Next Article in Journal
Clinical Features and Cutaneous Manifestations of Juvenile and Adult Patients of Dermatomyositis Associated with Myositis-Specific Autoantibodies
Previous Article in Journal
The Diagnostic Usefulness of Circulating Profile of Extracellular Matrix Components: Sulfated Glycosaminoglycans (sGAG), Hyaluronan (HA) and Extracellular Part of Syndecan-1 (sCD138) in Patients with Crohn’s Disease and Ulcerative Colitis
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Perspective

Urinary Biomarkers for Early Diagnosis of Lung Cancer

by
Roberto Gasparri
1,*,
Giulia Sedda
1,
Valentina Caminiti
1,
Patrick Maisonneuve
2,
Elena Prisciandaro
1 and
Lorenzo Spaggiari
1,3
1
Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy
2
Division of Epidemiology and Biostatistics, IEO, European Institute of Oncology IRCCS, Via Giuseppe Ripamonti 435, 20141 Milan, Italy
3
Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122 Milan, Italy
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2021, 10(8), 1723; https://doi.org/10.3390/jcm10081723
Submission received: 10 March 2021 / Revised: 29 March 2021 / Accepted: 11 April 2021 / Published: 16 April 2021
(This article belongs to the Section Oncology)

Abstract

:
Lung cancer is the leading cause of cancer deaths worldwide. Its early detection has the potential to significantly impact the burden of the disease. The screening and diagnostic techniques in current use suffer from limited specificity. The need therefore arises for a reliable biomarker to identify the disease earlier, which can be integrated into a test. This test would also allow for the recurrence risk after surgery to be stratified. In this context, urine could represent a non-invasive alternative matrix, with the urinary metabolomic profile offering a potential source for the discovery of diagnostic biomarkers. This paper aims to examine the current state of research and the potential for translation into clinical practice.

1. Introduction

Lung cancer has a high mortality rate globally, and in the majority of cases, diagnosis is often made at a late stage when the process of metastatization has already begun [1]. Thus, patient survival has been limited over the last 20 years [2]. This unfavorable outcome is mainly due to the absence of an easy-to-perform, accurate, non-invasive diagnostic test for the population at risk.
So far, in clinical practice, low-dose computed tomography (LDCT) is the only screening test validated for the early diagnosis of lung cancer in symptomatic subjects or screening of selected risk categories, such as heavy smokers over 50 years of age [3,4]. It has been demonstrated that for well-selected high-risk subjects, LDCT can promote a 20–39% reduction in the number of deaths due to lung cancer compared to chest X-ray or non-intervention procedures [5]. However, this does not apply to the entire high-risk population due to method costs, over-diagnosis, and the increased rate of false-positive results (approximately one in five LDCT screenings) that can lead to stressful experiences or more invasive tests [1].
Alongside increased understanding of the interactions between metabolism and cancer biology [6] associated with technological improvement, new methods have been developed and integrated into clinical practice, such as liquid biopsy [7]. For instance, in advanced lung cancer patients, liquid biopsies allow investigators to detect the presence of a single mutation or panel of mutations (e.g., EGFR or ALK mutations), enabling a personalized therapeutic strategy to be implemented for each patient [8,9]. Considering the heterogeneity of lung cancer [10,11], the need to integrate not only mutational analysis but also all the clinical features of each patient into a major complex algorithm is an issue that has been raised [12]. Investigators agree that the analysis should embrace the entirety of the tumor profile, suggesting an integration of the different levels of analysis, and the personal epidemiological data of the individual. Based on biological fluid analysis, omics researchers have consequently implemented pilot studies that have revealed great potential for early lung cancer diagnosis [13,14].
Among the different biological fluids (such as breath, blood, and bronchoalveolar fluid), urine offers several advantages: it can be collected from large cohorts, its collection is non-invasive, it incurs low handling costs, and prolonged frozen storage is possible. Moreover, the technology [15] has now evolved to the point whereby urine analysis may be performed as a multilevel approach.

