尿液蛋白质组作为下一代生物标志物的潜力

生物标志物是与机体生理及病理生理状态相关的可监测到变化的生化指标,尿液不属于内环境,没有稳态机制,能够积累并反映机体生理状态的早期变化,有潜力辅助疾病的早期诊断和预后监测。得益于非侵入性的收集方式,尿液可以被连续、大量、重复收集并便捷、稳定地保存,且组分相对简单,易于分析,是理想的标志物研究样本。但临床尿液样本蛋白质组可能会受到生活习惯、用药情况等多种混杂因素的影响,而动物模型方便控制变量,可以最大程度减少混杂因素的干扰,并使得在疾病发生、发展极早期采集样本成为可能;此外,患者的疾病分期、分型、用药情况等信息不能被忽视,现有样本策略和分析方式有待优化,例如,对同一个人不同时期、不同状态(例如患病前后)的尿液样本进行前后对照是一种理想的分析方式,这种方式能够消除个体间差异性的影响,符合个性化、精准化医疗的趋势;在无自身对照样本的情况下,一对多的分析方法能够更好地体现个体与健康群体的差别,辅助未知疾病的诊断和鉴别。尿液大分子的膜保存方式使得临床样本的保存更加简单经济。尿液生物标志物领域研究的进步需要政策和伦理的支持、资金和人力长期持续的投入以及大样本、大数据的辅助。本文综述了尿液生物标志物...     

Clinical Proteomics Identifies Urinary CD14 as a Potential Biomarker for Diagnosis of Stable Coronary Artery Disease

Inflammation plays a key role in coronary artery disease (CAD) and other manifestations of atherosclerosis. Recently, urinary proteins were found to be useful markers for reflecting inflammation status of different organs. To identify potential biomarker for diagnosis of CAD, we performed one-dimensional SDS-gel electrophoresis followed by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Among the proteins differentially expressed in urine samples, monocyte antigen CD14 was found to be consistently expressed in higher amounts in the CAD patients as compared to normal controls. Using enzyme-linked immunosorbent assays to analyze the concentrations of CD14 in urine and serum, we confirmed that urinary CD14 levels were significantly higher in patients (n = 73) with multi-vessel and single vessel CAD than in normal control (n = 35) (P < 0.001). Logistic regression analysis further showed that urinary CD14 concentration level is associated with severity or number of diseased vessels and SYNTAX score after adjustment for potential confounders. Concomitantly, the proportion of CD14+ monocytes was significantly increased in CAD patients (59.7 ± 3.6%) as compared with healthy controls (14.9 ± 2.1%) (P < 0.001), implicating that a high level of urinary CD14 may be potentially involved in mechanism(s) leading to CAD pathogenesis. By performing shotgun proteomics, we further revealed that CD14-associated inflammatory response networks may play an essential role in CAD. In conclusion, the current study has demonstrated that release of CD14 in urine coupled with more CD14+ monocytes in CAD patients is significantly correlated with severity of CAD, pointing to the potential application of urinary CD14 as a novel noninvasive biomarker for large-scale diagnostic screening of susceptible CAD patients.     

A Multi-Analyte Assay for the Non-Invasive Detection of Bladder Cancer

Accurate urinary assays for bladder cancer (BCa) detection would benefit both patients and healthcare systems. Through genomic and proteomic profiling of urine components, we have previously identified a panel of biomarkers that can outperform current urine-based biomarkers for the non-invasive detection of BCa. Herein, we report the diagnostic utility of various multivariate combinations of these biomarkers. We performed a case-controlled validation study in which voided urines from 127 patients (64 tumor bearing subjects) were analyzed. The urinary concentrations of 14 biomarkers (IL-8, MMP-9, MMP-10, SDC1, CCL18, PAI-1, CD44, VEGF, ANG, CA9, A1AT, OPN, PTX3, and APOE) were assessed by enzyme-linked immunosorbent assay (ELISA). Diagnostic performance of each biomarker and multivariate models were compared using receiver operating characteristic curves and the chi-square test. An 8-biomarker model achieved the most accurate BCa diagnosis (sensitivity 92%, specificity 97%), but a combination of 3 of the 8 biomarkers (IL-8, VEGF, and APOE) was also highly accurate (sensitivity 90%, specificity 97%). For comparison, the commercial BTA-Trak ELISA test achieved a sensitivity of 79% and a specificity of 83%, and voided urine cytology detected only 33% of BCa cases in the same cohort. These datashow that a multivariate urine-based assay can markedly improve the accuracy of non-invasive BCa detection. Further validation studies are under way to investigate the clinical utility of this panel of biomarkers for BCa diagnosis and disease monitoring.     

