Candidate protein biomarkers in chronic kidney disease: a proteomics study.

Proteinuria poses a substantial risk for the progression of chronic kidney disease (CKD) and its related complications. Kidneys excrete hundreds of individual proteins, some with a potential impact on CKD progression or as a marker of the disease. However, the available data on specific urinary proteins and their relationship with CKD severity remain limited. Therefore, we aimed to investigate the urinary proteome and its association with kidney function in CKD patients and healthy controls. The proteomic analysis of urine samples showed CKD stage-specific differences in the number of detected proteins and the exponentially modified protein abundance index for total protein (p = 0.007). Notably, specific urinary proteins such as B2MG, FETUA, VTDB, and AMBP exhibited robust negative associations with kidney function in CKD patients compared to controls. Also, A1AG2, CD44, CD59, CERU, KNG1, LV39, OSTP, RNAS1, SH3L3, and UROM proteins showed positive associations with kidney function in the entire cohort, while LV39, A1BG, and CERU consistently displayed positive associations in patients compared to controls. This study suggests that specific urinary proteins, which were found to be negatively or positively associated with the kidney function of CKD patients, can serve as markers of dysfunctional or functional kidneys, respectively.

A candidate panel of eight urinary proteins shows potential of early diagnosis and risk assessment for diabetic kidney disease in type 1 diabetes.

Diabetic kidney disease (DKD) poses a significant health challenge for individuals with diabetes. At its initial stages, DKD often presents asymptomatically, and the standard for non-invasive diagnosis, the albumin-creatinine ratio (ACR), employs discrete categorizations (normal, microalbuminuria, macroalbuminuria) with limitations in sensitivity and specificity across diverse population cohorts. Single biomarker reliance further restricts the predictive value in clinical settings. Given the escalating prevalence of diabetes, our study uses proteomic technologies to identify novel urinary proteins as supplementary DKD biomarkers. A total of 158 T1D subjects provided urine samples, with 28 (15 DKD; 13 non-DKD) used in the discovery stage and 131 (45 DKD; 40 pDKD; 46 non-DKD) used in the confirmation. We identified eight proteins (A1BG, AMBP, AZGP1, BTD, RBP4, ORM2, GM2A, and PGCP), all of which demonstrated excellent area-under-the-curve (AUC) values (0.959 to 0.995) in distinguishing DKD from non-DKD. Furthermore, this multi-marker panel successfully segregated the most ambiguous group (microalbuminuria) into three distinct clusters, with 80% of subjects aligning either as DKD or non-DKD. The remaining 20% exhibited continued uncertainty. Overall, the use of these candidate urinary proteins allowed for the better classification of DKD and offered potential for significant improvements in the early identification of DKD in T1D populations.

OSTEO18, a novel urinary proteomic signature, associated with osteoporosis in heart transplant recipients.

Background: Immunosuppressive treatment in heart transplant (HTx) recipient causes osteoporosis. The urinary proteomic profile (UPP) includes peptide fragments derived from the bone extracellular matrix. Study aims were to develop and validate a multidimensional UPP biomarker for osteoporosis in HTx patients from single sequenced urinary peptides identifying the parent proteins. Methods: A single-center HTx cohort was analyzed. Urine samples were measured by capillary electrophoresis coupled with mass spectrometry. Cases with osteoporosis and matching controls were randomly selected from all available 389 patients. In derivation case-control dataset, 1576 sequenced peptides detectable in ≥30 % of patients. Applying statistical analysis on these, an 18-peptide multidimensional osteoporosis UPP biomarker (OSTEO18) was generated by support vector modeling. The 2 replication datasets included 118 and 94 patients. For further validation, the whole cohort was analyzed. Statistical methods included logistic regression and receiver operating characteristic curve (ROC) analysis. Results: In derivation dataset, the AUC, sensitivity and specificity of OSTEO18 were 0.83 (95 % CI: 0.76-0.90), 74.3 % and 87.1 %, respectively. In replication datasets, results were confirmatory. In the whole cohort (154 osteoporotic patients [39.6 %]), the ORs for osteoporosis increased (p < 0.0001) across OSTEO18 quartiles from 0.39 (95 % CI: 0.25-0.61) to 3.14 (2.08-4.75). With full adjustment for known osteoporosis risk factors, OSTEO18 improved AUC from 0.708 to 0.786 (p = 0.0003) for OSTEO18 categorized (optimized threshold: 0.095) and to 0.784 (p = 0.0004) for OSTEO18 as continuously distributed classifier. Conclusion: OSTEO18 is a clinically meaningful novel biomarker indicative of osteoporosis in HTx recipients and is being certified as in-vitro diagnostic.