2. The Role of Kidney Physiology in Oncological Practice

The kidneys are responsible for the elimination of endogenous compounds, drugs, and nondrug xenobiotics. Renal clearance is normally considered the net result of glomerular filtration, tubular secretion, and reabsorption. Characterization of the contribution of individual transporters expressed on basolateral and apical membranes of the tubule epithelium to drug and chemical excretion has advanced significantly over the last two decades [16].
Urine, through renal filtration, is formed by all the metabolites produced by physiological cellular catabolism. Renal filtration results in a matrix that is less complex, and results in the presence of fewer factors known to interfere with biomarker assay [17]. For many years, it has been understood that urine is composed of glucose, ketones, and metabolite products. The majority of the proteins are instead reabsorbed at the glomerular level; thus, the urine proteome is considered to be less complex than the plasma proteome. Furthermore, the presence of high-abundance proteins (e.g., albumin, alpha-1-antitrypsin, immunoglobulin) that mask low-abundance proteins have not yet allowed the whole proteome to be classified precisely [17].
Finally, cancer interaction with the cellular host on several metabolic and biological mechanisms can induce the liberation of specific cancer-correlated metabolites.
Thanks to all these characteristics, researchers have centered their efforts on finding urinary metabolites for several cancers, such as those of the urological system (kidney, prostate, and bladder) and also those of the breast, ovary, and gastrointestinal tract [18,19].
Furthermore, recent technological advances in instrumentation and equipment, such as that used in nuclear magnetic resonance, mass spectrometry, and gas and liquid chromatography, have increased the chances of discovering urine onco-biomarkers and of analytical reproducibility [19].

3. Materials and Methods

We carried out a comprehensive literature search using PubMed to retrieve original research papers presenting data on urinary biomarkers for the early diagnosis of lung cancer.
No language restrictions were applied, and the date restriction was from the last ten years (2010–2020) to consider the improvement in technology. Putative biomarkers were evaluated based on the five-phase approach [17] to guarantee a scientific standard as well as a roadmap for successfully translating biomarker research from the basic science to the bedside. The following PubMed search query was used as a first step: (“urine” [All Fields] AND (“biomarker s” [All Fields] OR “biomarkers” [MeSH Terms] OR “biomarkers” [All Fields] OR “biomarker” [All Fields] OR “metabolite” [All Fields] OR “metabolites” [All Fields]) AND (“lung cancer” [All Fields] OR “lung neoplasms” [MeSH Terms]) AND (“2010” [Date-Entry]: “2020/10” [Date-Entry])). Titles and abstracts available in PubMed of all identified articles were screened to ascertain their relevance. The full texts of potentially relevant study reports were further evaluated. Additional study reports identified from other sources (Web of Science, Google scholar, Embase, Scopus, and the Cochrane Library, as well as citations in the reference lists of identified relevant articles or reviews on the topic) were also evaluated for inclusion. Selected articles were reviewed and data on the diagnostic performance (sensitivity, specificity, accuracy) of various urine metabolites for the detection of lung cancer were extracted and crosschecked independently by two investigators (RG and PM). Any disagreement was resolved by their joint consensus. Similarly, we carried out a second literature search using the following PubMed search query: (“urine” [All Fields] AND (“dog” [All Fields] OR “dogs” [All Fields]) AND (“lung cancer” [All Fields] OR “lung neoplasms” [MeSH Terms]) AND (“2010/01” [Date-Entry]: “2020/10” [Date-Entry]), to retrieve studies assessing lung cancer detection by sniffer dogs.
Overall, 263 references published from 1 January 2010 to 31 October 2020 were retrieved from the first PubMed query. After excluding irrelevant papers (reviews, animal or fundamental research studies), 20 articles satisfied the selection criteria and were included in the review. A single report identified using another approach was also included. Finally, six articles were retrieved from the second PubMed query and two studies on sniffer dogs were included in the review.

