Urinary Biomarkers for Early Diagnosis of Disease: History
View Latest Version
Please note this is an old version of this entry, which may differ significantly from the current revision.
Contributor:
Beatriz Sequeira-Antunes
,
Hugo Alexandre Ferreira

Urinary biomarkers are molecules found in urine that can be used as indicators of certain diseases or health conditions. The presence or absence of these biomarkers can help in the early detection of diseases, allowing for timely intervention and treatment, improving patients outcomes.

  • urine
  • biomarkers
  • proteins
  • nucleic acids

1. Urine Metabolites and Their Role as Biomarkers

According to the World Health Organization (WHO), biomarkers are defined as “any substance, structure, or process that can be measured in the body or its products and influence or predict the incidence of outcome or disease” [1]. One of the many advantages of using biomarkers is that, unlike disease symptoms, which are subjective, biomarkers provide an objective and measurable way to characterise the disease [2]. They can often be measured by analysing blood or urine samples, helping clinicians avoid complex invasive procedures [3].
Urine is a rich source of cellular metabolites and an important, easily accessible biological fluid, and one of the most useful biofluids for routine testing [4]. Metabolomics is a novel field of science that seeks to quantitatively describe the fluctuations of numerous metabolites within organisms. It offers significant benefits in identifying disease biomarkers because certain metabolites can vary among individuals, indicating their unique metabolic traits and the underlying manifestations of their disease [5][6][7]. However, the normal reference values of various urine metabolites have not been established yet, and further clinical validation is necessary.
Metabolomic studies typically begin with sampling, followed by sample analysis. There are several techniques to do this analysis, although the most common used is nuclear magnetic resonance spectroscopy (NMR) [8][9]. This technique is non-destructive in nature, quantitative, and has a safe metabolite identification that provides detailed information on the molecular structure. Other techniques are used, such as gas chromatography mass spectrometry (GC-MS), liquid chromatography mass spectrometry (LC-MS), and the enzyme-linked immunosorbent assay (ELISA) [8].
Currently, approximately 4500 metabolites have been documented in urine, showing connections to approximately 600 human conditions including, for example, obesity, cancer, inflammation, and neurological diseases [10].

1.1. Arterial Hypertension

Hypertension, which is characterized by elevated blood pressure in systemic circulation arteries, is among the most widespread of chronic diseases [7][11]. According to statistics, the global prevalence of hypertension was 26.4% by 2000, and it is anticipated to climb to 29.2% by 2025 [7]. This disease can lead to other several diseases, such as stroke, heart disease, and kidney failure [11]. In this way, it can be said that hypertension is a key modifiable risk factor for cardiovascular morbidity and mortality [12] for which it is important to find urinary biomarkers that would allow for an early diagnosis.
Previous metabolomic studies on hypertension have been mainly based on blood and urine samples. The study carried out by Loo et al. [13] had as the identification of a panel of urinary metabolites whose changes are related to risk factors of cardiovascular disease (CVD) as its main objective. The results indicated that there are six urinary metabolites associated with blood pressure (proline-betaine, carnitine, hippurate, 4-cresyl sulphate, phenylacetylglutamine, and N-methyl-2-pyridone-5-carboxamide).
In recent years, trimethylamine-N-oxide (TMAO) has also emerged as a potential biomarker for the development of CVD [14][15][16]. This metabolite is a small, organic, gut microbiome-generated compound whose concentration increases after ingesting dietary L-carnitine and phosphatidylcholine rich foods such as red meat, eggs, and fish [16].

1.2. Oxidative Stress and Inflammatory Disease

Oxidative stress is caused by a production imbalance between reactive oxygen species (ROS) and antioxidant defences [17][18]. That phenomenon, if uncontrolled, initializes numerous intracellular signaling pathways that trigger apoptosis or excessive cell growth, which can lead to organ dysfunction in the heart, pancreas, kidneys, and lungs. As a consequence, hypertension, diabetes, chronic kidney disease, and pulmonary disorders can develop [19].
Inflammation is a natural defence mechanism of the immune system that can be triggered by a variety of factors [20]. Oxidative stress has the ability to activate several transcription factors that cause specific genes involved in inflammatory pathways to be expressed differentially [19]. Therefore, there is evidence that oxidative stress and inflammation are coincident phenomena that exert an influence on each other [21].
If the level of ROS exceeds the antioxidant capacity of a cell, cellular biomolecules such as DNA, lipids, and proteins are oxidized. This leads to the creation of compounds that can serve as biomarkers of oxidative stress. The most commonly used urinary oxidative stress biomarkers are 8-hydroxy-2′-deoxyguanosine (8-OHdG) [19][22][23], phenylacetylglutamine, adenine, glycine [24], lactate [9], 8-isoprostane [22][25], malondialdehyde (MDA), F2-isoprostanes, and dityrosine (diY) [23].

1.3. Chronic Kidney Disease

Chronic kidney disease (CKD) is a condition that occurs when the kidney gradually loses its function or when a glomerular filtration rate (GFR) is less than 60 mL/min per 1.73 m2 for 3 or more months, regardless of the cause [26]. The prevalence of CKD is increasing worldwide, and the mortality rate continues to be unacceptably high [27]. This disease is a complex one because can it affect multiple organ systems and often coexists with numerous associated conditions, such as CVD, diabetes mellitus, and chronic inflammation [28]. GFR estimation and albuminuria are commonly utilized to diagnose and predict the prognosis of CKD in clinical practice. GFR estimation correlates with the severity of kidney malfunction, whereas albuminuria indicates the existence of kidney damage [27].
Detecting CKD early is a crucial and unsatisfied medical requirement, not only for forecasting and impeding CKD progression, but also for enhancing patient survival and decreasing associated morbidities. That can be accomplished through the identification of suitable biomarkers [27]. Some new possible biomarkers of the diagnosis of CKD and its prediction of outcomes have been identified, such as creatinine [28][29].

1.4. Urinary Tract Infection

Urinary tract infections (UTI) affect 150 million people each year worldwide, with an annual incidence of 12.6% in women and 3% in men [30]. It appears due to microbial pathogens invading the urinary tract, which can lead to several clinical manifestations [31].
Currently, urine culture is the standard method in the diagnostics of UTI [32]. However, this method is slow, and therefore the diagnosis happens with a considerable delay, which is not desirable [31]. Also, urine test strips can be used for rapid UTI screening. These strips evaluate several biomarkers, although only nitrite and leukocyte esterase show an independent relation with UTI. Nonetheless, there are reports of considerable variations in the test performances, which limit their further use, and as such new biomarkers that allow for a quick diagnosis are warranted [32]. Several promising urine biomarkers of UTI such as agmatine and N6-methyladenine have already been identified [33].