Randomized Trial of SGLT2 Inhibitor Identifies Target Proteins in Diabetic Kidney Disease.

Introduction: Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have emerged as novel therapeutics to treat diabetic kidney disease (DKD). Although the beneficial effects of SGLT2i have been demonstrated, their target mechanisms on kidney function are unknown. The current study aimed to elucidate these mechanisms by studying SGLT2i-induced changes in the urinary proteome of persons with type 2 diabetes (T2D) and DKD. Methods: A total of 40 participants with T2D were enrolled in a double-blinded randomized cross-over trial at the Steno Diabetes Center Copenhagen, Denmark. They were treated with 10 mg of dapagliflozin for 12 weeks. Thirty-two participants with complete urinary proteomics measures before and after the trial were included. All participants received renin-angiotensin system blockade and had albuminuria, (urine albumin-to-creatinine ratio [UACR] ≥30 mg/g). A type 1 diabetes (T1D) cohort consisting of healthy controls and persons with DKD was included for validation. Urinary proteome changes were analyzed using Wilcoxon signed-rank test. Functional enrichment analysis was conducted to discover affected biological processes. Results: Dapagliflozin treatment significantly (Padjusted < 0.05) affected 36 urinary peptide fragments derived from 19 proteins. Eighteen proteins were correspondingly reflected in the validation cohort. A multifold change in peptide abundance was observed in many proteins (A1BG, urinary albumin [ALB], Caldesmon 1, COLCRNN, heat shock protein 90-ß [HSP90AB1], IGLL5, peptidase inhibitor 16 [PI16], prostaglandin-H2-D-isomerase [PTGDS], SERPINA1). These also included urinary biomarkers of kidney fibrosis and function (type I and III collagens and albumin). Biological processes relating to inflammation, wound healing, and kidney fibrosis were enriched. Conclusion: The current study discovers the urinary proteome impacted by the SGLT2i, thereby providing new potential target sites and pathways, especially relating to wound healing and inflammation.

Predicting autosomal dominant polycystic kidney disease progression: review of promising Serum and urine biomarkers.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the leading causes of end-stage renal disease. In spite of the recent tremendous progress in the understanding of ADPKD pathogenesis, the molecular mechanisms of the disease remain incompletely understood. Considering emerging new targeted therapies for ADPKD, it has become crucial to disclose easily measurable and widely available biomarkers for identifying patients with future rapid disease progression. This review encompasses all the research with a shared goal of identifying promising serum or urine biomarkers for predicting ADPKD progression or response to therapy. The rate of the ADPKD progress varies significantly between patients. The phenotypic variability is only partly explained by the underlying genetic lesion diversity. Considering significant decline in kidney function in ADPKD is not usually evident until at least 50% of the parenchyma has been destroyed, conventional kidney function measures, such as glomerular filtration rate (GFR), are not suitable for monitoring disease progression in ADPKD, particularly in its early stages. Since polycystic kidney enlargement usually precedes the decline in GFR, height-adjusted total kidney volume (ht-TKV) has been accepted as an early biomarker for assessing disease severity in ADPKD patients. However, since measuring ht-TKV is time-consuming and observer-dependent, the identification of a sensitive and quickly measurable biomarker is of a great interest for everyday clinical practice. Throughout the last decade, due to development of proteomic and metabolomic techniques and the enlightenment of multiple molecular pathways involved in the ADPKD pathogenesis, a number of urine and serum protein biomarkers have been investigated in ADPKD patients, some of which seem worth of further exploring. These include copeptin, angiotensinogen, monocyte chemoattractant protein 1, kidney injury molecule-1 and urine-to-plasma urea ratio among many others. The aim of the current review is to provide an overview of all of the published evidence on potentially clinically valuable serum and urine biomarkers that could be used for predicting disease progression or response to therapy in patients with ADPKD. Hopefully, this review will encourage future longitudinal prospective clinical studies evaluating proposed biomarkers as prognostic tools to improve management and outcome of ADPKD patients in everyday clinical practice.

Urine-HILIC: Automated Sample Preparation for Bottom-Up Urinary Proteome Profiling in Clinical Proteomics.

