aaron M et al (2014) An ultrasensitive method for quantitating circulating tumour DNA with broad patient coverage. Nat Med 20(5). https://doi.org/10.1038/nm.3519

  18. Chun FK-H et al (2006) Circulating tumour-associated plasma DNA represents an independent and informative predictor of prostate cancer. BJU Int 98(3):544–548. https://doi.org/10.1111/j.1464-410X.2006.06352.x

    Article  CAS  PubMed  Google Scholar 

  19. Schütz E et al (2015) Chromosomal instability in cell-free DNA is a serum biomarker for prostate cancer. Clin Chem 61(1):239–248. https://doi.org/10.1373/clinchem.2014.226571

    Article  CAS  PubMed  Google Scholar 

  20. Fleischhacker M, Schmidt B (2006) Circulating nucleic acids (CNAs) and cancer – a survey. BBA Rev Cancer 1775(1). https://doi.org/10.1016/j.bbcan.2006.10.001

  21. Clark SJ, Melki J (2002) DNA methylation and gene silencing in cancer: which is the guilty party? Oncogene 21(35):5380–5387. https://doi.org/10.1038/sj.onc.1205598

    Article  CAS  PubMed  Google Scholar 

  22. Wang Y et al (2016) An epigenetic biomarker combination of PCDH17 and POU4F2 detects bladder cancer accurately by methylation analyses of urine sediment DNA in Han Chinese. Oncotarget 7(3):2754–2764. https://doi.org/10.18632/oncotarget.6666

    Article  PubMed  Google Scholar 

  23. Chung W et al (2011) Detection of bladder cancer using novel DNA methylation biomarkers in urine sediments. Cancer Epidemiol Biomarkers Prev 20(7):1483–1491. https://doi.org/10.1158/1055-9965.EPI-11-0067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Egger G et al (2004) Epigenetics in human disease and prospects for epigenetic therapy. Nature 429(6990):457–463. https://doi.org/10.1038/nature02625

    Article  CAS  PubMed  Google Scholar 

  25. Sanii S et al (2012) Expression of matrix metalloproteinase-2, but not caspase-3, facilitates distinction between benign and malignant thyroid follicular neoplasms. Asian Pac J Cancer Prev 13(5):2175–2178. https://doi.org/10.7314/apjcp.2012.13.5.2175

    Article  PubMed  Google Scholar 

  26. Lehmann-Werman R et al (2016) Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci U S A 113(13):E1826–E1834. https://doi.org/10.1073/pnas.1519286113

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Aryee MJ et al (2013) DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases. Sci Trans Med 5(169). https://doi.org/10.1126/scitranslmed.3005211

  28. Derks S et al (2004) Methylation-specific PCR unraveled. Cell Oncol Off J Int Soc Cell Oncol 26(5–6):291–299. https://doi.org/10.1155/2004/370301

    Article  CAS  Google Scholar 

  29. Annalisa A et al (2008) Am J Clin Pathol 129(5). https://doi.org/10.1309/DBPX1MFNDDJBW1FL

  30. Abdollah F et al (2013) Eur Urol 64(4). https://doi.org/10.1016/j.eururo.2013.03.006

  31. Emma J et al (2013) PloS one 8(11). https://doi.org/10.1371/journal.pone.0077407

  32. Hoon KG et al (2008) DNA methylation profiles of gastric carcinoma characterized by quantitative DNA methylation analysis. Lab Investig J Tech Method Pathol 88(2). https://doi.org/10.1038/labinvest.3700707

  33. Renard I et al (2010) Identification and validation of the methylated TWIST1 and NID2 genes through real-time methylation-specific polymerase chain reaction assays for the noninvasive detection of primary bladder cancer in urine samples. Eur Urol 58(1):96–104. https://doi.org/10.1016/j.eururo.2009.07.041

    Article  CAS  PubMed  Google Scholar 

  34. Thomas R et al (2011) Comprehensive genome methylation analysis in bladder cancer: identification and validation of novel methylated genes and application of these as urinary tumour markers. Clin Cancer Res Off J Am Assoc Cancer Res 17(17). https://doi.org/10.1158/1078-0432.CCR-10-2659

  35. Allory Y et al (2014) Telomerase reverse transcriptase promoter mutations in bladder cancer: high frequency across stages, detection in urine, and lack of association with outcome. Eur Urol 65(2). https://doi.org/10.1016/j.eururo.2013.08.052

  36. Kompier LC et al (2017) FGFR3, HRAS, KRAS, NRAS and PIK3CA mutations in bladder cancer and their potential as biomarkers for surveillance and therapy. PLoS ONE 5(11). https://doi.org/10.1371/journal.pone.0013821

  37. Zuiverloon TCM et al (2011) Optimization of nonmuscle invasive bladder cancer recurrence detection using a urine based FGFR3 mutation assay. J Urol 186(2). https://doi.org/10.1016/j.juro.2011.03.141