1.5. Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia, the incidence of which is rapidly increasing [34]. AD is characterised by the formation of extracellular amyloid plaques which result from the accumulation of amyloid β-proteins, and intracellular neurofibrillary tangles due to the aggregation of tau proteins, leading to neuronal signalling disruption and cell death [35][36]. Thus, the development of the disease involves numerous factors such as amyloid β build-up, oxidative stress, tau phosphorylation, lipid imbalance, mitochondrial dysfunction, and inflammation [37].
AD is an ailment that frequently impacts the elderly population, and its symptoms are discernible only in the advanced stages, rendering early diagnosis a challenging task [38]. Currently, diagnosis is based on subjective neuropsychological tests and by late-stage biomarkers in cerebrospinal fluid (CSF), which require a lengthy and often a painful procedure [36]. Other often used biomarkers include imaging biomarkers such as the uptake of the 11C-Pittsburgh compound B (11C-Pib), as assessed using positron emission tomography (PET), which binds to amyloid plaques, and the atrophy of the hippocampus and mesial temporal structures, as assessed by magnetic resonance imaging (MRI) [39][40]. Nonetheless, these imaging modalities and examination procedures can also be lengthy and very expensive, whilst also using ionising radiation (PET) and high magnetic fields (MRI), which present risks to the patient [41].
The signs of AD pathology may also be found in the urine, so it is important to know which biomarkers can be used. Due to the oscillation of urinary flow, normalisation of the biomarker concentration is recommended in most cases. This is easily achieved through the relationship between the concentration of the biomarker and the concentration of creatinine [34]. Examples of this kind of biomarker are amino acid-conjugated acrolein (AC-Acro/Cre) and 3-hydroxypropyl mercapturic acid (3-HPMA/Cre) [42][43]. Other urinary biomarkers can be associated with AD, such as 8-OHdG [34][38], and knowing the relationship between AD and oxidative stress, 8-isoprostane and glycine can be included as AD biomarkers [34].

1.6. Oncologic Diseases

Cancer is one of the major causes of mortality worldwide, and according to the America Cancer Society, in 2022 there will be an estimated 1.9 million new cancer cases diagnosed and 609,360 cancer deaths in the United States [44]. Its incidence is predicted to increase significantly, with a forecast of 22 million new cancer cases and 13 million cancer-related deaths occurring annually by 2030 [45]. The best chance of reducing these numbers is through early detection. To this end, the use of biomarkers can be useful. However, in recent years the identification of novel biomarkers in biological fluids has increased significantly, although further validation is needed.

1.6.1. Lung Cancer

Lung cancer (LC) has a high mortality rate globally, and in most cases, diagnosis is often made at a late stage when the process of metastization has already begun [46]. To avoid such a scenario, the use of biomarkers may be useful. Some examples of recurrent biomarkers proposed in scientific papers investigating lung cancer were tyrosine and tryptophan [47], hippurate [48][49][50], N-aceglutamide, β-hydroxyisovaleric acid, α-hydroxyisobutyric acid, and creatinine [48], valine, proline betaine, taurine [50], and phenylalanine [47][50].

1.6.2. Breast Cancer

Breast cancer (BC) is the second most common cancer overall and the most frequent type of cancer in women worldwide [51]. The early diagnosis of breast cancer greatly increases the chance of cure and survival from the disease. Mammography is the gold standard for BC screening; however, it has limited sensitivity, involves exposure to ionising radiation, and it has not been shown to significantly contribute to decrease mortality [49][52]. In this way, noninvasive tests for BC with high sensitivity are needed. The use of urinary metabolomics for breast cancer detection at an early stage has increased over the last few years [49]. The major contributing metabolites identified were 8-OHdG, 1-methyladenoside, 1-methylguanosine [53][54][55][56], creatinine, succinate, valine, and isoleucine [49][57]. Nam et al. also identified homovanillate, 4-hydroxyphenylacetate, 5-hydroxyindoleacetate, and urea in urine as biomarkers of BC [58].

1.6.3. Bladder Cancer

Bladder cancer is the seventh most common cancer, with an average of 356,000 new cases diagnosed worldwide every year [45][59]. It is the second most prevalent malignancy in middle age and in elderly men after prostate cancer [60]. The current standard procedure for bladder cancer detection and monitoring tumour progression and recurrence involves urine cytology, cystoscopy, and biopsy; however, these techniques have a number of limitations, as low sensitivity, in addition to the fact that is expensive, invasive, and painful [45]. Thus, new diagnostic approaches that improve the diagnostic accuracy, reduce pain levels and that are noninvasive are needed.
Screening bladder cancer patients through urine metabolomics biomarker technology is a promising strategy to improve detection and diagnosis [61]. Some of the commonly used biomarkers for bladder cancer include hippurate [59], lactate [62], succinate [62][63], phenylalanine, tyrosine, tryptophan, leucine, uric acid [60], carnitine [61][63], and taurine [59][62].

1.6.4. Prostate Cancer

Prostate cancer is among the most prevalent types of cancer in men across Europe, and its occurrence has surged dramatically over the last twenty years [64]. At present, the detection of prostate cancer is still an imprecise practice. The screening process involves measuring the level of prostate-specific antigen (PSA) in the blood and performing a digital rectal examination [65]. PSA is the only biomarker commonly utilized in the diagnosis of prostate cancer patients; however, its sensitivity and specificity are inadequate, resulting in the occurrence of false-negative and false-positive test outcomes [66]. To reduce incorrect results and increase the accuracy of diagnosis, it is necessary to search for additional prostate cancer biomarkers.
Sreekumar et al. [67] reported that increasing levels of proline, kynurenine, uracil, and glycerol-3-phosphate were significantly correlated with disease progression. The authors also found that sarcosine concentrations increased in patients with prostate cancer [67]. Some studies confirmed the potential of sarcosine as a noninvasive screening tool for prostate cancer [64][66][68][69][70][71][72]. Other identified contributing metabolites identified were leucine [70][72], creatinine [66][68][72], tyrosine, tryptophan, taurine [72][73], and alanine [65][70].

1.6.5. Gastric Cancer

Gastric cancer was estimated to be responsible for over one million new cases in 2018 worldwide and for more than 700,000 deaths [45]. Currently, the standard diagnostic method for gastric cancer is gastroduodenal endoscopy; however, this technique has several drawbacks, such as invasiveness and high-cost [74]. Currently, no diagnostic method is perfect for detecting gastric cancer at an early stage since, in most cases, it is asymptomatic [8]. In recent years, several urinary biomarkers have been identified as new tools for the early screening of gastric cancer. Among the 25 metabolites investigated by Chan et al. [75], only 2-hydroxyisobutyrate, 3-indoxylsufate and alanine provided useful information for gastric cancer diagnosis. Another study conducted by Dong et al. in 2009 [76] concluded that the level of urinary prostaglandin E2 metabolite (PGE-M) was higher in gastric cancer patients than in a control group. Furthermore, arginine, leucine, isoleucine, valine, citric acid, succinate, histidine, methionine, serine, aspartate, taurine, tyrosine, lactate, and phenylalanine [74][77] have been proposed as biomarkers for gastric cancer.