4. Results

From the papers extracted, two studies [20,21] reported on the training of sniffer dogs. Detection dogs are currently used to identify illegal substances, such as explosives or drugs, or to recognize missing persons in highly demanding environments. These recently published studies reported the ability of trained dogs to differentiate cancer patients from healthy individuals based on urine sniffing (Table 1). These results indicate that there are determinant molecules in the urine, predictive of lung cancer.
With these data as a starting point, in recent decades, human translational approaches have been developed. Many groups have analyzed urinary metabolites employing either gas or liquid chromatography coupled with mass spectrometry to make an early screening diagnosis or detect lung cancer recurrence (Table 2).
The most extensive study produced by the National Cancer Institute, published in 2014, was qualitative with a well-established design. They used mass spectrometry in a case-control study to assess the urine of over 1000 samples and uncovered a set of urine metabolites associated with a cancer diagnosis. Two metabolites, creatine riboside and N-acetylneuraminic acid, were significantly elevated in lung cancer patients. These results were subsequently validated in an independent sample set. Both metabolites were enriched in tumor tissue compared with adjacent non-tumor tissue and positively correlated with urine levels, thus revealing their direct association with tumor metabolism [22].
A successive evaluation of this panel of urinary metabolite lung cancer biomarkers in the well-characterized prospective Southern Community Cohort Study (SCCS) confirmed the association of creatine riboside and N-acetylneuraminic acid levels with lung cancer risk before the onset of clinically-detectable disease [24].
Seow, in 2019 [23], in a nested case-control study of 564 never-smoking women, found that 5-methyl-2-furoic acid in urine was associated with a decreased risk of lung cancer.
Finally, in 2020, Patel [26] improved the detection and precise quantification of the urinary cancer metabolite biomarkers creatine riboside and creatinine riboside, creatine and creatinine, analyzing 76 lung cancer patients and 98 controls, by precise ultra-pressure liquid chromatography-tandem mass spectrometry.
Moreover, several signatures have been investigated with promising results. Zhang’s group [28] selected a panel of five urinary molecules (ferritin light chain, mitogen-Activated Protein Kinase 1 Interacting Protein 1-Like, fibrinogen Beta Chain, two Member RAS Oncogene Family, RAB33B and RAB15) as a predictive model to differentiate lung cancer from healthy lung tissue. Carrola et al. [27] instead, reported their signature with hydroxyisovalerate, R-hydroxyisobutyrate, N-acetylglutamine, and creatinine in 125 individuals, with a good performance of sensitivity and specificity, including a single molecule, such as creatine.
Yuan, in 2014 [25], assessed the lung cancer risk via urinary constituents deriving from tobacco smoke, demonstrated a significantly different risk of lung cancer according to ethnic/racial characteristics.
Additional studies have been conducted using alternative techniques, such as ELISA and PCR (Table 3) to analyze urinary metabolites.
The colloid gold aggregation procedure was employed by Takahashi’s group [31,32] in two consecutive publications, which identified diacetylspermine as biomarkers in lung cancer patients.
In 2015, Mazzone [33] analyzed the volatile organic compounds (VOCs) of the urinary headspace, finding a signature that could distinguish lung cancer patients utilizing a colorimetric sensor array exposed to the headspace gas of neat and pre-treated urine cancerous samples. Other studies [34,35,36,37] used immunosorbent assays, such as enzyme-linked immunosorbent assay (ELISA).
Finally, two interesting studies explored the genome and epigenome level. In 2019 [40], Wu and colleagues published a prospective study detecting comparable profiles of cell-free DNA in sputum, plasma, urine, and tumor tissue from 50 lung cancer patients by next-generation sequencing. Liu B et al. [38] evaluated the simultaneous positive methylation of genes (CDO1, TAC1, HOXA7, HOXA9, SOX17, and ZFP42) not only in urine but also in plasma samples, suggesting putative epigenetic biomarkers.

5. Study Limitations

These studies underlined the potentiality of biomarkers to differentiate lung cancer patients from healthy subjects with a non-invasive, patient-friendly fluid collection. Current limitations, for example in [24,25,28], were the small sample size investigated and the absence of a standard approach. Furthermore, other investigators [22,23,26] reported that they could not adjust and control for dietary and drug intake, thus representing the inability to control for exogenous effects on metabolism. Moreover, these studies demonstrated a degree of bias, ranging from patient selection to low accuracy, and, therefore, limited their clinical translation.