Urine provides a diverse source of information related to a patient's health status and is ideal for clinical proteomics due to its ease of collection. To date, most methods for the preparation of urine samples lack the throughput required to analyze large clinical cohorts. To this end, we developed a novel workflow, urine-HILIC (uHLC), based on an on-bead protein capture, clean-up, and digestion without the need for bottleneck processing steps such as protein precipitation or centrifugation. The workflow was applied to an acute kidney injury (AKI) pilot study. Urine from clinical samples and a pooled sample was subjected to automated sample preparation in a KingFisher™ Flex magnetic handling station using the novel approach based on MagReSyn® HILIC microspheres. For benchmarking, the pooled sample was also prepared using a published protocol based on an on-membrane (OM) protein capture and digestion workflow. Peptides were analyzed by LCMS in data-independent acquisition (DIA) mode using a Dionex Ultimate 3000 UPLC coupled to a Sciex 5600 mass spectrometer. The data were searched in Spectronaut™ 17. Both workflows showed similar peptide and protein identifications in the pooled sample. The uHLC workflow was easier to set up and complete, having less hands-on time than the OM method, with fewer manual processing steps. Lower peptide and protein coefficient of variation was observed in the uHLC technical replicates. Following statistical analysis, candidate protein markers were filtered, at ≥8.35-fold change in abundance, ≥2 unique peptides and ≤1% false discovery rate, and revealed 121 significant, differentially abundant proteins, some of which have known associations with kidney injury. The pilot data derived using this novel workflow provide information on the urinary proteome of patients with AKI. Further exploration in a larger cohort using this novel high-throughput method is warranted.

NephroCheck at 10: addressing unmet needs in AKI diagnosis and risk stratification.

Despite its name, the current diagnosis of acute kidney injury (AKI) still depends on markers of decreased kidney function and not on markers of injury. This results in a delayed diagnosis: AKI is diagnosed based on serum creatinine criteria only when the severity of injury is enough to decrease glomerular filtration rate. Moreover, by the time AKI is diagnosed, the insult may have already ceased, and even appropriate therapy targeted at the specific insult and its associated pathogenic pathways may no longer be effective. Biomarkers of injury are needed that allow the diagnosis of AKI based on injury criteria. At least three commercially available immunoassays assessing urinary or plasma neutrophil gelatinase-associated lipocalin and urinary tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein-7 ([TIMP2]*[IGFBP7]) (NephroCheck®) have generated promising data regarding prediction and early diagnosis of AKI, although their relative performance may depend on clinical context. Recently, a urinary peptidomics classifier (PeptAKI) was reported to predict AKI better than current biomarkers. Focusing on [TIMP2]*[IGFBP7], the cellular origin of urinary TIMP2 and IGFBP7 remains unclear, especially under the most common predisposing condition for AKI, i.e. chronic kidney disease. We now discuss novel data on the kidney cell expression of TIMP2 and IGFBP7 and its clinical implications.

Statistical approaches applicable in managing OMICS data: Urinary proteomics as exemplary case.

With urinary proteomics profiling (UPP) as exemplary omics technology, this review describes a workflow for the analysis of omics data in large study populations. The proposed workflow includes: (i) planning omics studies and sample size considerations; (ii) preparing the data for analysis; (iii) preprocessing the UPP data; (iv) the basic statistical steps required for data curation; (v) the selection of covariables; (vi) relating continuously distributed or categorical outcomes to a series of single markers (e.g., sequenced urinary peptide fragments identifying the parental proteins); (vii) showing the added diagnostic or prognostic value of the UPP markers over and beyond classical risk factors, and (viii) pathway analysis to identify targets for personalized intervention in disease prevention or treatment. Additionally, two short sections respectively address multiomics studies and machine learning. In conclusion, the analysis of adverse health outcomes in relation to omics biomarkers rests on the same statistical principle as any other data collected in large population or patient cohorts. The large number of biomarkers, which have to be considered simultaneously requires planning ahead how the study database will be structured and curated, imported in statistical software packages, analysis results will be triaged for clinical relevance, and presented.

Novel urinary protein panels for the non-invasive diagnosis of non-alcoholic fatty liver disease and fibrosis stages.