  38. Kinde I et al (2013) TERT promoter mutations occur early in urothelial neoplasia and are biomarkers of early disease and disease recurrence in urine. Cancer Res 73(24):7162–7167. https://doi.org/10.1158/0008-5472.CAN-13-2498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Vandekerkhove G et al (2017) Circulating tumour DNA reveals clinically actionable somatic genome of metastatic bladder cancer. Clin Cancer Res Off J Am Assoc Cancer Res 23(21):6487–6497. https://doi.org/10.1158/1078-0432.CCR-17-1140

    Article  CAS  Google Scholar 

  40. Grivas P et al (2020) Circulating tumour DNA alterations in advanced urothelial carcinoma and association with clinical outcomes: a pilot study. Eur Urol Oncol 3(5):695–699. https://doi.org/10.1016/j.euo.2019.02.004

    Article  PubMed  Google Scholar 

  41. Romanov V et al (2020) Liquid biopsy analysis of FGFR3, TERT promoter and STAG2 hotspot mutations for disease surveillance in bladder cancer. Clin Oncol Res. https://doi.org/10.31487/j.cor.2020.02.11

  42. Groskopf J et al (2006) APTIMA PCA3 molecular urine test: development of a method to aid in the diagnosis of prostate cancer. Clin Chem 52(6):1089–1095. https://doi.org/10.1373/clinchem.2005.063289

    Article  CAS  PubMed  Google Scholar 

  43. Whitman EJ et al (2008) PCA3 score before radical prostatectomy predicts extracapsular extension and tumor volume. J Urol 180(5):1975–1978. https://doi.org/10.1016/j.juro.2008.07.060

    Article  PubMed  Google Scholar 

  44. Rothé F et al (2014) Plasma circulating tumour DNA as an alternative to metastatic biopsies for mutational analysis in breast cancer. Ann Oncol Off J Eur Soc Med Oncol 25(10):1959–1965. https://doi.org/10.1093/annonc/mdu288

    Article  Google Scholar 

  45. Lebofsky R et al (2015) Circulating tumour DNA as a non-invasive substitute to metastasis biopsy for tumour genotyping and personalized medicine in a prospective trial across all tumour types. Mol Oncol 9(4):783–790. https://doi.org/10.1016/j.molonc.2014.12.003

    Article  CAS  PubMed  Google Scholar 

  46. Liu H et al (2019) Identification of non-invasive biomarkers for chronic atrophic gastritis from serum exosomal microRNAs. BMC Cancer 19(1):129. https://doi.org/10.1186/s12885-019-5328-7

    Article  PubMed  PubMed Central  Google Scholar 

  47. O’Leary B et al (2018) The genetic landscape and clonal evolution of breast cancer resistance to Palbociclib plus Fulvestrant in the PALOMA-3 trial. Cancer Discov 8(11):1390–1403. https://doi.org/10.1158/2159-8290.CD-18-0264

    Article  PubMed  PubMed Central  Google Scholar 

  48. O’Leary B et al (2018) Early circulating tumour DNA dynamics and clonal selection with palbociclib and fulvestrant for breast cancer. Nat Commun 9(1):896. https://doi.org/10.1038/s41467-018-03215-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Zheng D et al (2016) Plasma EGFR T790M ctDNA status is associated with clinical outcome in advanced NSCLC patients with acquired EGFR-TKI resistance. Sci Rep 6:20913. https://doi.org/10.1038/srep20913

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lianos GD et al (2015) Circulating tumour DNA: new horizons for improving cancer treatment. Future Oncol. 11(4):545–548. https://doi.org/10.2217/fon.14.250

    Article  CAS  PubMed  Google Scholar 

  51. Dmitry Z et al (2010) MicroRNA markers for forensic body fluid identification obtained from microarray screening and quantitative RT-PCR confirmation. Int J Legal Med 124(3). https://doi.org/10.1007/s00414-009-0402-3

  52. Zhaohui H et al (2010) Plasma microRNAs are promising novel biomarkers for early detection of colorectal cancer. Int J Cancer 127(1). https://doi.org/10.1002/ijc.25007

  53. Cortez MA et al (2011) MicroRNAs in body fluids – the mix of hormones and biomarkers. Nat Rev Clin Oncol 8(8):467–477. https://doi.org/10.1038/nrclinonc.2011.76

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Okada H et al (2010) MicroRNAs in immune regulation – opportunities for cancer immunotherapy. Int J Biochem Cell Biol 42(8). https://doi.org/10.1016/j.biocel.2010.02.002

  55. Hai-Liang Z et al (2011) Serum miRNA-21: elevated levels in patients with metastatic hormone-refractory prostate cancer and potential predictive factor for the efficacy of docetaxel-based chemotherapy. Prostate 71(3). https://doi.org/10.1002/pros.21246

  56. Shen J et al (2012) Dysregulation of circulating microRNAs and prediction of aggressive prostate cancer. Prostate 72(13). https://doi.org/10.1002/pros.22499

  57. Selth LA et al (2013) Circulating microRNAs predict biochemical recurrence in prostate cancer patients. Br J Cancer 109(3). https://doi.org/10.1038/bjc.2013.369

  58. Jiang X et al (2015) Serum microRNA expression signatures identified from genome-wide microRNA profiling serve as novel noninvasive biomarkers for diagnosis and recurrence of bladder cancer. Int J Cancer 136(4):854–862. https://doi.org/10.1002/ijc.29041