6. Future Perspectives

The major limitation encountered so far stems from the small sample size. This will need to be remedied in future studies. Further development and validation by means of independent, routine techniques that are more operationally feasible, such as ELISA and PCR, also seem indispensable steps for future clinical development.
The discovery and validation of biomarkers calls for the implementation of a worldwide network of research centers with constant data sharing and dissemination of results. Such connections could focus on those biomarkers that are more appropriate for clinical use [42].
For this reason, the creation of a consortium would be desirable, in which biomarkers for mass population screening are discussed and evaluated.
Each contributor will need to use a variety of data-gathering approaches and methods and render all the data transparently available in the public domain. This will be translated into a worldwide big data database which could be interrogated and analyzed [43,44].
Artificial intelligence analysis will be utilized to process, overlap, and integrate the molecular biomarkers as well as clinical and epidemiological data. The results obtained will be further processed by machine learning algorithms, enabling multiple diagnostic algorithms to be created for the early diagnosis of lung cancer [45].
It should also be noted that this network will allow for standardizing methods, which are pivotal to guarantee a high level of accuracy.
Moreover, the same approach could be applied to other biological fluids, such as blood and exhaled breath, to establish and integrate profiling and discover each individual’s phenotype in the large and heterogeneous cohort of the at-risk population.

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

Currently, there are no clinically available validated urinary biomarkers for the early diagnosis of lung cancer. However, urine has been the focus of many promising research projects over the past decade.
From a research project design perspective, for the vast majority of cases, we have created a customization of research based on the tumor’s objective characteristics.
The sample size investigated, and the lack of a standard approach, limit scientific robustness. However, all these studies are of immense value in that they are paving the way towards an international repository of high-quality data and datasets that can be interrogated and analyzed globally for further investigations.
All these results indicate the many steps that have been taken in the investigation into urinary biomarkers. Of note among these are the National Cancer Institute (NCI) studies on different urinary biomarkers based on the biomarkers’ validation criteria.
Nowadays, lung cancer is considered a disease with systemic influences, in which many pathological processes interact and develop. With the necessary resources, information, tools, and unstinting dedication, this will allow for increasingly early discovery and a better chance of healing in the near future. This prospective perception includes combining and comparing the markers examined by different groups employing a worldwide big-data database.

Author Contributions

Conceptualization, R.G.; methodology, P.M.; formal analysis, R.G., G.S.; data curation, E.P.; writing—original draft preparation, V.C., R.G., L.S.; writing—review and editing, R.G., G.S.; supervision, L.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was partially supported by the Italian Ministry of Health with Ricerca Corrente and 5 × 1000 funds.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

We would like to thank William Russell-Edu for English editing of the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. 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]
  2. Siegel, R.L.; Miller, K.D.; Jemal, A. Cancer statistics, 2020. CA Cancer J. Clin. 2020, 70, 7–30. [Google Scholar] [CrossRef]
  3. 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]
  4. 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]
  5. 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]
  6. 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]
  7. Hofman, P. Liquid biopsy for early detection of lung cancer. Curr. Opin. Oncol. 2017, 29, 73–78. [Google Scholar] [CrossRef]
  8. Pao, W.; Girard, N. New driver mutations in non-small-cell lung cancer. Lancet Oncol. 2011, 12, 175–180. [Google Scholar] [CrossRef]
  9. 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]
  10. 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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]
  17. 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]
  18. 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]
  19. 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]
  20. 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]
  21. 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]
  22. 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]
  23. 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]
  24. 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]
  25. 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]
  26. 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]
  27. 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]
  28. 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]
  29. 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]
  30. 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]
  31. 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]
  32. 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]
  33. 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]
  34. 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]
  35. 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]
  36. 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]
  37. 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]
  38. 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]
  39. 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]
  40. 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]
  41. 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]
  42. 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]
  43. Burki, T.K. Predicting lung cancer prognosis using machine learning. Lancet Oncol. 2016, 17, e421. [Google Scholar] [CrossRef]
  44. 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]
  45. 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]
Table 1. Studies using sniffer dog detection of urinary VOCs.
Table 1. Studies using sniffer dog detection of urinary VOCs.
StudyPopulationMain 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%
Table 2. Summary of the studies using gas or liquid mass spectrometry for urine metabolite analysis
Table 2. Summary of the studies using gas or liquid mass spectrometry for urine metabolite analysis
StudyPopulationLung Cancer Patients (n)MethodMetabolitesMain Results
Mathé E.A. 2014 [22]1005469LC-MS/MSN-acetylneuraminic acid
Cortisol sulfate
Creatine
Riboside
561+
Accuracy = 78.1%
Seow W.J. 2019 [23]564275LC-MS/MS5-methyl-2-furoic-acidN.R.
Haznadar M. 2016 [24]529178LC-MS/MSCreatine riboside
N-acetylneuraminic acid
Cortisol sulfate
561+
Sensitivity = 50%
Specificity = 86%
Yuan J.M. 2014 [25]16582LC-MS/MSPheT
3-OH-Phe
total OH-Phe
Patel D.P. 2020 [26]17476UPLC-ESI-MSCreatine ribosi de
Creatinine riboside
Creatine
Creatinine
Carrola J. 2011 [27]12571HR-NMRhydroxyisovalerate
R-hydroxyisobutyrate
N-acetylglutamine
Creatinine
Sensitivity = 93%
Specificity = 94%
Zhang C. 2018 [28]23133LC-MS/MSFTL
MAPK1IP1L
FGB
RAB33B
RAB15
Sensitivity = 90–96.9%
Specificity = 54.5–90%
Hanai Y. 2012 [29]4020GC-TOF MS2-pentanoneSensitivity = 85–95%
Specificity = 70–100%
Anton A.P. 2016 [30]206HS-PTV-MS2-Butanone
2-Pentanone
Pyrrole
2-Heptanone 2-Ethyl-1-hexanol
Sensitivity = 40–100%
Specificity = 100%
LC: liquid chromatography; MS: mass spectrometry; UPLC-ESI: liquid chromatography electrospray; HR-NMR: high-resolution nuclear magnetic resonance; GC-TOF: Gas Chromatography Time-Of-Flight; HS–PTV: Headspace–Programmed Temperature Vaporization.
Table 3. Studies using alternative techniques.
Table 3. Studies using alternative techniques.
StudyPopulationLung Cancer Patients (n)MetabolitesMethod/DeviceMain Results
Takahashi Y., 2015 [31]171171N1,N12-diacetylspermineColloid gold aggregation procedureSensitivity: 69.4%
Specificity: 57.4%
Accuracy: 60.8%
Takahashi Y., 2015 [32]499260DiacetylspermineColloidal gold aggregation procedureSensitivity: 62.2%
Specificity: 71.7%
Mazzone P.J., 2015 [33]14590Volatile organic compounds analysisColorimetric sensor arraySensitivity: 81.4%
Specificity: 60.0%
Gào X., 2019 [34]980245NO metabolites (nitrite and nitrate)
8-isoprostane
ELISA
Gào X., 2018 [35]8662078-isoprostaneELISAAccuracy: 62.4%
Zhang W., 2020 [36]309112Ferritin light chain, Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like, Fibrinogen Beta Chain, Member RAS Oncogene Family RAB33B and RAB15ELISAAccuracy: 82.0–94.7%
Xia X., 2016 [37]6545MidkineELISASensitivity: 71.2%
Specificity: 88.1%
Wang W., 2020 [36]5131Kininogen 1
Osteopontin
α-1-antitrypsin
ELISASensitivity: 85–100%
Specificity: 53–65%
Liu B., 2020 [38]10174Gene: CDO1, TAC1, HOXA, SOX17Methylation on beads and real-time PCRSensitivity: 93%
Specificity: 30%
Nolen B.M., 2015 [39]23483Insulin-like growth factor-binding protein 1, interleukin-1 receptor antagonist a, Carcinoembryonic antigen-related cell adhesion molecule 1Multiplexed bead-based immunoassaysSensitivity: 72%
Specificity: 100%
Accuracy: 71–83%
Wu Z., 2019 [40]5050Cell-free DNANext-generation sequencing platformAccuracy: 69%
Kawamoto H., 2019 [41]17854Prostaglandin E-major urinary metaboliteRadioimmunoassaySensitivity: 67.7%
Specificity: 70.4%
ELISA: enzyme-linked immunosorbent assay; PCR: Polymerase Chain Reaction.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

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

AMA Style

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 Style

Gasparri, 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 Style

Gasparri, 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

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop