Discovery Of Urinary Biomarkers Research Articles

OSCA-finder: Redefining the assay of kidney disease diagnostic through metabolomics and deep learning

Liquid chromatography-mass spectrometry (LC-MS) is a platform for urine and blood sample analysis. However, the high variability in the urine sample reduced the confidence of metabolite identification. Therefore, pre and post-calibration operations are inevitable to ensure an accurate urine biomarker analysis. In this study, the phenomenon of a higher creatinine concentration variable in ureteropelvic junction obstruction (UPJO) patient urine samples than in healthy people was revealed, indicating the urine biomarker discovery of UPJO patients is not adapted to the creatinine calibrate strategy. Therefore, we proposed a pipeline “OSCA-Finder” to reshape the urine biomarker analysis. First, to ensure a more stable peak shape and total ion chromatography, we applied the product of osmotic pressure and injection volume as a calibration principle and integrated it with an online mixer dilution. Therefore, we obtained the most peaks and identified more metabolites in a urine sample with peak area group CV<30%. A data-enhanced strategy was applied to reduce the overfit while training a neural network binary classifier with an accuracy of 99.9%. Finally, seven accurate urine biomarkers combined with a binary classifier were applied to distinguish UPJO patients from healthy people. The results show that the UPJO diagnostic strategy based on urine osmotic pressure calibration has more potential than ordinary strategies.

Novel Urinary Liquid Biopsy Biomarkers and Their Role in Detecting Genitourinary Cancers

As liquid biopsy attracts more attention for the clinical detection and diagnosis of cancer, the need to establish reliable biomarkers has emerged. Plasma has received extensive study. However, for genitourinary (GU) cancers, urine can be the ideal body fluid. Urine can be collected in large quantities for frequent biomarker analysis and disease monitoring with relative ease. New biomarker studies are of great importance due to the limitations of the present diagnostic tests used for cancer detection and monitoring. Recently, many promising studies have investigated the role of cell-free DNA, DNA methylation, extracellular RNAs, and exosome cargos as biomarkers for GU cancer detection. This review explores the recent literature on the discovery of novel urinary biomarkers and their utility in detecting GU cancers. In small-scale studies, several novel biomarkers have shown preliminary evidence of superior clinical sensitivity and specificity compared to conventional GU cancer screening methods. With the use of these new urinary biomarkers, routine non-invasive screening and tumor monitoring may be possible.

DIA mass spectrometry characterizes urinary proteomics in neonatal and adult donkeys

Health monitoring is critical for newborn animals due to their vulnerability to diseases. Urine can be not only a useful and non-invasive tool (free-catch samples) to reflect the physiological status of animals but also to help monitor the progression of diseases. Proteomics involves the study of the whole complement of proteins and peptides, including structure, quantities, functions, variations and interactions. In this study, urinary proteomics of neonatal donkeys were characterized and compared to the profiles of adult donkeys to provide a reference database for healthy neonatal donkeys. The urine samples were collected from male neonatal donkeys on their sixth to tenth days of life (group N) and male adult donkeys aging 4–6 years old (group A). Library-free data-independent acquisition (direct DIA) mass spectrometry-based proteomics were applied to analyze the urinary protein profiles. Total 2179 urinary proteins were identified, and 411 proteins were differentially expressed (P < 0.05) between the two groups. 104 proteins were exclusively expressed in group N including alpha fetoprotein (AFP), peptidase-mitochondrial processing data unit (PMPCB), and upper zone of growth plate and cartilage matrix associated (UCMA), which might be used to monitor the health status of neonatal donkeys. In functional analysis, some differentially expressed proteins were identified related to immune system pathways, which might provide more insight in the immature immunity of neonatal donkeys. To the best of our knowledge, this is the first time to report donkey urinary proteome and our results might provide reference for urinary biomarker discovery used to monitor and evaluate health status of neonatal donkeys.

Establishment of Quantitative Proteomics Platform for Urine Biomarker Discovery and Validation

Establishment of Quantitative Proteomics Platform for Urine Biomarker Discovery and Validation

Urinary protein biomarker panel predicts esophageal squamous carcinoma from control cases and other tumors.