Applications of urinary proteomics in biomarker discovery

Urine is an important source of biomarkers. This article reviews current advances, major challenges, and future prospects in the field of urinary proteomics. Because the practical clinical problem is to distinguish diseases with similar symptoms, merely comparing samples from patients of a particular disease to those of healthy individuals is inadequate for finding biomarkers with sufficient diagnostic power. In addition, the variation of expression levels of urinary proteins among healthy individuals and individuals under different physiological conditions adds to the difficulty in identifying biomarkers. We propose that establishing the natural variation in urinary protein expression among a healthy population can serve as a reference to help identify protein abundance changes that are caused by disease, not by individual variations or physiological changes. We also discuss that comparing protein expression levels between urine and plasma may reveal the physiological function of the kidney and that may facilitate biomarker discovery. Finally, we propose that establishing a data-sharing platform for data collection and integrating results from all urinary biomarker studies will help promote the development of urinary proteomics.     

Platelet-Derived Growth Factor Receptor Beta: A Novel Urinary Biomarker for Recurrence of Non-Muscle-Invasive Bladder Cancer

Non-muscle-invasive bladder cancer (NMIBC) is one of the most common malignant tumors in the urological system with a high risk of recurrence, and effective non-invasive biomarkers for NMIBC relapse are still needed. The human urinary proteome can reflect the status of the microenvironment of the urinary system and is an ideal source for clinical diagnosis of urinary system diseases. Our previous work used proteomics to identify 1643 high-confidence urinary proteins in the urine from a healthy population. Here, we used bioinformatics to construct a cancer-associated protein-protein interaction (PPI) network comprising 16 high-abundance urinary proteins based on the urinary proteome database. As a result, platelet-derived growth factor receptor beta (PDGFRB) was selected for further validation as a candidate biomarker for NMIBC diagnosis and prognosis. Although the levels of urinary PDGFRB showed no significant difference between patients pre- and post-surgery (n = 185, P>0.05), over 3 years of follow-up, urinary PDGFRB was shown to be significantly higher in relapsed patients (n = 68) than in relapse-free patients (n = 117, P<0.001). The levels of urinary PDGFRB were significantly correlated with the risk of 3-year recurrence of NMIBC, and these levels improved the accuracy of a NMIBC recurrence risk prediction model that included age, tumor size, and tumor number (area under the curve, 0.862; 95% CI, 0.809 to 0.914) compared to PDGFR alone. Therefore, we surmise that urinary PDGFRB could serve as a non-invasive biomarker for predicting NMIBC recurrence.     

CCL18 in a multiplex urine-based assay for the detection of bladder cancer

The early detection of bladder cancer (BCa) is pivotal for successful patient treatment and management. Through genomic and proteomic studies, we have identified a number of bladder cancer-associated biomarkers that have potential clinical utility. In a case-control study, we examined voided urines from 127 subjects: 64 tumor-bearing subjects and 63 controls. The urine concentrations of the following proteins were assessed by enzyme-linked immunosorbent assay (ELISA); C-C motif chemokine 18 (CCL18), Plasminogen Activator Inhibitor 1 (PAI-1) and CD44. Data were compared to a commercial ELISA-based BCa detection assay (BTA-Trak?) and voided urinary cytology. We used analysis of the area under the curve of receiver operating characteristic curves to compare the ability of CCL18, PAI-1, CD44, and BTA to detect BCa in voided urine samples. Urinary concentrations of CCL18, PAI-1, and BTA were significantly elevated in subjects with BCa. CCL18 was the most accurate biomarker (AUC; 0.919; 95% confidence interval [CI], 0.8704-0.9674). Multivariate regression analysis highlighted CCL18 (OR; 18.31; 95% CI, 4.95-67.70, p<0.0001) and BTA (OR; 6.43; 95% CI, 1.86-22.21, p?=?0.0033) as independent predictors of BCa in voided urine samples. The combination of CCL18, PAI-1 and CD44 improved the area under the curve to 0.938. Preliminary results indicate that CCL18 was a highly accurate biomarker for BCa detection in this cohort. Monitoring CCL18 in voided urine samples has the potential to improve non-invasive tests for BCa diagnosis. Furthermore using the combination of CCL18, PAI-1 and CD44 may make the model more robust to errors to detect BCa over the individual biomarkers or BTA.     

Application of Label-free Quantitative Peptidomics for the Identification of Urinary Biomarkers of Kidney Chronic Allograft Dysfunction

The advent of quantitative proteomics opens new opportunities in biomedical and clinical research. Although quantitative proteomics methods based on stable isotope labeling are in general preferred for biomolecular research, biomarker discovery is a case example of a biomedical problem that may be better addressed by using label-free MS techniques. As a proof of concept of this paradigm, we report the use of label-free quantitative LC-MS to profile the urinary peptidome of kidney chronic allograft dysfunction (CAD). The aim was to identify predictive biomarkers that could be used to personalize immunosuppressive therapies for kidney transplant patients. We detected (by LC-M/MS) and quantified (by LC-MS) 6000 polypeptide ions in undigested urine specimens across 39 CAD patients and 32 control individuals. Although unsupervised hierarchical clustering differentiated between the groups when including all the identified peptides, specific peptides derived from uromodulin and kininogen were found to be significantly more abundant in control than in CAD patients and correctly identified the two groups. These peptides are therefore potential biomarkers that might be used for the diagnosis of CAD. In addition, ions at m/z 645.59 and m/z 642.61 were able to differentiate between patients with different forms of CAD with specificities and sensitivities of 90% in a training set and, significantly, of ∼70% in an independent validation set of samples. Interestingly low expression of uromodulin at m/z 638.03 coupled with high expression of m/z 642.61 diagnosed CAD in virtually all cases. Multiple reaction monitoring experiments further validated the results, illustrating the power of our label-free quantitative LC-MS approach for obtaining quantitative profiles of urinary polypeptides in a rapid, comprehensive, and precise fashion and for biomarker discovery.A major goal of clinical proteomics is to identify biomarkers that can aid in the diagnosis and prognosis of different conditions. In their ideal form, these biomarkers will not only assist the clinician in the diagnosis of a disease, but they will also give directions as to which therapy may be more appropriate for each patient, thus contributing to the development of personalized medicine. In this regard, urine represents an ideal, but yet largely unexplored, source of biomarkers because of the presence of large numbers of small peptides in this biological fluid and because it can be obtained non-invasively.However, although proteomics techniques are instrumental for increasing our understanding of molecular cell biology (1) the impact of proteomics in clinical practice has not yet reached initial expectations perhaps because of technological limitations (2, 3). Using hyphenated methods such as novel LC-MS techniques for quantitative proteomics (4, 5) may prove advantageous for the identification and validation of biomarkers (3, 6). This is because LC-MS allows the detection of proteomes with greater depth, dynamic range, and enhanced accuracy of quantization than when using one-dimensional profiling techniques that record all ions in a single mass spectrum, such as MALDI-TOF MS or SELDI-TOF MS (7). On-line LC-ESI-MS is quantitative in nature because the initial LC separation step contributes to reducing the amount of analytes that are simultaneously ionized, thus reducing the possibility of ion suppression, and because ion formation by electrospray ionization is proportional to analyte concentration (8, 9). Initial reports that used LC-MS for the analysis of the urinary proteome provided proof of principle of the use of this technique for the analysis of urinary polypeptides (10–12), and recently, using new generation LC-MS/MS instrumentation, more than 1500 proteins have been detected in urine (13). Nevertheless despite these advances in our understanding of the qualitative composition of the urinary proteome, precise and comprehensive quantification of urinary polypeptides to discover potential biomarkers remains a challenge.The ideal, and more widely used, strategies to derive quantitative information from LC-MS experiments are based on differential stable isotope labeling of proteins or peptides, which are then mixed and quantified relative to each other in single multidimensional LC-LC-MS experiments (14). This technique, however, is not ideal for biomarker discovery because of problems associated with protein derivatization in a clinical setting, because of its limited throughput, and because, although not impossible, isotope labeling techniques make it difficult to compare a large number of specimens; at present labeling reagents can be used for simultaneous comparison of up to eight protein samples (15).Novel analytical strategies for quantitative proteomics that do not require isotope labeling have been reported (4, 5, 16). These techniques can quantify polypeptides with precisions and accuracies comparable to those based on isotope labeling (17). In addition, such label-free quantitative LC-MS approaches can compare an unlimited number of samples, and it is therefore ideal for biomarker discovery as experimental designs normally involve comparing a large number of specimens to statistically validate the results. Thus, label-free quantitative LC-MS would clearly assist in analyzing the full potential of urine clinical samples as a source of disease biomarkers. The aim of the study presented herein was to prove this concept taking chronic allograft dysfunction (CAD)¹ as a paradigm.During the last years, the incidence and prevalence of end stage renal disease has increased worldwide (18). Successful renal transplantation improves the patients'' quality of life and increases survival as compared with long term dialysis treatment (19). However, despite these improvements, a substantial portion of grafts develop progressive dysfunction and fail within a decade even with the use of appropriate doses of immunosuppressive drugs to prevent acute rejection (20). CAD is responsible for more than 50% of graft losses and remains a central clinical challenge. Although patients can return to dialysis after transplant failure, loss of a functioning graft is associated with a 3-fold increase in the risk of death, a substantial decrease in quality of life for those who survive, and a 4-fold increase in healthcare costs (21).CAD is mediated by a combination of immune, ischemic, and inflammatory stimuli, and multiple pathways and mediators lead to cumulative structural damage to all compartments of the transplanted kidney. Sclerosing changes associated with tubulointerstitial injury are mediated by the processes of active fibrogenesis, resulting in epithelial loss and the phenotype of tubular atrophy and chronic interstitial fibrosis (22). Available diagnostic methods include clinical presentation, biochemical parameters, and biopsies. Currently the only non-invasive biomarker of CAD is serum creatinine and glomerular filtration rate (GFR), but neither is particularly sensitive or specific and may not reflect early alterations (20, 22). At present, biopsy allograft is regarded as the gold standard for the diagnosis of CAD allowing its early detection; however, this is a costly procedure that is associated with clinical complications (23).Clinicians are hence faced with a dilemma. On the one hand, protocol biopsies may detect rejection at an earlier subclinical stage and allow prompt initiation of treatment, which may translate into improved long term graft survival (24). On the other hand, this also implies that patients with preserved graft function, i.e. without CAD, undergo this invasive procedure unnecessarily. Therefore, identification of non-invasive biomarkers for the early diagnosis of CAD would be invaluable for alleviating the major health and economic burden that this condition causes to western countries (25).The aim of the present study was to evaluate whether the urinary peptidome, as analyzed by a novel analytical strategy based on label-free quantification of urinary polypeptides by LC-MS, would differentiate between patients with CAD, those showing stable renal transplant (SRT), and a group of living donors. To our knowledge, this represents the first study reporting urine polypeptide signatures and individual biomarkers that group patients according to their underlying renal phenotype and hence represent potential candidates for non-invasive diagnosis of CAD.     