1.6.6. Kidney Cancer

Kidney or renal cancer is among the top ten most prevalent forms of cancer, and it is more frequent in men than in women [45]. According to the American Cancer Society’s projection for 2022, roughly 79,000 new cases of kidney cancer and approximately 13,920 fatalities due to this type of cancer were anticipated [78]. Currently, most kidney cancers are detected before symptoms appear, namely when performing routine examinations, as well as when investigating symptoms such as back or abdominal pain using imaging [79]. However, it remains true that alternative diagnosis methods, particularly those that use urine markers, could be useful in detecting kidney cancer even earlier. Therefore, the identification of a screening biomarker has the potential for substantial health benefits. Promising biomarkers include acylcarnites such as isobrutyrycarnite, suberoylcarnite, and acetylcarnite [80].

2. Urine Proteins Biomarkers

Apart from metabolites, the presence or absence of proteins in urine can provide valuable information about various medical conditions, including kidney injury and certain types of cancers. 
One of the most common urine proteins used as biomarker is urinary albumin, which is produced in the liver and helps to maintain the balance of fluids in the body. Overall, urinary albumin can be a useful biomarker for a range of diseases and conditions that affect the cardiovascular system, such as hypertension [12][81], the liver [82], and the kidneys, such as chronic kidney disease [27].
Although urinary albumin is useful for assessing kidney function, it is important to look for other indicators of kidney injury other than GFR. Current established filtration markers for the prediction of outcomes in patients with CKD include creatinine and cystatin C [29][83]. Other biomarkers potentially useful to indicate kidney damage are β2-Microglobulin (B2M) and Beta Trace Protein (BTP). These proteins are filtered out of the blood by the kidneys and excreted in the urine, normally in small amounts of less than 20 and 300 micrograms per liter (µg/L), respectively. Thus, elevated levels of BTP and B2M in urine can be an indication of kidney damage or dysfunction, and can be used as biomarkers to diagnose and monitor various kidney-related diseases and conditions [27][84].
Uromodulin is a protein that is produced by the kidney and is the most abundant protein found in urine [85]. This protein can be used as a biomarker, since its concentration gradually decreases with worsening kidney function, so in patients with CKD, its concentration will be lower [27]. Moreover, the Neutrophil Gelatinase-Associated Lipocalin (NGAL) [83][85] and Kidney Injury Disease-1 (KIM-1) [85][86] have also been suggested to be potential biomarkers of CKD.
Inflammation and CKD are closely related, since inflammation can lead to damage of the kidney tissue or exacerbate existing kidney damage and hasten the progression of the disease. Therefore, the study and identification of related biomarkers is essential in the prevention and management of kidney disease. Inflammatory biomarkers such as C-Reactive Protein (CRP) [87], Interleukin-6 (IL-6) [87][88], Tumor Necrosis Factor-alpha (TNF-α)[87][88][89], and Growth Differentiation Factor-15 (GDF-15) [87][90] have been linked to renal function decline. Regarding urinary tract infections, some proteins have been identified as potential biomarkers, such as lactoferrin (LF) [91], xanthine oxidase (XO), and myeloperoxidase (MPO) [32].
Urine protein biomarkers have also shown promise in the detection and monitoring of certain types of cancer. These biomarkers are often produced by cancer cells or by the body in response to cancer. An example is Prostate-Specific Antigen (PSA), which is a protein produced by the prostate gland, whose elevated urinary levels may indicate prostate cancer [68]. Other biomarkers that are being studied for their potential use in the detection and monitoring of cancer include Human Epididymis Protein 4 (HE4) for ovarian cancer [92], Bladder Tumor Antigen (BTA) for bladder cancer [93], and Matrix Metalloproteinases (MMPs) for various types of cancer, such as MMP-9 for breast cancer [94].
The diagnosis and monitoring of neurodegenerative diseases, such as Alzheimer’s disease, using urine biomarkers have been studied over time. One such biomarker is the Beta-Amyloid (βA) protein, which is detected in the urine. Beta-amyloid is a protein that is present in the brains of individuals with Alzheimer’s disease, and may also be present in the urine of these individuals [95].
Other urinary biomarkers can be associated with AD, such as AD-associated Neuronal Thread Protein (AD7c-NTP) [96], osteopontin, gelsolin, and Insulin-like Growth Factor-Binding Protein 7 (IGF BP7) [35].

3. Urine Nucleic Acids as Biomarkers

In addition to hundreds of proteins, urine also contains exfoliated tumor cells and tumor cell-free amino acids, in addition to tumor-derived DNA, mRNA, and microRNA (miRNA) [97][98].
In recent years, there has been growing interest in the use of urinary methylation-based biomarkers for the diagnosis and monitoring of some types of urogenital cancers, particularly in their early stages, as alterations in DNA methylation are thought to be among the earliest events in the development of tumors. According to Bryzgunova et al., methylation of Glutathione S-Transferase P1 (GSTP1) shows potential as a promising biomarker for prostate cancer, which can be detected in urine samples from affected patients [99].
Apart from alterations in DNA methylation, messenger RNA (mRNA) molecules in urine can be used as biomarkers. An example of that is the two available tests, SelectMDx and ExoDx, for the detection of prostate cancer. The former detects Distal-Less Homeobox 1 (DLX1) and Homeobox C6 (HOXC6) mRNA in urine after prostate massage, while the latter detects Prostate Cancer Antigen 3 (PCA3), Erythroblast Transformation-Specific (ETS)-related gene (ERG) and Sterile alpha Motifpointed Domain-Containing ETS transcription Factor (SPDEF) in urinary exosomes and does not require a digital rectal exam [100][101].
Feng et al. investigated the potential use of C-C Motif Chemokine Ligand 5 (CCL5) and C-X-C Motif Chemokine Ligand 1 (CXCL1) mRNA levels in urinary sediment as prognostic biomarkers for diabetic nephropathy. The results showed that both CCL5 and CXCL1 were upregulated in diabetic nephropathy patients and were associated with a decline in renal function [102]. In addition, low levels of CD2-associated protein (CD2AP) mRNA in urinary exosomes were associated with an increased risk of kidney disease [103].
The concept of liquid biopsy, which involves the detection, analysis, and monitoring of cancer through various bodily fluids such as urine [104], was initially introduced for circulating tumor cells but has since been extended to include circulating tumor DNA [105]. Circulating tumor DNA has been studied as a potential biomarker for various types of cancer, with a particular focus on genitourinary tract cancers, such as bladder cancer. Regarding bladder cancer, Christensen et al. were able to detect urinary cell-free DNA, specifically targeting three hotspot mutations in Phosphatidylinositol-4,5-bisphosphate 3-Kinase Catalytic subunit Alpha, PIK3CA (E545K) and Fibroblast Growth Factor Receptor 3, and FGFR3 (S249C, Y373C) [106]. Other possible biomarkers were identified, such as Long Interspersed Nuclear Element-1 (LINE1) [107] for lung cancer, p53 mutation (codon 249) for hepatic cancer [108], and vimentin hypermethylation for colorectal cancer (Song2012 [109]).
MicroRNA (miRNAs) are small, typically 20–25 nucleotides in length, non-coding RNA molecules that play a crucial role in the regulation of gene expression [98]. miRNAs have emerged as promising biomarkers for the diagnosis and monitoring of various types of cancer. In prostate cancer, several miRNA were identified, such as miR-107, miR-574-3p [110], miR-205, miR-214 [111], and miR-888 [112]. Other miRNA biomarkers that are being studied for their potential use in the detection and monitoring of cancer include miR-144-5p [113], miR-23b/27b [114], and miR-145 [113] for bladder cancer, and miR-96 and miR-214 [115] for urothelial cancer.
Previous studies showed that urinary levels of microRNA correlated with kidney diseases. Lv et al. [116] concluded that miR-29c from urinary exosomes was significantly downregulated in CKD patients. In a study conducted by Szeto et al. [117], miRNA levels were measured in urinary sediment. The researchers observed a correlation between the expression of urinary miR-21 and miR-216a and the rate of decline in renal function, as well as the risk of developing renal failure requiring dialysis [117]. In most cases, kidney diseases are linked with cardiovascular diseases, such as renovascular hypertension, which is high blood pressure caused by renal artery disease. Through their studies, Yang et al. and Know et al. have identified miR-26A [118] and miR-21, miR-93, and miR-200b [119], respectively, as potential markers for diagnosing renovascular hypertension.
Various urine nucleic acid biomarkers described previously are currently under study. Consequently, there are no normal reference values yet and further clinical validation is warranted.

This entry is adapted from the peer-reviewed paper 10.3390/biomedicines11041051

References

  1. World Health Organization; Safety IPOC. Biomarkers in Risk Assessment: Validity and Validation; World Health Organization: Geneva, Switzerland, 2001.
  2. Shere, A.; Eletta, O.; Goyal, H. Circulating blood biomarkers in essential hypertension: A literature review. J. Lab. Precis. Med. 2017, 2, 99.
  3. Pierce, J.D.; McCabe, S.; White, N.; Clancy, R.L. Biomarkers: An important clinical assessment tool. Am. J. Nurs. 2012, 112, 52–58.
  4. Li, A.J.; Martinez-Moral, M.P.; Kannan, K. Variability in urinary neonicotinoid concentrations in single-spot and first-morning void and its association with oxidative stress markers. Environ. Int. 2020, 135, 105415.
  5. Bouatra, S.; Aziat, F.; Mandal, R.; Guo, A.C.; Wilson, M.R.; Knox, C.; Bjorndahl, T.C.; Krishnamurthy, R.; Saleem, F.; Liu, P.; et al. The human urine metabolome. PLoS ONE 2013, 8, e73076.
  6. Park, S.-m.; Won, D.D.; Lee, B.J.; Escobedo, D.; Esteva, A.; Aalipour, A.; Ge, T.J.; Kim, J.H.; Suh, S.; Choi, E.H. A mountable toilet system for personalized health monitoring via the analysis of excreta. Nat. Biomed. Eng. 2020, 4, 624–635.
  7. Zhao, H.; Liu, Y.; Li, Z.; Song, Y.; Cai, X.; Zhang, T.; Yang, L.; Li, L.; Gao, S.; Li, Y.; et al. Identification of essential hypertension biomarkers in human urine by non-targeted metabolomics based on UPLC-Q-TOF/MS. Clin. Chim. Acta 2018, 486, 192–198.
  8. Jayavelu, N.D.; Bar, N.S. Metabolomic studies of human gastric cancer: Review. World J. Gastroenterol. 2014, 20, 8092–8101.
  9. Fitzpatrick, M.; Young, S.P. Metabolomics--a novel window into inflammatory disease. Swiss Med. Wkly. 2013, 143, w13743.
  10. Miller, I.J.; Peters, S.R.; Overmyer, K.A.; Paulson, B.R.; Westphall, M.S.; Coon, J.J. Real-time health monitoring through urine metabolomics. NPJ Digit. Med. 2019, 2, 109.
  11. Chachaj, A.; Matkowski, R.; Gröbner, G.; Szuba, A.; Dudka, I. Metabolomics of Interstitial Fluid, Plasma and Urine in Patients with Arterial Hypertension: New Insights into the Underlying Mechanisms. Diagnostics 2020, 10, 936.
  12. Wang, T.J.; Gona, P.; Larson, M.G.; Levy, D.; Benjamin, E.J.; Tofler, G.H.; Jacques, P.F.; Meigs, J.B.; Rifai, N.; Selhub, J. Multiple biomarkers and the risk of incident hypertension. Hypertension 2007, 49, 432–438.
  13. Loo, R.L.; Zou, X.; Appel, L.J.; Nicholson, J.K.; Holmes, E. Characterization of metabolic responses to healthy diets and association with blood pressure: Application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a randomized controlled study. Am. J. Clin. Nutr. 2018, 107, 323–334.
  14. Rox, K.; Rath, S.; Pieper, D.H.; Vital, M.; Brönstrup, M. A simplified LC-MS/MS method for the quantification of the cardiovascular disease biomarker trimethylamine- N-oxide and its precursors. J. Pharm. Anal. 2021, 11, 523–528.
  15. Yu, D.; Shu, X.O.; Rivera, E.S.; Zhang, X.; Cai, Q.; Calcutt, M.W.; Xiang, Y.B.; Li, H.; Gao, Y.T.; Wang, T.J.; et al. Urinary Levels of Trimethylamine-N-Oxide and Incident Coronary Heart Disease: A Prospective Investigation Among Urban Chinese Adults. J. Am. Heart Assoc. 2019, 8, e010606.
  16. Schiattarella, G.G.; Sannino, A.; Toscano, E.; Giugliano, G.; Gargiulo, G.; Franzone, A.; Trimarco, B.; Esposito, G.; Perrino, C. Gut microbe-generated metabolite trimethylamine-N-oxide as cardiovascular risk biomarker: A systematic review and dose-response meta-analysis. Eur. Heart J. 2017, 38, 2948–2956.
  17. Pizzino, G.; Irrera, N.; Cucinotta, M.; Pallio, G.; Mannino, F.; Arcoraci, V.; Squadrito, F.; Altavilla, D.; Bitto, A. Oxidative Stress: Harms and Benefits for Human Health. Oxidative Med. Cell. Longev. 2017, 2017, 8416763.
  18. Betteridge, D.J. What is oxidative stress? Metabolism 2000, 49, 3–8.
  19. Tejchman, K.; Kotfis, K.; Sieńko, J. Biomarkers and Mechanisms of Oxidative Stress-Last 20 Years of Research with an Emphasis on Kidney Damage and Renal Transplantation. Int. J. Mol. Sci. 2021, 22, 8010.
  20. Chatterjee, S. Oxidative stress, inflammation, and disease. In Oxidative Stress and Biomaterials; Elsevier: Amsterdam, The Netherlands, 2016; pp. 35–58.
  21. Lugrin, J.; Rosenblatt-Velin, N.; Parapanov, R.; Liaudet, L. The role of oxidative stress during inflammatory processes. Biol. Chem. 2014, 395, 203–230.
  22. Selvaraju, V.; Ayine, P.; Fadamiro, M.; Babu, J.R.; Brown, M.; Geetha, T. Urinary biomarkers of inflammation and oxidative stress are elevated in obese children and correlate with a marker of endothelial dysfunction. Oxidative Med. Cell. Longev. 2019, 2019, 9604740.
  23. Martinez-Moral, M.-P.; Kannan, K. Allantoin as a marker of oxidative stress: Inter-and intraindividual variability in urinary concentrations in healthy individuals. Environ. Sci. Technol. Lett. 2019, 6, 283–288.
  24. Kim, Y.J.; Huh, I.; Kim, J.Y.; Park, S.; Ryu, S.H.; Kim, K.B.; Kim, S.; Park, T.; Kwon, O. Integration of Traditional and Metabolomics Biomarkers Identifies Prognostic Metabolites for Predicting Responsiveness to Nutritional Intervention against Oxidative Stress and Inflammation. Nutrients 2017, 9, 233.
  25. Graille, M.; Wild, P.; Sauvain, J.J.; Hemmendinger, M.; Guseva Canu, I.; Hopf, N.B. Urinary 8-isoprostane as a biomarker for oxidative stress. A systematic review and meta-analysis. Toxicol. Lett. 2020, 328, 19–27.
  26. Vaidya, S.R.; Aeddula, N.R. Chronic Renal Failure; StatPearls: Treasure Island, FL, USA, 2022.
  27. Lousa, I.; Reis, F.; Beirão, I.; Alves, R.; Belo, L.; Santos-Silva, A. New Potential Biomarkers for Chronic Kidney Disease Management-A Review of the Literature. Int. J. Mol. Sci. 2020, 22, 43.
  28. Edelstein, C.L. Characteristics of an Ideal Biomarker of Kidney Diseases. In Biomarkers of Kidney Disease; Academic press: Cambridge, MA, USA, 2016.
  29. Bidin, M.Z.; Shah, A.M.; Stanslas, J.; Seong, C.L.T. Blood and urine biomarkers in chronic kidney disease: An update. Clin. Chim. Acta 2019, 495, 239–250.
  30. Jhang, J.-F.; Kuo, H.-C. Recent advances in recurrent urinary tract infection from pathogenesis and biomarkers to prevention. Tzu Chi Med. J. 2017, 29, 131.
  31. Karlsen, H.; Dong, T. Biomarkers of urinary tract infections: State of the art, and promising applications for rapid strip-based chemical sensors. Anal. Methods 2015, 7, 7961–7975.
  32. Masajtis-Zagajewska, A.; Nowicki, M. New markers of urinary tract infection. Clin. Chim. Acta 2017, 471, 286–291.
  33. Gregson, D.B.; Wildman, S.D.; Chan, C.C.; Bihan, D.G.; Groves, R.A.; Rydzak, T.; Pittman, K.; Lewis, I.A. Metabolomics strategy for diagnosing urinary tract infections. medRxiv 2021.
  34. Hrubešová, K.; Fousková, M.; Habartová, L.; Fišar, Z.; Jirák, R.; Raboch, J.; SETNIčKA, V. Search for biomarkers of Alzheimer‘s disease: Recent insights, current challenges and future prospects. Clin. Biochem. 2019, 72, 39–51.
  35. Yao, F.; Hong, X.; Li, S.; Zhang, Y.; Zhao, Q.; Du, W.; Wang, Y.; Ni, J. Urine-Based Biomarkers for Alzheimer’s Disease Identified Through Coupling Computational and Experimental Methods. J. Alzheimer’s Dis. 2018, 65, 421–431.
  36. An, M.; Gao, Y. Urinary Biomarkers of Brain Diseases. Genom. Proteom. Bioinform. 2015, 13, 345–354.
  37. Rani, P.; Vivek, S.; Ram, S.M. A Systematic Review on Urinary Biomarkers for Early Diagnosis of Alzheimer’s Disease (AD). Int. J. Nutr. Pharmacol. Neurol. Dis. 2020, 10, 91.
  38. Seol, W.; Kim, H.; Son, I. Urinary Biomarkers for Neurodegenerative Diseases. Exp. Neurobiol. 2020, 29, 325–333.
  39. Wu, W.; Venugopalan, J.; Wang, M.D. 11C-PIB PET image analysis for Alzheimer’s diagnosis using weighted voting ensembles. In Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Republic of Korea, 11–15 July 2017; pp. 3914–3917.
  40. Bao, W.; Xie, F.; Zuo, C.; Guan, Y.; Huang, Y.H. PET neuroimaging of Alzheimer’s disease: Radiotracers and their utility in clinical research. Front. Aging Neurosci. 2021, 13, 624330.
  41. van Oostveen, W.M.; de Lange, E.C.M. Imaging Techniques in Alzheimer’s Disease: A Review of Applications in Early Diagnosis and Longitudinal Monitoring. Int. J. Mol. Sci. 2021, 22, 2110.
  42. Yoshida, M.; Higashi, K.; Kuni, K.; Mizoi, M.; Saiki, R.; Nakamura, M.; Waragai, M.; Uemura, K.; Toida, T.; Kashiwagi, K.; et al. Distinguishing mild cognitive impairment from Alzheimer’s disease with acrolein metabolites and creatinine in urine. Clin. Chim. Acta 2015, 441, 115–121.
  43. Tsou, H.-H.; Hsu, W.-C.; Fuh, J.-L.; Chen, S.-P.; Liu, T.-Y.; Wang, H.-T. Alterations in acrolein metabolism contribute to Alzheimer’s disease. J. Alzheimer’s Dis. 2018, 61, 571–580.
  44. Cancer Facts & Figures. 2022. Available online: https://www.cancer.org/research/cancer-facts-statistics/all-cancer-facts-figures/cancer-facts-figures-2022.html (accessed on 16 January 2023).
  45. Bax, C.; Lotesoriere, B.J.; Sironi, S.; Capelli, L. Review and Comparison of Cancer Biomarker Trends in Urine as a Basis for New Diagnostic Pathways. Cancers 2019, 11, 1244.
  46. 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.
  47. An, Z.; Chen, Y.; Zhang, R.; Song, Y.; Sun, J.; He, J.; Bai, J.; Dong, L.; Zhan, Q.; Abliz, Z. Integrated ionization approach for RRLC-MS/MS-based metabonomics: Finding potential biomarkers for lung cancer. J. Proteome Res. 2010, 9, 4071–4081.
  48. 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.
  49. 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.
  50. Yang, Q.; Shi, X.; Wang, Y.; Wang, W.; He, H.; Lu, X.; Xu, G. Urinary metabonomic study of lung cancer by a fully automatic hyphenated hydrophilic interaction/RPLC-MS system. J. Sep. Sci. 2010, 33, 1495–1503.
  51. Li, J.; Guan, X.; Fan, Z.; Ching, L.M.; Li, Y.; Wang, X.; Cao, W.M.; Liu, D.X. Non-Invasive Biomarkers for Early Detection of Breast Cancer. Cancers 2020, 12, 2767.
  52. Park, J.; Shin, Y.; Kim, T.H.; Kim, D.-H.; Lee, A. Urinary Metabolites as Biomarkers for Diagnosis of Breast Cancer: A Preliminary Study. J. Breast Dis. 2019, 7, 44–51.
  53. Omran, M.M.; Rashed, R.E.; Darwish, H.; Belal, A.A.; Mohamed, F.Z. Development of a gas chromatography-mass spectrometry method for breast cancer diagnosis based on nucleoside metabolomes 1-methyl adenosine, 1-methylguanosine and 8-hydroxy-2′-deoxyguanosine. Biomed. Chromatogr. 2020, 34, e4713.
  54. Rashed, R.; Darwish, H.; Omran, M.; Belal, A.; Zahran, F. A novel serum metabolome score for breast cancer diagnosis. Br. J. Biomed. Sci. 2020, 77, 196–201.
  55. Woo, H.M.; Kim, K.M.; Choi, M.H.; Jung, B.H.; Lee, J.; Kong, G.; Nam, S.J.; Kim, S.; Bai, S.W.; Chung, B.C. Mass spectrometry based metabolomic approaches in urinary biomarker study of women’s cancers. Clin. Chim. Acta 2009, 400, 63–69.
  56. Seidel, A.; Brunner, S.; Seidel, P.; Fritz, G.I.; Herbarth, O. Modified nucleosides: An accurate tumour marker for clinical diagnosis of cancer, early detection and therapy control. Br. J. Cancer 2006, 94, 1726–1733.
  57. Slupsky, C.M.; Steed, H.; Wells, T.H.; Dabbs, K.; Schepansky, A.; Capstick, V.; Faught, W.; Sawyer, M.B. Urine metabolite analysis offers potential early diagnosis of ovarian and breast cancers. Clin. Cancer Res. 2010, 16, 5835–5841.
  58. Nam, H.; Chung, B.C.; Kim, Y.; Lee, K.; Lee, D. Combining tissue transcriptomics and urine metabolomics for breast cancer biomarker identification. Bioinformatics 2009, 25, 3151–3157.
  59. Srivastava, S.; Roy, R.; Singh, S.; Kumar, P.; Dalela, D.; Sankhwar, S.N.; Goel, A.; Sonkar, A.A. Taurine—A possible fingerprint biomarker in non-muscle invasive bladder cancer: A pilot study by 1H NMR spectroscopy. Cancer Biomark. 2010, 6, 11–20.
  60. Alberice, J.V.; Amaral, A.F.; Armitage, E.G.; Lorente, J.A.; Algaba, F.; Carrilho, E.; Márquez, M.; García, A.; Malats, N.; Barbas, C. Searching for urine biomarkers of bladder cancer recurrence using a liquid chromatography-mass spectrometry and capillary electrophoresis-mass spectrometry metabolomics approach. J. Chromatogr. A 2013, 1318, 163–170.
  61. Huang, Z.; Lin, L.; Gao, Y.; Chen, Y.; Yan, X.; Xing, J.; Hang, W. Bladder cancer determination via two urinary metabolites: A biomarker pattern approach. Mol. Cell. Proteom. 2011, 10, M111.007922.
  62. Wittmann, B.M.; Stirdivant, S.M.; Mitchell, M.W.; Wulff, J.E.; McDunn, J.E.; Li, Z.; Dennis-Barrie, A.; Neri, B.P.; Milburn, M.V.; Lotan, Y.; et al. Bladder cancer biomarker discovery using global metabolomic profiling of urine. PLoS ONE 2014, 9, e115870.
  63. Jin, X.; Yun, S.J.; Jeong, P.; Kim, I.Y.; Kim, W.J.; Park, S. Diagnosis of bladder cancer and prediction of survival by urinary metabolomics. Oncotarget 2014, 5, 1635–1645.
  64. Bianchi, F.; Dugheri, S.; Musci, M.; Bonacchi, A.; Salvadori, E.; Arcangeli, G.; Cupelli, V.; Lanciotti, M.; Masieri, L.; Serni, S.; et al. Fully automated solid-phase microextraction-fast gas chromatography-mass spectrometry method using a new ionic liquid column for high-throughput analysis of sarcosine and N-ethylglycine in human urine and urinary sediments. Anal. Chim. Acta 2011, 707, 197–203.
  65. Dereziński, P.; Klupczynska, A.; Sawicki, W.; Pałka, J.A.; Kokot, Z.J. Amino Acid Profiles of Serum and Urine in Search for Prostate Cancer Biomarkers: A Pilot Study. Int. J. Med. Sci. 2017, 14, 1–12.
  66. Jiang, Y.; Cheng, X.; Wang, C.; Ma, Y. Quantitative determination of sarcosine and related compounds in urinary samples by liquid chromatography with tandem mass spectrometry. Anal. Chem. 2010, 82, 9022–9027.
  67. Sreekumar, A.; Poisson, L.M.; Rajendiran, T.M.; Khan, A.P.; Cao, Q.; Yu, J.; Laxman, B.; Mehra, R.; Lonigro, R.J.; Li, Y.; et al. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression. Nature 2009, 457, 910–914.
  68. Wu, H.; Liu, T.; Ma, C.; Xue, R.; Deng, C.; Zeng, H.; Shen, X. GC/MS-based metabolomic approach to validate the role of urinary sarcosine and target biomarkers for human prostate cancer by microwave-assisted derivatization. Anal. Bioanal. Chem. 2011, 401, 635–646.
  69. Stabler, S.; Koyama, T.; Zhao, Z.; Martinez-Ferrer, M.; Allen, R.H.; Luka, Z.; Loukachevitch, L.V.; Clark, P.E.; Wagner, C.; Bhowmick, N.A. Serum methionine metabolites are risk factors for metastatic prostate cancer progression. PLoS ONE 2011, 6, e22486.
  70. Shamsipur, M.; Naseri, M.T.; Babri, M. Quantification of candidate prostate cancer metabolite biomarkers in urine using dispersive derivatization liquid–liquid microextraction followed by gas and liquid chromatography–mass spectrometry. J. Pharm. Biomed. Anal. 2013, 81, 65–75.
  71. Gkotsos, G.; Virgiliou, C.; Lagoudaki, I.; Sardeli, C.; Raikos, N.; Theodoridis, G.; Dimitriadis, G. The Role of Sarcosine, Uracil, and Kynurenic Acid Metabolism in Urine for Diagnosis and Progression Monitoring of Prostate Cancer. Metabolites 2017, 7, 9.
  72. Heger, Z.; Cernei, N.; Gumulec, J.; Masarik, M.; Eckschlager, T.; Hrabec, R.; Zitka, O.; Adam, V.; Kizek, R. Determination of common urine substances as an assay for improving prostate carcinoma diagnostics. Oncol. Rep. 2014, 31, 1846–1854.
  73. Fernández-Peralbo, M.; Gómez-Gómez, E.; Calderón-Santiago, M.; Carrasco-Valiente, J.; Ruiz-García, J.; Requena-Tapia, M.; Luque de Castro, M.; Priego-Capote, F. Prostate cancer patients–negative biopsy controls discrimination by untargeted metabolomics analysis of urine by LC-QTOF: Upstream information on other omics. Sci. Rep. 2016, 6, 38243.
  74. Jung, J.; Jung, Y.; Bang, E.J.; Cho, S.I.; Jang, Y.J.; Kwak, J.M.; Ryu, D.H.; Park, S.; Hwang, G.S. Noninvasive diagnosis and evaluation of curative surgery for gastric cancer by using NMR-based metabolomic profiling. Ann. Surg. Oncol. 2014, 21 (Suppl. S4), S736–S742.
  75. Chan, A.W.; Mercier, P.; Schiller, D.; Bailey, R.; Robbins, S.; Eurich, D.T.; Sawyer, M.B.; Broadhurst, D. (1)H-NMR urinary metabolomic profiling for diagnosis of gastric cancer. Br. J. Cancer 2016, 114, 59–62.
  76. Dong, L.M.; Shu, X.O.; Gao, Y.T.; Milne, G.; Ji, B.T.; Yang, G.; Li, H.L.; Rothman, N.; Zheng, W.; Chow, W.H.; et al. Urinary prostaglandin E2 metabolite and gastric cancer risk in the Shanghai women’s health study. Cancer Epidemiol. Biomark. Prev. 2009, 18, 3075–3078.
  77. Chen, J.L.; Fan, J.; Lu, X.J. CE-MS based on moving reaction boundary method for urinary metabolomic analysis of gastric cancer patients. Electrophoresis 2014, 35, 1032–1039.
  78. Key Statistics About Kidney Cancer. Available online: https://www.cancer.org/cancer/kidney-cancer/about/key-statistics.html (accessed on 16 January 2023).
  79. Gray, R.E.; Harris, G.T. Renal cell carcinoma: Diagnosis and management. Am. Fam. Physician 2019, 99, 179–184.
  80. Ganti, S.; Weiss, R.H. Urine metabolomics for kidney cancer detection and biomarker discovery. Urol. Oncol. 2011, 29, 551–557.
  81. Takase, H.; Sugiura, T.; Ohte, N.; Dohi, Y. Urinary Albumin as a Marker of Future Blood Pressure and Hypertension in the General Population. Medicine 2015, 94, e511.
  82. Cholongitas, E.; Goulis, I.; Ioannidou, M.; Soulaidopoulos, S.; Chalevas, P.; Akriviadis, E. Urine albumin-to-creatinine ratio is associated with the severity of liver disease, renal function and survival in patients with decompensated cirrhosis. Hepatol. Int. 2017, 11, 306–314.
  83. Lopez-Giacoman, S.; Madero, M. Biomarkers in chronic kidney disease, from kidney function to kidney damage. World J. Nephrol. 2015, 4, 57.
  84. George, J.A.; Gounden, V. Novel glomerular filtration markers. In Advances in Clinical Chemistry; Makowski, G.S., Ed.; Academic Press: Cambridge, MA, USA, 2019; Volume 88, pp. 91–119.
  85. Rysz, J.; Gluba-Brzózka, A.; Franczyk, B.; Jabłonowski, Z.; Ciałkowska-Rysz, A. Novel Biomarkers in the Diagnosis of Chronic Kidney Disease and the Prediction of Its Outcome. Int. J. Mol. Sci. 2017, 18, 1702.
  86. Zabetian, A.; Coca, S.G. Plasma and urine biomarkers in chronic kidney disease: Closer to clinical application. Curr. Opin. Nephrol. Hypertens. 2021, 30, 531.
  87. Prasad, S.; Tyagi, A.K.; Aggarwal, B.B. Detection of inflammatory biomarkers in saliva and urine: Potential in diagnosis, prevention, and treatment for chronic diseases. Exp. Biol. Med. 2016, 241, 783–799.
  88. Lee, B.T.; Ahmed, F.A.; Hamm, L.L.; Teran, F.J.; Chen, C.-S.; Liu, Y.; Shah, K.; Rifai, N.; Batuman, V.; Simon, E.E.; et al. Association of C-reactive protein, tumor necrosis factor-alpha, and interleukin-6 with chronic kidney disease. Bmc Nephrol. 2015, 16, 77.
  89. Liu, B.-C.; Zhang, L.; Lv, L.-l.; Wang, Y.-l.; Liu, D.-g.; Zhang, X.-l. Application of antibody array technology in the analysis of urinary cytokine profiles in patients with chronic kidney disease. Am. J. Nephrol. 2006, 26, 483–490.
  90. Nair, V.; Robinson-Cohen, C.; Smith, M.R.; Bellovich, K.A.; Bhat, Z.Y.; Bobadilla, M.; Brosius, F.; de Boer, I.H.; Essioux, L.; Formentini, I.; et al. Growth Differentiation Factor-15 and Risk of CKD Progression. J. Am. Soc. Nephrol. 2017, 28, 2233–2240.
  91. Arao, S.; Matsuura, S.; Nonomura, M.; Miki, K.; Kabasawa, K.; Nakanishi, H. Measurement of urinary lactoferrin as a marker of urinary tract infection. J. Clin. Microbiol. 1999, 37, 553–557.
  92. James, N.E.; Chichester, C.; Ribeiro, J.R. Beyond the Biomarker: Understanding the Diverse Roles of Human Epididymis Protein 4 in the Pathogenesis of Epithelial Ovarian Cancer. Front. Oncol. 2018, 8, 124.
  93. Jeong, S.H.; Ku, J.H. Urinary Markers for Bladder Cancer Diagnosis and Monitoring. Front. Cell Dev. Biol. 2022, 10, 892067.
  94. Fernández, C.A.; Yan, L.; Louis, G.; Yang, J.; Kutok, J.L.; Moses, M.A. The matrix metalloproteinase-9/neutrophil gelatinase-associated lipocalin complex plays a role in breast tumor growth and is present in the urine of breast cancer patients. Clin. Cancer Res. 2005, 11, 5390–5395.
  95. Takata, M.; Nakashima, M.; Takehara, T.; Baba, H.; Machida, K.; Akitake, Y.; Ono, K.; Hosokawa, M.; Takahashi, M. Detection of amyloid beta protein in the urine of Alzheimer’s disease patients and healthy individuals. Neurosci. Lett. 2008, 435, 126–130.
  96. Ghanbari, H.; Ghanbari, K.; Beheshti, I.; Munzar, M.; Vasauskas, A.; Averback, P. Biochemical assay for AD7C-NTP in urine as an Alzheimer’s disease marker. J. Clin. Lab. Anal. 1998, 12, 285–288.
  97. Alvarez, M.L.; Khosroheidari, M.; Ravi, R.K.; DiStefano, J.K. Comparison of protein, microRNA, and mRNA yields using different methods of urinary exosome isolation for the discovery of kidney disease biomarkers. Kidney Int. 2012, 82, 1024–1032.
  98. Cimmino, I.; Bravaccini, S.; Cerchione, C. Urinary biomarkers in tumors: An overview. Urin. Biomark. Methods Protoc. 2021, 2292, 3–15.
  99. Bryzgunova, O.E.; Morozkin, E.S.; Yarmoschuk, S.V.; Vlassov, V.V.; Laktionov, P.P. Methylation-specific sequencing of GSTP1 gene promoter in circulating/extracellular DNA from blood and urine of healthy donors and prostate cancer patients. Circ. Nucleic Acids Plasma Serum V 2008, 1137, 222–225.
  100. Fujita, K.; Nonomura, N. Urinary biomarkers of prostate cancer. Int. J. Urol. 2018, 25, 770–779.
  101. Carneiro, A.; Priante Kayano, P.; Gomes Barbosa, Á.; Langer Wroclawski, M.; Ko Chen, C.; Cavlini, G.C.; Reche, G.J.; Sanchez-Salas, R.; Tobias-Machado, M.; Sowalsky, A.G.; et al. Are localized prostate cancer biomarkers useful in the clinical practice? Tumor Biol. 2018, 40, 1010428318799255.
  102. Feng, S.-T.; Yang, Y.; Yang, J.-F.; Gao, Y.-M.; Cao, J.-Y.; Li, Z.-L.; Tang, T.-T.; Lv, L.-L.; Wang, B.; Wen, Y.; et al. Urinary sediment CCL5 messenger RNA as a potential prognostic biomarker of diabetic nephropathy. Clin. Kidney J. 2022, 15, 534–544.
  103. Lv, L.-L.; Cao, Y.-H.; Pan, M.-M.; Liu, H.; Tang, R.-N.; Ma, K.-L.; Chen, P.-S.; Liu, B.-C. CD2AP mRNA in urinary exosome as biomarker of kidney disease. Clin. Chim. Acta 2014, 428, 26–31.
  104. Poulet, G.; Massias, J.; Taly, V. Liquid Biopsy: General Concepts. Acta Cytol. 2019, 63, 449–455.
  105. Alix-Panabieres, C.; Pantel, K. Liquid Biopsy: From Discovery to Clinical Application. Cancer Discov. 2021, 11, 858–873.
  106. Christensen, E.; Birkenkamp-Demtroder, K.; Nordentoft, I.; Hoyer, S.; Van der Keur, K.; Van Kessel, K.; Zwarthoff, E.; Agerbaek, M.; Orntoft, T.F.; Jensen, J.B.; et al. Liquid biopsy analysis of FGFR3 and PIK3CA hotspot mutations for disease surveillance in bladder cancer. Cancer Res. 2017, 77, 961–969.
  107. Ren, S.; Ren, X.-D.; Guo, L.-F.; Qu, X.-M.; Shang, M.-Y.; Dai, X.-T.; Huang, Q. Urine cell-free DNA as a promising biomarker for early detection of non-small cell lung cancer. J. Clin. Lab. Anal. 2020, 34, e23321.
  108. Lin, S.Y.; Dhillon, V.; Jain, S.; Chang, T.-T.; Hu, C.-T.; Lin, Y.-J.; Chen, S.-H.; Chang, K.-C.; Song, W.; Yu, L.; et al. A Locked Nucleic Acid Clamp-Mediated PCR Assay for Detection of a p53 Codon 249 Hotspot Mutation in Urine. J. Mol. Diagn. 2011, 13, 474–484.
  109. Song, B.P.; Jain, S.; Lin, S.Y.; Chen, Q.; Block, T.M.; Song, W.; Brenner, D.E.; Su, Y.-H. Detection of Hypermethylated Vimentin in Urine of Patients with Colorectal Cancer. J. Mol. Diagn. 2012, 14, 112–119.
  110. Bryant, R.J.; Pawlowski, T.; Catto, J.W.F.; Marsden, G.; Vessella, R.L.; Rhees, B.; Kuslich, C.; Visakorpi, T.; Hamdy, F.C. Changes in circulating microRNA levels associated with prostate cancer. Br. J. Cancer 2012, 106, 768–774.
  111. Srivastava, A.; Goldberger, H.; Dimtchev, A.; Ramalinga, M.; Chijioke, J.; Marian, C.; Oermann, E.K.; Uhm, S.; Kim, J.S.; Chen, L.N.; et al. MicroRNA Profiling in Prostate Cancer—The Diagnostic Potential of Urinary miR-205 and miR-214. PLoS ONE 2013, 8, e76994.
  112. Lewis, H.; Lance, R.; Troyer, D.; Beydoun, H.; Hadley, M.; Orians, J.; Benzine, T.; Madric, K.; Semmes, O.J.; Drake, R.; et al. miR-888 is an expressed prostatic secretions-derived microRNA that promotes prostate cell growth and migration. Cell Cycle 2014, 13, 227–239.
  113. Matsushita, R.; Seki, N.; Chiyomaru, T.; Inoguchi, S.; Ishihara, T.; Goto, Y.; Nishikawa, R.; Mataki, H.; Tatarano, S.; Itesako, T.; et al. Tumour-suppressive microRNA-144-5p directly targets CCNE1/2 as potential prognostic markers in bladder cancer. Br. J. Cancer 2015, 113, 282–289.
  114. Chiyomaru, T.; Seki, N.; Inoguchi, S.; Ishihara, T.; Mataki, H.; Matsushita, R.; Goto, Y.; Nishikawa, R.; Tatarano, S.; Itesako, T.; et al. Dual regulation of receptor tyrosine kinase genes EGFR and c-Met by the tumor-suppressive microRNA-23b/27b cluster in bladder cancer. Int. J. Oncol. 2015, 46, 487–496.
  115. Yamada, Y.; Enokida, H.; Kojima, S.; Kawakami, K.; Chiyomaru, T.; Tatarano, S.; Yoshino, H.; Kawahara, K.; Nishiyama, K.; Seki, N.; et al. MiR-96 and miR-183 detection in urine serve as potential tumor markers of urothelial carcinoma: Correlation with stage and grade, and comparison with urinary cytology. Cancer Sci. 2011, 102, 522–529.
  116. Lv, L.-L.; Cao, Y.-H.; Ni, H.-F.; Xu, M.; Liu, D.; Liu, H.; Chen, P.-S.; Liu, B.-C. MicroRNA-29c in urinary exosome/microvesicle as a biomarker of renal fibrosis. Am. J. Physiol. Ren. Physiol. 2013, 305, F1220–F1227.
  117. Szeto, C.-C.; Ching-Ha, K.B.; Ka-Bik, L.; Mac-Moune, L.F.; Cheung-Lung, C.P.; Gang, W.; Kai-Ming, C.; Kam-Tao, L.P. Micro-RNA expression in the urinary sediment of patients with chronic kidney diseases. Dis. Mrk. 2012, 33, 137–144.
  118. Zhu, X.-Y.; Ebrahimi, B.; Eirin, A.; Woollard, J.R.; Tang, H.; Jordan, K.L.; Ofori, M.; Saad, A.; Herrmann, S.M.S.; Dietz, A.B.; et al. Renal Vein Levels of MicroRNA-26a Are Lower in the Poststenotic Kidney. J. Am. Soc. Nephrol. 2015, 26, 1378–1388.
  119. Kwon, S.H.; Tang, H.; Saad, A.; Woollard, J.R.; Lerman, A.; Textor, S.C.; Lerman, L.O. Differential Expression of microRNAs in Urinary Extracellular Vesicles Obtained From Hypertensive Patients. Am. J. Kidney Dis. 2016, 68, 331–332.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
This entry is offline, you can click here to edit this entry!
Video Production Service
Feedback