Discovery of noninvasive urinary biomarkers for the early diagnosis of esophageal squamous carcinoma (ESCC). We conducted proteomic analyses of 499 human urine samples obtained from healthy individuals (n = 321) and ESCC (n = 83), bladder cancer (n = 17), breast cancer (n = 12), colorectal cancer (n = 16), lung cancer (n = 33) and thyroid cancer (n = 17) patients from multiple medical centers. Those samples were divided into a discovery set (n = 247) and an independent validation set (n = 157). Among urinary proteins identified in the comprehensive quantitative proteomics analysis, we selected a panel of three urinary biomarkers (ANXA1, S100A8, TMEM256), and established a logistic regression model in the discovery set that can correctly classify the majority of ESCC cases in the validation sets with the area under the curve (AUC) values of 0.825. This urinary biomarker panel not only discriminates ESCC patients from healthy individuals but also differentiates ESCC from other common tumors. Notably, the panel distinguishes stage I ESCC patients from healthy individuals with AUC values of 0.886. On the analysis of stage-specific biomarkers, another combination panel of protein (ANXA1, S100A8, SOD3, TMEM256) demonstrated a good AUC value of 0.792 for stage I ESCC. Urinary biomarker panel represents a promising auxiliary diagnostic tool for ESCC, including early-stage ESCC.

Extracellular Vesicle Proteome in Prostate Cancer: A Comparative Analysis of Mass Spectrometry Studies.

Molecular diagnostics based on discovery research holds the promise of improving screening methods for prostate cancer (PCa). Furthermore, the congregated information prompts the question whether the urinary extracellular vesicles (uEV) proteome has been thoroughly explored, especially at the proteome level. In fact, most extracellular vesicles (EV) based biomarker studies have mainly targeted plasma or serum. Therefore, in this study, we aim to inquire about possible strategies for urinary biomarker discovery particularly focused on the proteome of urine EVs. Proteomics data deposited in the PRIDE archive were reanalyzed to target identifications of potential PCa markers. Network analysis of the markers proposed by different prostate cancer studies revealed moderate overlap. The recent throughput improvements in mass spectrometry together with the network analysis performed in this study, suggest that a larger standardized cohort may provide potential biomarkers that are able to fully characterize the heterogeneity of PCa. According to our analysis PCa studies based on urinary EV proteome presents higher protein coverage compared to plasma, plasma EV, and voided urine proteome. This together with a direct interaction of the prostate gland and urethra makes uEVs an attractive option for protein biomarker studies. In addition, urinary proteome based PCa studies must also evaluate samples from bladder and renal cancers to assess specificity for PCa.

Ultrabright plasmonic fluor nanolabel-enabled detection of a urinary ER stress biomarker in autosomal dominant tubulointerstitial kidney disease.

Autosomal dominant tubulointerstitial kidney disease (ADTKD)-uromodulin (UMOD) is the most common nonpolycystic genetic kidney disease, but it remains unrecognized due to its clinical heterogeneity and lack of screening test. Moreover, the fact that the clinical feature is a poor predictor of disease outcome further highlights the need for the development of mechanistic biomarkers in ADTKD. However, low abundant urinary proteins secreted by thick ascending limb cells, where UMOD is synthesized, have posed a challenge for the detection of biomarkers in ADTKD-UMOD. In the CRISPR/Cas9-generated murine model and patients with ADTKD-UMOD, we found that immunoglobulin heavy chain-binding protein (BiP), an endoplasmic reticulum chaperone, was exclusively upregulated by mutant UMOD in the thick ascending limb and easily detected by Western blot analysis in the urine at an early stage of disease. However, even the most sensitive ELISA failed to detect urinary BiP in affected individuals. We therefore developed an ultrasensitive, plasmon-enhanced fluorescence-linked immunosorbent assay (p-FLISA) to quantify urinary BiP concentration by harnessing the newly invented ultrabright fluorescent nanoconstruct, termed "plasmonic Fluor." p-FLISA demonstrated that urinary BiP excretion was significantly elevated in patients with ADTKD-UMOD compared with unaffected controls, which may have potential utility in risk stratification, disease activity monitoring, disease progression prediction, and guidance of endoplasmic reticulum-targeted therapies in ADTKD.NEW & NOTEWORTHY Autosomal dominant tubulointerstitial kidney disease (ADTKD)-uromodulin (UMOD) is an underdiagnosed cause of chronic kidney disease (CKD). Lack of ultrasensitive bioanalytical tools has hindered the discovery of low abundant urinary biomarkers in ADTKD. Here, we developed an ultrasensitive plasmon-enhanced fluorescence-linked immunosorbent assay (p-FLISA). p-FLISA demonstrated that secreted immunoglobulin heavy chain-binding protein is an early urinary endoplasmic reticulum stress biomarker in ADTKD-UMOD, which will be valuable in monitoring disease progression and the treatment response in ADTKD.

Trends in urine biomarker discovery for urothelial bladder cancer: DNA, RNA, or protein?

Urothelial bladder cancer is a complex disease displaying a landscape of heterogenous molecular subtypes, mutation profiles and clinical presentations. Diagnosis and surveillance rely on flexible cystoscopy which has high accuracy, albeit accompanied by a high-cost burden for healthcare providers and discomfort for patients. Advances in “omic” technologies and computational biology have provided insights into the molecular pathogenesis of bladder cancer and provided powerful tools to identify markers for disease detection, risk stratification, and predicting responses to therapy. To date, numerous attempts have been made to discover and validate diagnostic biomarkers that could be deployed as an adjunct to the cystoscopic diagnosis and long-term surveillance of bladder cancer. We report a comprehensive literature analysis using PubMed to assess the changing trends in investigating DNA, RNA, or proteins as diagnostic urinary biomarkers over a period of 5 decades: 1970–2020. A gradual shift has been observed in research away from protein biomarkers to nucleic acids including different classes of RNA, and DNA methylation and mutation markers. Until 2000, publications involving protein biomarker discovery constituted 87% of the total number of research articles with DNA comprising 6% and RNA 7%. Since 2000 the proportion of protein biomarker articles has fallen to 40%, and DNA and RNA studies increased to 32% and 28%, respectively. Clearly research focus, perhaps driven by technological innovation, has shifted from proteins to nucleic acids. We optimistically hypothesise that, following thorough validation, a clinically useful detection test for bladder cancer based on a panel of DNA or RNA markers could become reality within 5–10 years.

A proteomic analysis of urine biomarkers in autism spectrum disorder

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by early-onset social-communication challenges, restricted and repetitive behaviors, or unusual sensory-motor behaviors. A lack of specific biomarkers hinders the early diagnosis and treatment of this disease in many children. This study analyzes and validates potential urinary biomarkers using mass spectrometry proteomics. Global proteomics profiles of urine from 19 ASD patients and 19 healthy control subjects were compared to identify significantly changed proteins. These proteins were validated with targeted proteomics using parallel reaction monitoring (PRM) in an independent validation set consisting of samples from 40 ASD patients and 38 healthy controls. A total of 34 significantly changed proteins were found in the discovery set, among which seven proteins were identified as potential biomarkers for ASD through PRM assays in the validation set. Of these seven proteins, immunoglobulin kappa variable 4–1, immunoglobulin kappa variable 3–20, and immunoglobulin lambda variable 1–51 were up-regulated, while ATP synthase F1 subunit alpha, 10 kDa heat shock protein, apolipoprotein C-III, and arylsulfatase F were down-regulated. Six of these seven proteins support previous findings that ASD is accompanied by altered immune response and lipid metabolism, as well as mitochondrial dysfunction. This study lays the groundwork for additional research using biomarkers to clinically diagnose ASD. The proteomics and PRM raw data of this study have been deposited under the accession number IPX0002592000 at iProX. SignificanceThis study identified 34 proteins in urine of ASD patients that were significantly changed from the urinary proteins of healthy subjects using LC-MS/MS-based proteomics in a discovery set. Seven of these proteins were validated by PRM analysis in an independent validation set. This report represents the first description of combined label-free quantitative proteomics and PRM analysis of targeted proteins for discovery of ASD urinary biomarkers. The results will be helpful for early diagnosis and can provide additional insight into the molecular mechanisms of ASD.

Protocols for Preparation and Mass Spectrometry Analysis of Clinical Urine Samples to Identify Candidate Biomarkers of Schistosoma-Associated Bladder Cancer.

Advances in mass spectrometry instrumentation have revolutionized analytical capability in clinical proteomics. In parallel, various sample preparation methods have been developed to try to address the inherent complexity and dynamic range of clinical samples, typically involving a combination of depletion of abundant proteins followed by extensive prefractionation. However, the depth of coverage routinely achieved in discovery proteomics experiments on peripheral fluids such as serum, still leaves something to be desired, especially if no depletion or prefractionation is done in order to increase the throughput of clinical samples. Remarkably, despite being an easily accessible, typically sterile and diagnostically rich clinical sample, urine is often overlooked and as such has received less development effort. As an ultrafiltrate of blood, urine contains proteins and protein fragments originating from all parts of the body which may have diagnostic or prognostic potential if accurately and reproducibly quantified. Here, we describe an efficient and simple method for the concentration of urine samples by methanol-chloroform precipitation and subsequent in-solution tryptic digestion prior to discovery or targeted mass spectrometry analysis. We exemplify this method by reference to the discovery of novel candidate urinary biomarkers of schistosomiasis. Importantly, the methods described here have been used to identify >1900 protein groups in human urine by label-free discovery proteomics, without requiring any prior depletion or prefractionation, making this approach amenable to high throughput clinical biomarker studies in many diseases.

Toward a validated diagnostic test with machine learning algorithm for interstitial cystitis

Diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) is difficult as there is no definitive test for IC/BPS. Instead, the diagnosis is based on urinary symptoms and cystoscopy may be recommended. However, cystoscopic diagnosis is associated with potentially exacerbating painful side effects and is highly subjective among physicians. Furthermore, IC/PBS symptoms overlap with symptoms of bladder cancer, urinary tract infection, or overactive bladder. As a result, many patients may go years without a correct diagnosis and proper disease management. The goal of our current IC/BPS research is to develop a simple diagnostic test based on several urine proteins called the IC-risk score (IC-RS). A machine learning (ML) algorithm uses this information to determine if a person has IC/BPS or not; if they have IC/BPS, whether their IC/BPS is characterized by Hunner's lesions. We are currently in the middle of a grant to collect urine samples from 1000 patients with IC/BPS and 1,000 normal controls from across the United States. We are using social media such as Twitter and Facebook and working with patient advocacy organizations to collect urine samples from across the country. We hope to validate the IC-RS and apply for regulatory approval. Having a validated diagnostic test for IC/BPS would be a major advancement to help urology patients. In addition, drug companies developing new drugs and therapies for IC/BPS would have a better way to determine who to include in their clinical trials, and possibly another way to measure if their drug or therapy is effective. We will hereby review the steps that have led us in urine biomarker discovery research from urine protein assessment to use crowdsourcing stakeholders participation to ML algorithm IC-RS score development.

Discovery of Urinary Biomarkers of Seaweed Intake Using Untargeted LC-MS Metabolomics in a Three-Way Cross-Over Human Study.

Seaweeds are a marine source rich in potentially bioactive components, and therefore have attracted attention since the middle of the twentieth century. Accurate and objective assessment of the intake of seaweeds to study their health effects is hampered by a lack of validated intake biomarkers. In this three-armed, randomized, cross-over study, an untargeted metabolomics approach was applied for discovering novel intake biomarkers. Twenty healthy participants (9 men and 11 women) were provided each of three test meals in a randomized order: 5 g of Laminaria digitate (LD), 5 g of Undaria pinnatifida (UP), or a control meal with energy-adjusted pea protein. Four urine samples and a 24 h pooled urine were collected along with blood samples at seven time-points. All samples were profiled by LC-ESI-QTOF-MS and the data were analyzed by univariate analysis and excretion kinetics to select putative intake biomarkers. In total, four intake biomarkers were selected from urine samples. They were identified as hydroxyl-dihydrocoumarin at Level III, loliolid glucuronide at level I, and isololiolid glucuronide at level II, while the last one remains unknown. Further identification and validation of these biomarkers by a cross-sectional study is essential to assess their specificity and robustness.

Urinary Biomarkers and Their Potential for the Non-Invasive Detection of Endometrial Cancer.

Endometrial cancer is the most common malignancy of the female genital tract and its incidence is rising in parallel with the mounting prevalence of obesity. Early diagnosis has great potential to improve outcomes as treatment can be curative, especially for early stage disease. Current tests and procedures for diagnosis are limited by insufficient accuracy in some and unacceptable levels of invasiveness and discomfort in others. There has, therefore, been a growing interest in the search for sensitive and specific biomarkers for endometrial cancer detection based on non-invasive sampling methodologies. Urine, the prototype non-invasive sample, is attractive for biomarker discovery as it is easily accessible and can be collected repeatedly and in quantity. Identification of urinary biomarkers for endometrial cancer detection relies on the excretion of systemic biomarkers by the kidneys or urinary contamination by biomarkers shed from the uterus. In this review, we present the current standing of the search for endometrial cancer urinary biomarkers based on cytology, genomic, transcriptomic, proteomic, and metabolomic platforms. We summarize the biomarker candidates and highlight the challenges inherent in urinary biomarker discovery. We review the various technologies with promise for biomarker detection and assess these novel approaches for endometrial cancer biomarker research.

Volatile organic compounds analysis optimization and biomarker discovery in urine of Non-Hodgkin lymphoma patients before and during chemotherapy

The Non-Hodgkin's lymphoma (NHL) is a severe and common neoplasm generated by metabolic changes due to cells mutations. Its prognostic or diagnosis seems therefore viable by monitoring metabolites produced and secreted into biological fluids or tissues. Hence, we used an untargeted metabolomics approach to evaluate if the profiles of volatile organic compounds (VOCs) present in urine would serve to Non-Hodgkin's lymphoma (NHL) prognostic or diagnostic. Headspace solid-phase microextraction (HS-SPME) was optimized and applied to pools of urine or serum samples from healthy patients after optimization using a Central Composite Design (CCD), followed by gas chromatography coupled to mass spectrometry (GC–MS) analysis. CCD factorial design 23 and the response surfaces indicated the optimized extraction parameters. Individual urine samples from healthy and diseased subjects were prepared and analyzed by the optimized method, and a thorough statistical analysis revealed several metabolites as potential biomarkers for NHL as well as able to describe the patient's metabolic profile before each chemotherapy cycle. This approach seems promising in contributing to the prognosis and choice of appropriate treatment of NHL for each individual.

Optimisation of urine sample preparation for shotgun proteomics

AbstractUrine reflects the renal function and urinary and kidney systems, but it may also reflect the presence of cancer in other parts of the body. Urine also has potential for providing prognostic information during therapeutic treatments thanks to non-invasive monitoring. A quick and reproducible protein purification procedure is essential to allow data comparison between proteomic studies in urine biomarker discovery. The article describes a simple, reproducible and cheap sample preparation procedure with a maximum protein yield (400 µg) obtained from only 10 mL of urine utilising cut-off filter desalting and digestion. The reported procedure removes yellowish background coloration residues and thus prevents the errors in spectrophotometric protein concentration determination. Different extraction solvents used in the presented procedure point to the possibility of partial elimination of abundant proteins (albumin and keratin family), as well as to the improvement of the sequence coverage of proteins identified, which helps to reveal changes in the urinary proteome. With this workflow, proteins can be easily obtained on standard laboratory equipment within 3 h. Data are available via ProteomeXchange with identifier PXD019738.

ITRAQ-based comparative proteomics analysis reveals specific urinary biomarkers for various kidney diseases.

Background: Proteome studies for multiple renal diseases is bare. Methodology & results: Using isobaric tags for relative and absolute quantitation labeling, many differentially expressed proteins (DEPs) were identified in acute kidney injury (AKI), AKI+chronic kidney disease (CKD), diabetic CKD and nondiabetic CKD with or without IgA nephropathy (IgAN). Comparative analysis indicated that 34, 35, 17, 91 and 14 unique DEPs were found in AKI, AKI + CKD, CKD, diabetic CKD and nondiabetic CKD. Compared with nondiabetic CKD with IgAN, 47 unique DEPs were found in that without IgAN. Serum amyloid A1 (SAA1) and hepatocyte growth factor activator were unregulated in AKI and nondiabetic CKD without IgAN, respectively. Regenerating islet-derived protein 3-α (Reg3A) upregulation is associated with AKI and AKI + CKD patients. Conclusion: This research contributes to urinary biomarker discovery from multiple renal diseases.

Ampholine immobilized polymer microspheres for increasing coverage of human urinary proteome

Urinary proteome, as an important component of body fluid proteome, could reflect kidney, urogenital tract function and pathological changes of human organs. This study reports a convenient strategy for urine proteome analysis through ampholine immobilized polymer microsphere (ampholine@PM) fractionation strategy. After ampholine@PM treatment, 16,543 unique peptides corresponding to 2173 non-redundant urinary proteins were identified, while only 856 proteins, corresponding to 3524 peptides were identified in the crude urine sample. The number of proteins and peptides was increased by 1.54 and 3.69 times, respectively. 31 urinary candidate biomarkers have also been identified (17 candidate biomarkers of glomerular injury and 14 candidate biomarkers of tubular injury), showing the potential of our strategy in urinary biomarker discovery study. In additional to the urine proteome, N-glycoproteome analysis was also performed after ampholine@PM fractionation followed by the N-glycopeptides enrichment. The number was increased from 144 to 281 for N-glycoproteins, 261 to 709 for N-glycopeptides, and 226 to 493 for N-glycosylation sites, after ampholine@PM treatment. Based on the significant increase on the identified N-glycoprotein number, ampholine@PM fractionation strategy also offered a beneficial tool for the post translational modification analysis of urine proteome.

Advances in urinary biomarker discovery in urological research.

A disease-specific biomarker (or biomarkers) is a characteristic reflecting a pathological condition in human body, which can be used as a diagnostic or prognostic tool for the clinical management. A urine-based biomarker(s) may provide a clinical value as attractive tools for clinicians to utilize in the clinical setting in particular to bladder diseases including bladder cancer and other bladder benign dysfunctions. Urine can be easily obtained by patients with no preparation or painful procedures required from patients' side. Currently advanced omics technologies and computational power identified potential omics-based novel biomarkers. An unbiased profiling based on transcriptomics, proteomics, epigenetics, metabolomics approaches et al. found that expression at RNA, protein, and metabolite levels are linked with specific bladder diseases and outcomes. In this review, we will discuss about the urine-based biomarkers reported by many investigators including us and how these biomarkers can be applied as a diagnostic and prognostic tool in clinical trials and patient care to promote bladder health. Furthermore, we will discuss how these promising biomarkers can be developed into a smart medical device and what we should be cautious about toward being used in real clinical setting.

A pilot investigation of a urinary metabolic biomarker discovery in renal cell carcinoma.

Renal cell carcinoma (RCC) is the most common and lethal malignancy of the kidney. Distinguishing RCC from benign renal tumors and healthy controls is still a clinical challenge. Urine metabolomics has been used to identify biomarkers of clinical diseases. In the present study, we explored the urine metabolomes of a cohort of 61 patients with renal tumors (39 RCC and 22 benign renal tumors) and 68 healthy controls using liquid chromatography coupled with high-resolution mass spectrometry (LC-MS). Metabolic profiling of urine could significantly differentiate RCC from healthy controls and benign renal tumors. Metabolic pathways, including lysine metabolism and phenylalanine metabolism, were found to be disturbed in the RCC group. Steroid hormone biosynthesis was significantly different between the benign tumor group and the RCC group. RCC biomarkers were further explored. A metabolite panel consisting of cortolone, testosterone and L-2-aminoadipate adenylate was discovered to have good ability of distinguishing RCC from benign tumors, with an AUC of 0.868 for tenfold cross-validation and 0.873 for the validation group. In addition, the panel of aminoadipic acid, 2-(formamido)-N1-(5-phospho-D-ribosyl) acetamidine and alpha-N-phenylacetyl-L-glutamine could distinguish the RCC group from the healthy control group, with an AUC of 0.841 for tenfold cross-validation and 0.894 for the validation group. This pilot study suggests that urine metabolomics may be useful in differentiating RCC from healthy controls and benign renal tumors.

Proteomic approaches in the discovery of potential urinary biomarkers of mucopolysaccharidosis type II

Mucopolysaccharindosis type II (MPS II) is a rare lysosomal storage disorder caused by deficient or absent activity of the iduronate-2-sulfatase (IDS) enzyme, which leads to pathological accumulation of the glycosaminoglycans(GAGs). The absence of early diagnosis can result in irreversible developmental, neurological, and physiological damage. The lack of clear understanding of the etiology of physiological dysfunction in MPS II has been a major obstacle to the development of new treatment. Therefore, a reliable biomarker for early diagnosis and exploration of pathogenic mechanism are of great importance. Proteomics provides powerful tool for protein expression alterations and study of complicated pathological process. This study was performed to identify the differential protein profile in urine of MPS II patients using two-dimensional gel electrophoresis(2D-PAGE)combining with MALDI-TOF/TOF and a total of 15 differentially expressed proteins were identified. Content of alpha1-antitrypsin, Gm2 activator and lipocalin-type prostaglandin D synthase was measured by ELISA method. The value of urinary α1-AT/Cr in MPS II group was 0.79 ± 0.10 mg/mmol, significantly higher than 0.42 ± 0.05 mg/mmol in healthy control group; whereas the value of GM2A/Cr and L-PGDS/Cr in MPS II group was 1.30 ± 0.12 μg/mmol and 9.86 ± 1.16 ng/mmol respectively, which was significantly lower than 2.19 ± 0.19 μg/mmol and 13.98 ± 1.48 ng/mmol in healthy control group. The proteins can be considered as accessory diagnostic biomarkers for MPS II. This approach helped to discover early diagnostic markers and provided a better understanding of the pathogenic mechanism of MPS II.