Relationship of urinary isoprostanes to prostate cancer occurence

To estimate the oxidative stress in patients with prostate cancer and in a control group, we used the biomarker of lipid peroxidation?Cisoprostanes (8-isoPGF₂) and the level of selected antioxidants (glucose and uric acid [UA]). The level of urinary isoprostanes was determined in patients and controls using an immunoassay kit according to the manufacturer??s instruction. The levels of UA and glucose were also determined in serum by the use of UA Assay Kit and Glucose Assay Kit. We observed a statistically increased the level of isoprostanes in urine of patients with prostate cancer in compared with a control group. The concentration of tested antioxidants in blood from patients with prostate cancer was also higher than in healthy subjects. Moreover, our experiments indicate that the correlation between the increased amount of UA and the lipid peroxidation exists in prostate cancer patients (in all tested groups). Prostate cancer risk by urinary isoprostanes level was analyzed, and a positive association was found (relative risk for highest vs. lowest quartile of urinary isoprostanes?=?1.6; 95?% confidence interval 1.2?C2.4; p for trend?=?0.03). We suggest that reactive oxygen species induce peroxidation of unsaturated fatty acid in patients with prostate cancer, and the level of isoprostanes may be used as a non-invasive marker for determination of oxidative stress. We also propose that UA may enhance the oxidative stress in patients with prostate cancer.     

Urinary Neutrophil Gelatinase-Associated Lipocalin: A Useful Biomarker for Tacrolimus-Induced Acute Kidney Injury in Liver Transplant Patients

Tacrolimus is widely used as an immunosuppressant in liver transplantation, and tacrolimus-induced acute kidney injury (AKI) is a serious complication of liver transplantation. For early detection of AKI, various urinary biomarkers such as monocyte chemotactic protein-1, liver-type fatty acid-binding protein, interleukin-18, osteopontin, cystatin C, clusterin and neutrophil gelatinase-associated lipocalin (NGAL) have been identified. Here, we attempt to identify urinary biomarkers for the early detection of tacrolimus-induced AKI in liver transplant patients. Urine samples were collected from 31 patients after living-donor liver transplantation (LDLT). Twenty recipients developed tacrolimus-induced AKI. After the initiation of tacrolimus therapy, urine samples were collected on postoperative days 7, 14, and 21. In patients who experienced AKI during postoperative day 21, additional spot urine samples were collected on postoperative days 28, 35, 42, 49, and 58. The 8 healthy volunteers, whose renal and liver functions were normal, were asked to collect their blood and spot urine samples. The urinary levels of NGAL, monocyte chemotactic protein-1 and liver-type fatty acid-binding protein were significantly higher in patients with AKI than in those without, while those of interleukin-18, osteopontin, cystatin C and clusterin did not differ between the 2 groups. The area under the receiver operating characteristics curve of urinary NGAL was 0.876 (95% confidence interval, 0.800–0.951; P<0.0001), which was better than those of the other six urinary biomarkers. In addition, the urinary levels of NGAL at postoperative day 1 (p = 0.0446) and day 7 (p = 0.0006) can be a good predictive marker for tacrolimus-induced AKI within next 6 days, respectively. In conclusion, urinary NGAL is a sensitive biomarker for tacrolimus-induced AKI, and may help predict renal event caused by tacrolimus therapy in liver transplant patients.     