    Article  CAS  PubMed  Google Scholar 

  59. Sasaki H et al (2016) Expression level of urinary microRNA-146a-5p is increased in patients with bladder cancer and decreased in those after transurethral resection. Clin Genitourin Cancer 14(5). https://doi.org/10.1016/j.clgc.2016.04.002

  60. Qian S et al (2014) miR-146a functions as a tumour suppressor in prostate cancer by targeting Rac1. Prostate 74(16). https://doi.org/10.1002/pros.22878

  61. Andreu Z et al (2017) Extracellular vesicles as a source for non-invasive biomarkers in bladder cancer progression. Eur J Pharm Sci 98:70–79. https://doi.org/10.1016/j.ejps.2016.10.008

    Article  CAS  PubMed  Google Scholar 

  62. Hofbauer SL et al (2018) A urinary microRNA (miR) signature for diagnosis of bladder cancer. Urol Oncol 36(12):531.e1–531.e8. https://doi.org/10.1016/j.urolonc.2018.09.006

    Article  CAS  PubMed  Google Scholar 

  63. Lekchnov EA et al (2018) Searching for the novel specific predictors of prostate cancer in urine: the analysis of 84 miRNA expression. Int J Mol Sci 19(12):4088. https://doi.org/10.3390/ijms19124088

    Article  PubMed  PubMed Central  Google Scholar 

  64. Guelfi G et al (2018) Next generation sequencing of urine exfoliated cells: an approach of prostate cancer microRNAs research. Sci Rep 8(1):7111. https://doi.org/10.1038/s41598-018-24236-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Ashley DM et al (2016) Proteomics and peptidomics: moving toward precision medicine in urological malignancies. Oncotarget 7(32). https://doi.org/10.18632/oncotarget.8931

  66. Sigdel Tara K et al (2014) The identification of novel potential injury mechanisms and candidate biomarkers in renal allograft rejection by quantitative proteomics. Mol Cell Proteomics 13(2). https://doi.org/10.1074/mcp.M113.030577

  67. Frantzi M et al (2014) Discovery and validation of urinary biomarkers for detection of renal cell carcinoma. J Proteomics 98. https://doi.org/10.1016/j.jprot.2013.12.010

  68. Miquel BJ et al (2014) Peptidomics of urine and other biofluids for cancer diagnostics. Clin Chem 60(8). https://doi.org/10.1373/clinchem.2013.211714

  69. Harald M et al (2007) High-resolution proteome/peptidome analysis of peptides and low-molecular-weight proteins in urine. Proteomics Clin Appl 1(8). https://doi.org/10.1002/PRCA.200700043

  70. Ashley DM et al (2017) An integrated proteomic and peptidomic assessment of the normal human urinome. Clin Chem Lab Med 55(2). https://doi.org/10.1515/cclm-2016-0390

  71. Chen C-L et al (2013) Identification of potential bladder cancer markers in urine by abundant-protein depletion coupled with quantitative proteomics. J Proteomics 85:28–43. https://doi.org/10.1016/j.jprot.2013.04.024

    Article  CAS  PubMed  Google Scholar 

  72. Yi-Ting C et al (2010) Discovery of novel bladder cancer biomarkers by comparative urine proteomics using iTRAQ technology. J Proteome Res 9(11). https://doi.org/10.1021/pr100576x

  73. Flatley B et al (2014) MALDI MS profiling of post-DRE urine samples highlights the potential of β-microseminoprotein as a marker for prostatic diseases. Prostate 74(1):103–111. https://doi.org/10.1002/pros.22736

    Article  CAS  PubMed  Google Scholar 

  74. Kjølhede JD et al (2014) Quantitative proteomics of fractionated membrane and lumen exosome proteins from isogenic metastatic and nonmetastatic bladder cancer cells reveal differential expression of EMT factors. Proteomics 14(6). https://doi.org/10.1002/pmic.201300452

  75. Bansal N et al (2014) Low- and high-grade bladder cancer appraisal via serum-based proteomics approach. Clin Chim Acta 436. https://doi.org/10.1016/j.cca.2014.05.012

  76. Chang SS (2017) Re: SH3BGRL3 protein as a potential prognostic biomarker for urothelial carcinoma: a novel binding partner of epidermal growth factor receptor. J Urol 198(1). https://doi.org/10.1016/j.juro.2017.04.006

  77. Calistri D et al (2012) Urinary biomarkers of non-muscle-invasive bladder cancer: current status and future potential. Expert Rev Anticancer Ther 12(6):743–752. https://doi.org/10.1586/era.12.50

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Chen, H., Xu, C., Fang, Z., Mao, S. (2023). Cell-Free DNA, MicroRNAs, Proteins, and Peptides as Liquid Biopsy Biomarkers in Prostate Cancer and Bladder Cancer. In: Huang, T., Yang, J., Tian, G. (eds) Liquid Biopsies. Methods in Molecular Biology, vol 2695. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3346-5_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-3346-5_11

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-3345-8

  • Online ISBN: 978-1-0716-3346-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics