Urinary Biomarkers for Early Diagnosis of Lung Cancer
Abstract
:1. Introduction
2. The Role of Kidney Physiology in Oncological Practice
3. Materials and Methods
4. Results
5. Study Limitations
6. Future Perspectives
7. Summary
- -
- Urine is an appealing biological fluid in terms of ease and safety of collection, and quantity.
- -
- Renal filtration also results in a less complex matrix than that of blood, containing fewer factors known to interfere with biomarker assays.
- -
- So far, many urinary metabolites have been processed. However, they await validation.
- -
- Analytical methods have been reported for the detection of urinary biomarkers.
- -
- Technological strides in urine analytical methodology have resulted in enormous progress for basic research.
- -
- These methods could be standardized and integrated into a procedure for targeted metabolomics by clinical investigators. The resulting quantification of biomarkers would offer a formidable diagnostic tool for early-stage lung cancer.
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Knight, S.B.; Crosbie, P.A.; Balata, H.; Chudziak, J.; Hussell, T.; Dive, C. Progress and prospects of early detection in lung cancer. Open Biol. 2017, 7. [Google Scholar] [CrossRef] [Green Version]
- Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 2020, 70, 7–30. [Google Scholar] [CrossRef]
- The National Lung Screening Trial Research Team Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. N. Engl. J. Med. 2011, 365, 395–409. [CrossRef] [Green Version]
- Jaklitsch, M.T.; Jacobson, F.L.; Austin, J.H.M.; Field, J.K.; Jett, J.R.; Keshavjee, S.; MacMahon, H.; Mulshine, J.L.; Munden, R.F.; Salgia, R.; et al. The American Association for Thoracic Surgery guidelines for lung cancer screening using low-dose computed tomography scans for lung cancer survivors and other high-risk groups. J. Thorac. Cardiovasc. Surg. 2012, 144, 33–38. [Google Scholar] [CrossRef] [Green Version]
- Silva, M.; Milanese, G.; Pastorino, U.; Sverzellati, N. Lung cancer screening: Tell me more about post-test risk. J. Thorac. Dis. 2019, 11, 3681–3688. [Google Scholar] [CrossRef]
- Vander Heiden, M.; Cantley, L.; Thompson, C. Understanding the Warburg effect: The metabolic Requirements of cell proliferation. Science 2009, 324, 1029–1033. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hofman, P. Liquid biopsy for early detection of lung cancer. Curr. Opin. Oncol. 2017, 29, 73–78. [Google Scholar] [CrossRef]
- Pao, W.; Girard, N. New driver mutations in non-small-cell lung cancer. Lancet Oncol. 2011, 12, 175–180. [Google Scholar] [CrossRef]
- Rolfo, C.; Russo, A. Liquid biopsy for early stage lung cancer moves ever closer. Nat. Rev. Clin. Oncol. 2020, 17, 523–524. [Google Scholar] [CrossRef]
- Jamal-Hanjani, M.; Wilson, G.A.; McGranahan, N.; Birkbak, N.J.; Watkins, T.B.K.; Veeriah, S.; Shafi, S.; Johnson, D.H.; Mitter, R.; Rosenthal, R.; et al. Tracking the Evolution of Non–Small-Cell Lung Cancer. N. Engl. J. Med. 2017, 376, 2109–2121. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- De Bruin, E.C.; McGranahan, N.; Mitter, R.; Salm, M.; Wedge, D.C.; Yates, L.; Jamal-Hanjani, M.; Shafi, S.; Murugaesu, N.; Rowan, A.J.; et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science 2014, 346, 251–256. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sedda, G.; Gasparri, R. A new era in lung cancer care: From early diagnosis to personalized treatment. Shanghai Chest 2019, 3, 9. [Google Scholar] [CrossRef]
- Swanton, C.; Govindan, R. Clinical Implications of Genomic Discoveries in Lung Cancer. N. Engl. J. Med. 2016, 374, 1864–1873. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Robles, A.I.; Harris, C.C. Integration of multiple “OMIC” biomarkers: A precision medicine strategy for lung cancer. Lung Cancer 2017, 107, 50–58. [Google Scholar] [CrossRef] [Green Version]
- Broza, Y.Y.; Zhou, X.; Yuan, M.; Qu, D.; Zheng, Y.; Vishinkin, R.; Khatib, M.; Wu, W.; Haick, H. Disease Detection with Molecular Biomarkers: From Chemistry of Body Fluids to Nature-Inspired Chemical Sensors. Chem. Rev. 2019, 119, 11761–11817. [Google Scholar] [CrossRef]
- Miners, J.O.; Yang, X.; Knights, K.M.; Zhang, L. The Role of the Kidney in Drug Elimination: Transport, Metabolism, and the Impact of Kidney Disease on Drug Clearance. Clin. Pharmacol. Ther. 2017, 102, 436–449. [Google Scholar] [CrossRef] [PubMed]
- Harpole, M.; Davis, J.; Espina, V. Expert Review of Proteomics: Current state of the art for enhancing urine biomarker discovery Current state of the art for enhancing urine biomarker discovery. Expert Rev. Proteomics 2016, 9450, 609–626. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Dinges, S.S.; Hohm, A.; Vandergrift, L.A.; Nowak, J.; Habbel, P.; Kaltashov, I.A.; Cheng, L.L. Cancer metabolomic markers in urine: Evidence, techniques and recommendations. Nat. Rev. Urol. 2019, 16, 339–362. [Google Scholar] [CrossRef] [PubMed]
- Burton, C.; Ma, Y. Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges. Curr. Med. Chem. 2019, 26, 5–28. [Google Scholar] [CrossRef]
- Amundsen, T.; Sundstrøm, S.; Buvik, T.; Gederaas, O.A.; Haaverstad, R. Can dogs smell lung cancer? First study using exhaled breath and urine screening in unselected patients with suspected lung cancer. Acta Oncol. (Madr.) 2014, 53, 307–315. [Google Scholar] [CrossRef]
- Mazzola, S.M.; Pirrone, F.; Sedda, G.; Gasparri, R.; Romano, R.; Spaggiari, L.; Mariangela, A. Two-step investigation of lung cancer detection by sniffer dogs. J. Breath Res. 2020, 14, 26011. [Google Scholar] [CrossRef] [PubMed]
- Mathé, E.A.; Patterson, A.D.; Haznadar, M.; Manna, S.K.; Krausz, K.W.; Bowman, E.D.; Shields, P.G.; Idle, J.R.; Smith, P.B.; Anami, K.; et al. Noninvasive urinary metabolomic profiling identifies diagnostic and prognostic markers in lung cancer. Cancer Res. 2014, 74, 3259–3270. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Seow, W.J.; Shu, X.-O.; Nicholson, J.K.; Holmes, E.; Walker, D.I.; Hu, W.; Cai, Q.; Gao, Y.-T.; Xiang, Y.-B.; Moore, S.C.; et al. Association of Untargeted Urinary Metabolomics and Lung Cancer Risk Among Never-Smoking Women in China. JAMA Netw. Open 2019, 2, e1911970. [Google Scholar] [CrossRef] [PubMed]
- Haznadar, M.; Cai, Q.; Krausz, K.W.; Bowman, E.D.; Margono, E.; Noro, R.; Thompson, M.D.; Mathé, E.A.; Munro, H.M.; Steinwandel, M.D.; et al. Urinary metabolite risk biomarkers of lung cancer: A prospective cohort study. Cancer Epidemiol. Biomark. Prev. 2016, 25, 978–986. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Yuan, J.M.; Butler, L.M.; Gao, Y.T.; Murphy, S.E.; Carmella, S.G.; Wang, R.; Nelson, H.H.; Hecht, S.S. Urinary metabolites of a polycyclic aromatic hydrocarbon and volatile organic compounds in relation to lung cancer development in lifelong never smokers in the Shanghai Cohort Study. Carcinogenesis 2014, 35, 339–345. [Google Scholar] [CrossRef] [PubMed]
- Patel, D.P.; Pauly, G.T.; Tada, T.; Parker, A.L.; Toulabi, L.; Kanke, Y.; Oike, T.; Krausz, K.W.; Gonzalez, F.J.; Harris, C.C. Improved detection and precise relative quantification of the urinary cancer metabolite biomarkers—Creatine riboside, creatinine riboside, creatine and creatinine by UPLC-ESI-MS/MS: Application to the NCI-Maryland cohort population controls and lung can. J. Pharm. Biomed. Anal. 2020, 191, 113596. [Google Scholar] [CrossRef]
- Carrola, J.; Rocha, C.M.; Barros, A.S.; Gil, A.M.; Goodfellow, B.J.; Carreira, I.M.; Bernardo, J.; Gomes, A.; Sousa, V.; Carvalho, L.; et al. Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of urine. J. Proteome Res. 2011, 10, 221–230. [Google Scholar] [CrossRef]
- Zhang, C.; Leng, W.; Sun, C.; Lu, T.; Chen, Z.; Men, X.; Wang, Y.; Wang, G.; Zhen, B.; Qin, J. Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors. EBioMedicine 2018, 30, 120–128. [Google Scholar] [CrossRef] [Green Version]
- Hanai, Y.; Shimono, K.; Matzumura, K.; Vachani, A.; Albelda, S.; Yamazaki, K.; Beauchamp, G.K.; Hiroaki, O. Urinary Volatile Compounds as Biomarkers for Lung Cancer. Biosci. Biotechnol. Biochem. 2012, 76, 679–684. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pérez Antón, A.; Ramos, Á.G.; del Nogal Sánchez, M.; Pavón, J.L.P.; Cordero, B.M.; Pozas, Á.P.C. Headspace-programmed temperature vaporization-mass spectrometry for the rapid determination of possible volatile biomarkers of lung cancer in urine. Anal. Bioanal. Chem. 2016, 408, 5239–5246. [Google Scholar] [CrossRef]
- Takahashi, Y.; Sakaguchi, K.; Horio, H.; Hiramatsu, K.; Moriya, S.; Takahashi, K.; Kawakita, M. Urinary N1, N12-diacetylspermine is a non-invasive marker for the diagnosis and prognosis of non-small-cell lung cancer. Br. J. Cancer 2015, 113, 1493–1501. [Google Scholar] [CrossRef]
- Takahashi, Y.; Horio, H.; Sakaguchi, K.; Hiramatsu, K.; Kawakita, M. Significant correlation between urinary N1, N12-diacetylspermine and tumor invasiveness in patients with clinical stage IA non-small cell lung cancer. BMC Cancer 2015, 15, 1–8. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mazzone, P.J.; Wang, X.F.; Lim, S.; Choi, H.; Jett, J.; Vachani, A.; Zhang, Q.; Beukemann, M.; Seeley, M.; Martino, R.; et al. Accuracy of volatile urine biomarkers for the detection and characterization of lung cancer. BMC Cancer 2015, 15, 15–20. [Google Scholar] [CrossRef] [Green Version]
- Gào, X.; Xuan, Y.; Benner, A.; Anusruti, A.; Brenner, H.; Schöttker, B. Nitric Oxide Metabolites and Lung Cancer Incidence: A Matched Case-Control Study Nested in the ESTHER Cohort. Oxid. Med. Cell. Longev. 2019, 2019. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gào, X.; Brenner, H.; Holleczek, B.; Cuk, K.; Zhang, Y.; Anusruti, A.; Xuan, Y.; Xu, Y.; Schöttker, B. Urinary 8-isoprostane levels and occurrence of lung, colorectal, prostate, breast and overall cancer: Results from a large, population-based cohort study with 14 years of follow-up. Free Radic. Biol. Med. 2018, 123, 20–26. [Google Scholar] [CrossRef] [PubMed]
- Wang, W.; Wang, S.; Zhang, M. Evaluation of kininogen 1, osteopontin and α-1-antitrypsin in plasma, bronchoalveolar lavage fluid and urine for lung squamous cell carcinoma diagnosis. Oncol. Lett. 2020, 19, 2785–2792. [Google Scholar] [CrossRef] [PubMed]
- Xia, X.; Lu, J.J.; Zhang, S.S.; Su, C.H.; Luo, H.H. Midkine is a serum and urinary biomarker for the detection and prognosis of non-small cell lung cancer. Oncotarget 2016, 7, 87462–87472. [Google Scholar] [CrossRef] [Green Version]
- Liu, B.; Ricarte Filho, J.; Mallisetty, A.; Villani, C.; Kottorou, A.; Rodgers, K.; Chen, C.; Ito, T.; Holmes, K.; Gastala, N.; et al. Detection of Promoter DNA Methylation in Urine and Plasma Aids the Detection of Non-Small Cell Lung Cancer. Clin. Cancer Res. 2020, 26, 4339–4348. [Google Scholar] [CrossRef]
- Nolen, B.M.; Lomakin, A.; Marrangoni, A.; Velikokhatnaya, L.; Prosser, D.; Lokshin, A.E. Urinary Protein Biomarkers in the Early Detection of Lung Cancer. Cancer Prev. Res. 2015, 8, 111–119. [Google Scholar] [CrossRef] [Green Version]
- Wu, Z.; Yang, Z.; Li, C.S.; Zhao, W.; Liang, Z.X.; Dai, Y.; Zhu, Q.; Miao, K.L.; Cui, D.H.; Chen, L.A. Differences in the genomic profiles of cell-free DNA between plasma, sputum, urine, and tumor tissue in advanced NSCLC. Cancer Med. 2019, 8, 910–919. [Google Scholar] [CrossRef]
- Kawamoto, H.; Hara, H.; Araya, J.; Ichikawa, A.; Fujita, Y.; Utsumi, H.; Hashimoto, M.; Wakui, H.; Minagawa, S.; Numata, T.; et al. Prostaglandin E-Major Urinary Metabolite (PGE-MUM) as a Tumor Marker for Lung Adenocarcinoma. Cancers 2019, 11, 768. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kalinke, L.; Thakrar, R.; Janes, S.M. The promises and challenges of early non-small cell lung cancer detection: Patient perceptions, low-dose CT screening, bronchoscopy and biomarkers. Mol. Oncol. 2020, d, 1–21. [Google Scholar] [CrossRef]
- Burki, T.K. Predicting lung cancer prognosis using machine learning. Lancet Oncol. 2016, 17, e421. [Google Scholar] [CrossRef]
- Hart, G.R.; Roffman, D.A.; Decker, R.; Deng, J. A multi-parameterized artificial neural network for lung cancer risk prediction. PLoS ONE 2018, 13, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gasparri, R.; Romano, R.; Sedda, G.; Borri, A.; Petrella, F.; Galetta, D.; Casiraghi, M.; Spaggiari, L. Diagnostic biomarkers for lung cancer prevention. J. Breath Res. 2018, 12. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Study | Population | Main Results |
---|---|---|
Amundsen T. 2014 [20] | Lung cancer (77) | Sensitivity: 60% Specificity: 29.2% |
Mazzola S.M. 2020 [21] | Lung cancer (140), Controls (194) | Sensitivity: 45–73% Specificity: 89–91% |
Study | Population | Lung Cancer Patients (n) | Method | Metabolites | Main Results |
---|---|---|---|---|---|
Mathé E.A. 2014 [22] | 1005 | 469 | LC-MS/MS | N-acetylneuraminic acid Cortisol sulfate Creatine Riboside 561+ | Accuracy = 78.1% |
Seow W.J. 2019 [23] | 564 | 275 | LC-MS/MS | 5-methyl-2-furoic-acid | N.R. |
Haznadar M. 2016 [24] | 529 | 178 | LC-MS/MS | Creatine riboside N-acetylneuraminic acid Cortisol sulfate 561+ | Sensitivity = 50% Specificity = 86% |
Yuan J.M. 2014 [25] | 165 | 82 | LC-MS/MS | PheT 3-OH-Phe total OH-Phe | |
Patel D.P. 2020 [26] | 174 | 76 | UPLC-ESI-MS | Creatine ribosi de Creatinine riboside Creatine Creatinine | |
Carrola J. 2011 [27] | 125 | 71 | HR-NMR | hydroxyisovalerate R-hydroxyisobutyrate N-acetylglutamine Creatinine | Sensitivity = 93% Specificity = 94% |
Zhang C. 2018 [28] | 231 | 33 | LC-MS/MS | FTL MAPK1IP1L FGB RAB33B RAB15 | Sensitivity = 90–96.9% Specificity = 54.5–90% |
Hanai Y. 2012 [29] | 40 | 20 | GC-TOF MS | 2-pentanone | Sensitivity = 85–95% Specificity = 70–100% |
Anton A.P. 2016 [30] | 20 | 6 | HS-PTV-MS | 2-Butanone 2-Pentanone Pyrrole 2-Heptanone 2-Ethyl-1-hexanol | Sensitivity = 40–100% Specificity = 100% |
Study | Population | Lung Cancer Patients (n) | Metabolites | Method/Device | Main Results |
---|---|---|---|---|---|
Takahashi Y., 2015 [31] | 171 | 171 | N1,N12-diacetylspermine | Colloid gold aggregation procedure | Sensitivity: 69.4% Specificity: 57.4% Accuracy: 60.8% |
Takahashi Y., 2015 [32] | 499 | 260 | Diacetylspermine | Colloidal gold aggregation procedure | Sensitivity: 62.2% Specificity: 71.7% |
Mazzone P.J., 2015 [33] | 145 | 90 | Volatile organic compounds analysis | Colorimetric sensor array | Sensitivity: 81.4% Specificity: 60.0% |
Gào X., 2019 [34] | 980 | 245 | NO metabolites (nitrite and nitrate) 8-isoprostane | ELISA | |
Gào X., 2018 [35] | 866 | 207 | 8-isoprostane | ELISA | Accuracy: 62.4% |
Zhang W., 2020 [36] | 309 | 112 | Ferritin light chain, Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like, Fibrinogen Beta Chain, Member RAS Oncogene Family RAB33B and RAB15 | ELISA | Accuracy: 82.0–94.7% |
Xia X., 2016 [37] | 65 | 45 | Midkine | ELISA | Sensitivity: 71.2% Specificity: 88.1% |
Wang W., 2020 [36] | 51 | 31 | Kininogen 1 Osteopontin α-1-antitrypsin | ELISA | Sensitivity: 85–100% Specificity: 53–65% |
Liu B., 2020 [38] | 101 | 74 | Gene: CDO1, TAC1, HOXA, SOX17 | Methylation on beads and real-time PCR | Sensitivity: 93% Specificity: 30% |
Nolen B.M., 2015 [39] | 234 | 83 | Insulin-like growth factor-binding protein 1, interleukin-1 receptor antagonist a, Carcinoembryonic antigen-related cell adhesion molecule 1 | Multiplexed bead-based immunoassays | Sensitivity: 72% Specificity: 100% Accuracy: 71–83% |
Wu Z., 2019 [40] | 50 | 50 | Cell-free DNA | Next-generation sequencing platform | Accuracy: 69% |
Kawamoto H., 2019 [41] | 178 | 54 | Prostaglandin E-major urinary metabolite | Radioimmunoassay | Sensitivity: 67.7% Specificity: 70.4% |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Gasparri, R.; Sedda, G.; Caminiti, V.; Maisonneuve, P.; Prisciandaro, E.; Spaggiari, L. Urinary Biomarkers for Early Diagnosis of Lung Cancer. J. Clin. Med. 2021, 10, 1723. https://doi.org/10.3390/jcm10081723
Gasparri R, Sedda G, Caminiti V, Maisonneuve P, Prisciandaro E, Spaggiari L. Urinary Biomarkers for Early Diagnosis of Lung Cancer. Journal of Clinical Medicine. 2021; 10(8):1723. https://doi.org/10.3390/jcm10081723
Chicago/Turabian StyleGasparri, Roberto, Giulia Sedda, Valentina Caminiti, Patrick Maisonneuve, Elena Prisciandaro, and Lorenzo Spaggiari. 2021. "Urinary Biomarkers for Early Diagnosis of Lung Cancer" Journal of Clinical Medicine 10, no. 8: 1723. https://doi.org/10.3390/jcm10081723
APA StyleGasparri, R., Sedda, G., Caminiti, V., Maisonneuve, P., Prisciandaro, E., & Spaggiari, L. (2021). Urinary Biomarkers for Early Diagnosis of Lung Cancer. Journal of Clinical Medicine, 10(8), 1723. https://doi.org/10.3390/jcm10081723