Blood-Based Protein Biomarker Panel for the Detection of Colorectal Cancer

Background

The majority of colorectal cancer (CRC) cases are preventable by early detection and removal of precancerous polyps. Even though CRC is the second most common internal cancer in Australia, only 30 per cent of the population considered to have risk factors participate in stool-based test screening programs. Evidence indicates a robust, blood-based, diagnostic assay would increase screening compliance. A number of potential diagnostic blood-based protein biomarkers for CRC have been reported, but all lack sensitivity or specificity for use as a stand-alone diagnostic. The aim of this study was to identify and validate a panel of protein-based biomarkers in independent cohorts that could be translated to a reliable, non-invasive blood-based screening test.

Principal Findings

In two independent cohorts (n = 145 and n = 197), we evaluated seven single biomarkers in serum of CRC patients and age/gender matched controls that showed a significant difference between controls and CRC, but individually lack the sensitivity for diagnostic application. Using logistic regression strategies, we identified a panel of three biomarkers that discriminated between controls and CRC with 73% sensitivity at 95% specificity, when applied to either of the two cohorts. This panel comprised of Insulin like growth factor binding protein 2 (IGFBP2), Dickkopf-3 (DKK3), and Pyruvate kinase M2(PKM2).

Conclusions

Due to the heterogeneous nature of CRC, a single biomarker is unlikely to have sufficient sensitivity or specificity for use as a stand-alone diagnostic screening test and a panel of markers may be more effective. We have identified a 3 biomarker panel that has higher sensitivity and specificity for early stage (Stage I and -II) disease than the faecal occult blood test, raising the possibility for its use as a non-invasive blood diagnostic or screening test.     

A blood based 12-miRNA signature of Alzheimer disease patients

Background

Alzheimer disease (AD) is the most common form of dementia but the identification of reliable, early and non-invasive biomarkers remains a major challenge. We present a novel miRNA-based signature for detecting AD from blood samples.

Results

We apply next-generation sequencing to miRNAs from blood samples of 48 AD patients and 22 unaffected controls, yielding a total of 140 unique mature miRNAs with significantly changed expression levels. Of these, 82 have higher and 58 have lower abundance in AD patient samples. We selected a panel of 12 miRNAs for an RT-qPCR analysis on a larger cohort of 202 samples, comprising not only AD patients and healthy controls but also patients with other CNS illnesses. These included mild cognitive impairment, which is assumed to represent a transitional period before the development of AD, as well as multiple sclerosis, Parkinson disease, major depression, bipolar disorder and schizophrenia. miRNA target enrichment analysis of the selected 12 miRNAs indicates an involvement of miRNAs in nervous system development, neuron projection, neuron projection development and neuron projection morphogenesis. Using this 12-miRNA signature, we differentiate between AD and controls with an accuracy of 93%, a specificity of 95% and a sensitivity of 92%. The differentiation of AD from other neurological diseases is possible with accuracies between 74% and 78%. The differentiation of the other CNS disorders from controls yields even higher accuracies.

Conclusions

The data indicate that deregulated miRNAs in blood might be used as biomarkers in the diagnosis of AD or other neurological diseases.     

A stable panel comprising 18 urinary proteins in the human healthy population

Just as biomarkers specific for diseases, biomarkers indicative of healthy conditions are valuable for the early diagnosis, monitoring, and prognosis of diseases. Our study focused on discovering via proteomics a stable panel of urinary proteins in the human healthy population. Urine samples were collected three times during 4 months from 100 male and 100 female healthy donors and analyzed through four different fractionation techniques (i.e. in-gel, 2D-LC, OFFGEL, and mRP) coupled with HPLC-Chip-MS/MS. Thus, 1641 urinary proteins were identified with a high confidence, among which 70 exhibiting an intergender/day variation <0.25 were selected and matched with the previously published five largest urinary proteomes to get 56 candidate proteins. Next, a panel comprising 18 intact urinary proteins was constructed by comparing the urinary proteomes via SDS-PAGE and 2DE. Finally, such 18 urinary proteins were validated via enzyme-linked immunosorbent assay in eight healthy individuals. Most of these proteins had been related to multiple rather than to single diseases. Therefore, we surmise that this protein set could be used as a biomarker to assess the human health status. Further determinations of the normal fluctuations of the single urinary proteins in this series using samples from large numbers of healthy individuals are required prior to any application in clinical settings.     

Identification and Validation of Urinary Metabolite Biomarkers for Major Depressive Disorder

Major depressive disorder (MDD) is a widespread and debilitating mental disorder. However, there are no biomarkers available to aid in the diagnosis of this disorder. In this study, a nuclear magnetic resonance spectroscopy–based metabonomic approach was employed to profile urine samples from 82 first-episode drug-naïve depressed subjects and 82 healthy controls (the training set) in order to identify urinary metabolite biomarkers for MDD. Then, 44 unselected depressed subjects and 52 healthy controls (the test set) were used to independently validate the diagnostic generalizability of these biomarkers. A panel of five urinary metabolite biomarkers—malonate, formate, N-methylnicotinamide, m-hydroxyphenylacetate, and alanine—was identified. This panel was capable of distinguishing depressed subjects from healthy controls with an area under the receiver operating characteristic curve (AUC) of 0.81 in the training set. Moreover, this panel could classify blinded samples from the test set with an AUC of 0.89. These findings demonstrate that this urinary metabolite biomarker panel can aid in the future development of a urine-based diagnostic test for MDD.Major depressive disorder (MDD)¹ is a debilitating mental disorder affecting up to 15% of the general population and accounting for 12.3% of the global burden of disease (1, 2). Currently, the diagnosis of MDD still relies on the subjective identification of symptom clusters rather than empirical laboratory tests. The current diagnostic modality results in a considerable error rate (3), as the clinical presentation of MDD is highly heterogeneous and the current symptom-based method is not capable of adequately characterizing this heterogeneity (4). An approach that can be used to circumvent these limitations is to identify disease biomarkers to support objective diagnostic laboratory tests for MDD.Metabonomics, which can measure the small molecules in given biosamples such as plasma and urine without bias (5), has been extensively used to characterize the metabolic changes of diseases and thus facilitate the identification of novel disease-specific signatures as putative biomarkers (6–10). Nuclear magnetic resonance (NMR) spectroscopy–based metabonomic approaches characterized by sensitive, high-throughput molecular screening have been employed previously in identifying novel biomarkers for a variety of neuropsychiatric disorders, including stroke, bipolar disorder, and schizophrenia (11–13).Specifically with regard to MDD, several animal studies have already characterized the metabolic changes in the blood and urine (14–19). These studies provide valuable clues as to the pathophysiological mechanism of MDD. However, no study has been designed with the aim of diagnosing this disease. Recently, using an NMR-based metabonomic approach, this research group identified a unique plasma metabolic signature that enables the discrimination of MDD from healthy controls with both high sensitivity and specificity (20). These findings motivated further study on urinary diagnostic metabolite biomarkers for MDD, which would be more valuable from a clinical applicability standpoint, as urine can be more non-invasively collected. Moreover, previous studies have also demonstrated the feasibility of identifying diagnostic metabolite biomarkers of psychiatric disorders in the urine. For example, using an NMR-based metabonomics approach, Yap et al. (21) identified a unique urinary metabolite signature that clearly discriminated autism patients from healthy controls. As systemic metabolic disturbances have been observed in the urine of a depressed animal model, it is likely that diagnostic metabolite markers for MDD can be detected in human urine.Therefore, in this study, NMR spectroscopy combined with multivariate pattern recognition techniques were used to profile 82 first-episode drug-naïve MDD subjects and 82 healthy controls (the training set) in order to identify potential metabolite biomarkers for MDD. Furthermore, 44 unselected MDD subjects and 52 healthy controls (the test set) were employed to independently validate the diagnostic performance of these urinary metabolite biomarkers.     

Novel urinary biomarkers for diagnosing bipolar disorder

Bipolar disorder (BD) is a debilitating mental disorder. However, there are no biomarkers available to support objective laboratory testing for this disorder. Here, a nuclear magnetic resonance spectroscopy-based metabonomic method was used to characterize the urinary metabolic profiling of BD subjects and healthy controls in order to identify and validate urinary metabolite biomarkers for BD. Four metabolites, α-hydroxybutyrate, choline, isobutyrate, and N-methylnicotinamide, were defined as biomarkers. A combined panel of these four urinary metabolites could effectively discriminate between BD subjects and healthy controls, achieving an area under the receiver operating characteristic curve (AUC) of 0.89 in a training set (n = 60 BD patients and n = 62 controls). Moreover, this urinary biomarker panel was capable of discriminating blinded test samples (n = 26 BD patients and n = 34 controls) with an AUC of 0.86. These findings suggest that a urine-based laboratory test using these biomarkers may be useful in the diagnosis of BD.     

Urinary proteomics: towards biomarker discovery, diagnostics and prognostics

During recent years, the proteomics field has moved onward to clinical applications, particularly for biomarker discovery, diagnostics and prognostics of human diseases. The urine is one of the ideal clinical samples for such applications because it is readily available in almost all patients, and its collection is very simple and non-invasive. Urinary proteomics thus becomes one of the most interesting subdisciplines in the clinical proteomics area. This article highlights and updates recent progress in the urinary proteomics field for clinical applications.     

Identification of a Plasma Four-microRNA Panel as Potential Noninvasive Biomarker for Osteosarcoma

Background

Circulating microRNAs (miRNAs) are emerging as promising biomarkers for human cancer. Osteosarcoma is the most common human primary malignant bone tumor in children and young adults. The objective of this study was to investigate whether circulating miRNAs in plasma could be a useful biomarker for detecting osteosarcoma and monitoring tumor removal dynamics.

Methods

Plasma samples were obtained from 90 patients before surgery, 50 patients after one month of surgery, and 90 healthy individuals. The study was divided into three steps: First, initial screening of the profiles of circulating miRNAs in pooled plasma samples from healthy controls and pre-operative osteosarcoma patients using a TaqMan low density array (TLDA). Second, evaluation of miRNA concentration in individual plasma samples from 90 pre-operative osteosarcoma patients and 90 healthy controls by a quantitative real time PCR (qRT-PCR) assay. Third, evaluation of miRNA concentration in paired plasma samples from 50 pre- and post-operative osteosarcoma patients by qRT-PCR assay.

Results

Four plasma miRNAs including miR-195-5p, miR-199a-3p, miR-320a, and miR-374a-5p were significantly increased in the osteosarcoma patients. Receiver operating characteristics curve analysis of the combined populations demonstrated that the four-miRNA signature could discriminate cases from controls with an area under the curve of 0.9608 (95% CI 0.9307-0.9912). These 4 miRNAs were markedly decreased in the plasma after operation. In addition, circulating miR-195-5p and miR-199a-3p were correlated with metastasis status, while miR-199a-3p and miR-320a were correlated with histological subtype.

Conclusions

Our data suggest that altered levels of circulating miRNAs might have great potential to serve as novel, non-invasive biomarkers for osteosarcoma.     

A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case–control study

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers, with around 9% of patients surviving >5 years. Asymptomatic in its initial stages, PDAC is mostly diagnosed late, when already a locally advanced or metastatic disease, as there are no useful biomarkers for detection in its early stages, when surgery can be curative. We have previously described a promising biomarker panel (LYVE1, REG1A, and TFF1) for earlier detection of PDAC in urine. Here, we aimed to establish the accuracy of an improved panel, including REG1B instead of REG1A, and an algorithm for data interpretation, the PancRISK score, in additional retrospectively collected urine specimens. We also assessed the complementarity of this panel with CA19-9 and explored the daily variation and stability of the biomarkers and their performance in common urinary tract cancers.Methods and findingsClinical specimens were obtained from multiple centres: Barts Pancreas Tissue Bank, University College London, University of Liverpool, Spanish National Cancer Research Center, Cambridge University Hospital, and University of Belgrade. The biomarker panel was assayed on 590 urine specimens: 183 control samples, 208 benign hepatobiliary disease samples (of which 119 were chronic pancreatitis), and 199 PDAC samples (102 stage I–II and 97 stage III–IV); 50.7% were from female individuals. PDAC samples were collected from patients before treatment. The samples were assayed using commercially available ELISAs. Statistical analyses were performed using non-parametric Kruskal–Wallis tests adjusted for multiple comparisons, and multiple logistic regression. Training and validation datasets for controls and PDAC samples were obtained after random division of the whole available dataset in a 1:1 ratio. The substitution of REG1A with REG1B enhanced the performance of the panel to detect resectable PDAC. In a comparison of controls and PDAC stage I–II samples, the areas under the receiver operating characteristic curve (AUCs) increased from 0.900 (95% CI 0.843–0.957) and 0.926 (95% CI 0.843–1.000) in the training (50% of the dataset) and validation sets, respectively, to 0.936 in both the training (95% CI 0.903–0.969) and the validation (95% CI 0.888–0.984) datasets for the new panel including REG1B. This improved panel showed both sensitivity (SN) and specificity (SP) to be >85%. Plasma CA19-9 enhanced the performance of this panel in discriminating PDAC I–II patients from controls, with AUC = 0.992 (95% CI 0.983–1.000), SN = 0.963 (95% CI 0.913–1.000), and SP = 0.967 (95% CI 0.924–1.000). We demonstrate that the biomarkers do not show significant daily variation, and that they are stable for up to 5 days at room temperature. The main limitation of our study is the low number of stage I–IIA PDAC samples (n = 27) and lack of samples from individuals with hereditary predisposition to PDAC, for which specimens collected from control individuals were used as a proxy.ConclusionsWe have successfully validated our urinary biomarker panel, which was improved by substituting REG1A with REG1B. At a pre-selected cutoff of >80% SN and SP for the affiliated PancRISK score, we demonstrate a clinically applicable risk stratification tool with a binary output for risk of developing PDAC (‘elevated’ or ‘normal’). PancRISK provides a step towards precision surveillance for PDAC patients, which we will test in a prospective clinical study, UroPanc.

Silvana Debernardi and colleagues establish a clinical risk score and a biomarker panel for early detection of pancreatic cancer.     

Sex-Specific Urinary Biomarkers for Diagnosing Bipolar Disorder

Sex-based differences are prominent in affective disorders, but there are no biomarkers available to support sex-specific, laboratory-based diagnostics for male and female bipolar disorder (BD) patients. Here, a NMR-based metabonomic approach was used to preliminarily identify sex-specific urinary metabolite biomarkers for diagnosing male and female BD patients. A male-specific biomarker panel consisting of four metabolites (α-hydroxybutyrate, choline, formate, and N-methylnicotinamide) effectively discriminated between male BD and healthy controls (HC) subjects, achieving an area under the receiver operating characteristic curve (AUC) of 0.942. A female-specific biomarkers panel consisting of four metabolites (α-hydroxybutyrate, oxalacetate, acetone, and N-methylnicotinamide) effectively discriminated between female BD and HC subjects, achieving an AUC of 0.909. The male-specific biomarker panel displayed low discriminatory power in the female group, and the female-specific biomarker panel displayed low discriminatory power in the male group. Moreover, several other metabolites showed different trends between male and female BD subjects. These findings suggest that male and female BD patients have distinct biomarker fingerprints and that these two sex-specific biomarker panels may serve as effective diagnostic tools in distinguishing male and female BD patients from their healthy counterparts. Our work may provide a window into the mechanisms underlying the pathoetiology of BD in both men and women.     

A specific miRNA signature in the peripheral blood of glioblastoma patients

The prognosis of patients afflicted by glioblastoma remains poor. Biomarkers for the disease would be desirable in order to allow for an early detection of tumor progression or to indicate rapidly growing tumor subtypes requiring more intensive therapy. In this study, we investigated whether a blood-derived specific miRNA fingerprint can be defined in patients with glioblastoma. To this end, miRNA profiles from the blood of 20 patients with glioblastoma and 20 age- and sex-matched healthy controls were compared. Of 1158 tested miRNAs, 52 were significantly deregulated, as assessed by unadjusted Student's t-test at an alpha level of 0.05. Of these, two candidates, miR-128 (up-regulated) and miR-342-3p (down-regulated), remained significant after correcting for multiple testing by Benjamini-Hochberg adjustment with a p-value of 0.025. The altered expression of these two biomarkers was confirmed in a second cohort of glioblastoma patients and healthy controls by real-time PCR and validated for patients who had received neither radio- nor chemotherapy and for patients who had their glioblastomas resected more than 6 months ago. Moreover, using machine learning, a comprehensive miRNA signature was obtained that allowed for the discrimination between blood samples of glioblastoma patients and healthy controls with an accuracy of 81% [95% confidence interval (CI) 78-84%], specificity of 79% (95% CI 75-83%) and sensitivity of 83% (95% CI 71-85%). In summary, our proof-of-concept study demonstrates that blood-derived glioblastoma-associated characteristic miRNA fingerprints may be suitable biomarkers and warrant further exploration.     

Proteomic profiling of urine: implications for lupus nephritis

Introduction: Lupus nephritis (LN) is a common and significant manifestation, affecting 60% of adults and 80% of children with systemic lupus erythematosus, with up to 30% of patients progressing to end stage renal disease. There remains an unmet need for non-invasive markers of disease activity, damage, and response to therapy. In addition, non-invasive biomarkers that predict therapeutic efficacy are needed to enable cost-effective clinical trials of novel agents.

Areas covered: This review examines the methodological aspects of urinary proteomics, the role of proteome profiling in identifying promising urinary biomarkers in LN, and the translation of research findings into clinically useful tools in the management of LN.

Expert opinion: Targeted and unbiased proteomics have identified several promising urinary biomarkers that predict LN activity, damage (chronicity), and response to therapy. In particular, a combination of biologically plausible urinary biomarkers termed as RAIL (Renal Activity Index for Lupus) has emerged as an excellent predictor of LN activity as well as response to therapy, being able to predict efficacy within 3 months of therapy. If validated in additional large prospective studies, the RAIL biomarkers will transform the care of patients with LN, allowing for a personalized and predictive approach and improved outcomes.