BACKGROUND & AIMS: There is an unmet clinical need for non-invasive tests to diagnose non-alcoholic fatty liver disease (NAFLD) and individual fibrosis stages. We aimed to test whether urine protein panels could be used to identify NAFLD, NAFLD with fibrosis (stage F ≥ 1) and NAFLD with significant fibrosis (stage F ≥ 2). METHODS: We collected urine samples from 100 patients with biopsy-confirmed NAFLD and 40 healthy volunteers, and proteomics and bioinformatics analyses were performed in this derivation cohort. Diagnostic models were developed for detecting NAFLD (UPNAFLD model), NAFLD with fibrosis (UPfibrosis model), or NAFLD with significant fibrosis (UPsignificant fibrosis model). Subsequently, the derivation cohort was divided into training and testing sets to evaluate the efficacy of these diagnostic models. Finally, in a separate independent validation cohort of 100 patients with biopsy-confirmed NAFLD and 45 healthy controls, urinary enzyme-linked immunosorbent assay analyses were undertaken to validate the accuracy of these new diagnostic models. RESULTS: The UPfibrosis model and the UPsignificant fibrosis model showed an AUROC of .863 (95% CI: .725-1.000) and 0.858 (95% CI: .712-1.000) in the training set; and .837 (95% CI: .711-.963) and .916 (95% CI: .825-1.000) in the testing set respectively. The UPNAFLD model showed an excellent diagnostic performance and the area under the receiver operator characteristic curve (AUROC) exceeded .90 in the derivation cohort. In the independent validation cohort, the AUROC for all three of the above diagnostic models exceeded .80. CONCLUSIONS: Our newly developed models constructed from urine protein biomarkers have good accuracy for non-invasively diagnosing liver fibrosis in NAFLD.

Quantitative proteomics identifies and validates urinary biomarkers of rhabdomyosarcoma in children.

BACKGROUND: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma with poor prognosis in children. The 5-year survival rate for early RMS has improved, whereas it remains unsatisfactory for advanced patients. Urine can rapidly reflect changes in the body and identify low-abundance proteins. Early screening of tumor markers through urine in RMS allows for earlier treatment, which is associated with better outcomes. METHODS: RMS patients under 18 years old, including those newly diagnosed and after surgery, were enrolled. Urine samples were collected at the time points of admission and after four cycles of chemotherapy during follow-up. Then, a two-stage workflow was established. (1) In the discovery stage, differential proteins (DPs) were initially identified in 43 RMS patients and 12 healthy controls (HCs) using a data-independent acquisition method. (2) In the verification stage, DPs were further verified as biomarkers in 54 RMS patients and 25 HCs using parallel reaction monitoring analysis. Furthermore, a receiver operating characteristic (ROC) curve was used to construct the protein panels for the diagnosis of RMS. Gene Ontology (GO) and Ingenuity Pathway Analysis (IPA) software were used to perform bioinformatics analysis. RESULTS: A total of 251 proteins were significantly altered in the discovery stage, most of which were enriched in the head, neck and urogenital tract, consistent with the most common sites of RMS. The most overrepresented biological processes from GO analysis included immunity, inflammation, tumor invasion and neuronal damage. Pathways engaging the identified proteins revealed 33 common pathways, including WNT/ß-catenin signaling and PI3K/AKT signaling. Finally, 39 proteins were confirmed as urinary biomarkers for RMS, and a diagnostic panel composed of 5 candidate proteins (EPS8L2, SPARC, HLA-DRB1, ACAN, and CILP) was constructed for the early screening of RMS (AUC: 0.79, 95%CI = 0.66 ~ 0.92). CONCLUSIONS: These findings provide novel biomarkers in urine that are easy to translate into clinical diagnosis of RMS and illustrate the value of global and targeted urine proteomics to identify and qualify candidate biomarkers for noninvasive molecular diagnosis.

Urinary Exosomes: A Promising Biomarker for Disease Diagnosis.

Exosomes are nanoscale vesicles derived from endocytosis, formed by fusion of multivesicular bodies with membranes and secreted into the extracellular matrix or body fluids. Many studies have shown that exosomes can be present in a variety of biological fluids, such as plasma, urine, saliva, amniotic fluid, ascites, and sweat, and most types of cells can secrete exosomes. Exosomes play an important role in many aspects of human development, including immunity, cardiovascular diseases, neurodegenerative diseases, and neoplasia. Urine can be an alternative to blood or tissue samples as a potential source of disease biomarkers because of its simple, noninvasive, sufficient, and stable characteristics. Therefore, urinary exosomes have valuable potential for early screening, monitoring disease progression, prognosis, and treatment. The method for isolating urinary exosomes has been perfected, and exosome proteomics is widely used. Therefore, we review the potential use of urinary exosomes for disease diagnosis and summarize the related literature.

Urinary Protein Profiling for Potential Biomarkers of Chronic Kidney Disease: A Pilot Study.

Proteinuria is a risk factor for chronic kidney disease (CKD) progression and associated complications. However, there is insufficient information on individual protein components in urine and the severity of CKD. We aimed to investigate urinary proteomics and its association with proteinuria and kidney function in early-stage CKD and in healthy individuals. A 24 h urine sample of 42 individuals (21-CKD and 21-healthy individuals) was used for mass spectrometry-based proteomics analysis. An exponentially modified protein abundance index (emPAI) was calculated for each protein. Data were analyzed by Mascot software using the SwissProt database and bioinformatics tools. Overall, 298 unique proteins were identified in the cohort; of them, 250 proteins belong to the control group with median (IQR) emPAI 39.1 (19−53) and 142 proteins belong to the CKD group with median (IQR) emPAI 67.8 (49−117). The level of 24 h proteinuria positively correlated with emPAI (r = 0.390, p = 0.011). The emPAI of some urinary proteomics had close positive (ALBU, ZA2G, IGKC) and negative (OSTP, CD59, UROM, KNG1, RNAS1, CD44, AMBP) correlations (r < 0.419, p < 0.001) with 24 h proteinuria levels. Additionally, a few proteins (VTDB, AACT, A1AG2, VTNC, and CD44) significantly correlated with kidney function. In this proteomics study, several urinary proteins correlated with proteinuria and kidney function. Pathway analysis identified subpathways potentially related to early proteinuric CKD, allowing the design of prospective studies that explore their response to therapy and their relationship to long-term outcomes.

Tracking Prostate Carcinogenesis over Time through Urine Proteome Profiling in an Animal Model: An Exploratory Approach.

Prostate cancer (PCa) is one of the most lethal diseases in men, which justifies the search for new diagnostic tools. The aim of the present study was to gain new insights into the progression of prostate carcinogenesis by analyzing the urine proteome. To this end, urine from healthy animals and animals with prostate adenocarcinoma was analyzed at two time points: 27 and 54 weeks. After 54 weeks, the incidence of pre-neoplastic and neoplastic lesions in the PCa animals was 100%. GeLC-MS/MS and subsequent bioinformatics analyses revealed several proteins involved in prostate carcinogenesis. Increased levels of retinol-binding protein 4 and decreased levels of cadherin-2 appear to be characteristic of early stages of the disease, whereas increased levels of enolase-1 and T-kininogen 2 and decreased levels of isocitrate dehydrogenase 2 describe more advanced stages. With increasing age, urinary levels of clusterin and corticosteroid-binding globulin increased and neprilysin levels decreased, all of which appear to play a role in prostate hyperplasia or carcinogenesis. The present exploratory analysis can be considered as a starting point for studies targeting specific human urine proteins for early detection of age-related maladaptive changes in the prostate that may lead to cancer.

Computational Integration of Renal Histology and Urinary Proteomics using Neural Networks.

Histological image data and molecular profiles provide context into renal condition. Often, a biopsy is drawn to diagnose or monitor a suspected kidney problem. However, molecular profiles can go beyond a pathologist's ability to see and diagnose. Using AI, we computationally incorporated urinary proteomic profiles with microstructural morphology from renal biopsy to investigate new and existing molecular links to image phenotypes. We studied whole slide images of periodic acid-Schiff stained renal biopsies from 56 DN patients matched with 2,038 proteins measured from each patient's urine. Using Seurat, we identified differentially expressed proteins in patients that developed end-stage renal disease within 2 years of biopsy. Glomeruli, globally sclerotic glomeruli, and tubules were segmented from WSI using our previously published HAIL pipeline. For each glomerulus, 315 handcrafted digital image features were measured, and for tubules, 207 features. We trained fully connected networks to predict urinary protein measurements that were differentially expressed between patients who did/ did not progress to ESRD within 2 years of biopsy. The input to this network was either glomerular or tubular histomorphological features in biopsy. Trained network weights were used as a proxy to rank which morphological features correlated most highly with specific urinary proteins. We identified significant image feature-protein pairs by ranking network weights by magnitude. We also looked at which features on average were most significant in predicting proteins. For both glomeruli and tubules, RGB color values and variance in PAS+ areas (specifically basement membrane for tubules) were, on average, more predictive of molecular profiles than other features. There is a strong connection between molecular profile and image phenotype, which can be elucidated through computational methods. These discovered links can provide insight to disease pathways, and discover new factors contributing to incidence and progression.

Urinary Proteomics for Noninvasive Prenatal Screening of Trisomy 21: New Biomarker Candidates.

Trisomy 21 is a common birth defect in humans. Screening for trisomy 21 is one of the most important tasks in routine prenatal care and robust noninvasive diagnostics are needed in clinical practice. Urinary proteomics offers a new research platform for diagnostics innovation in this context. We report here new biomarker candidates using urinary proteomics profiling. Specifically, we used liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the proteomes of urine samples from 19 pregnant women (aged 28-44 years) carrying fetuses with trisomy 21 and 22 healthy pregnant women (aged 27-42 years) carrying fetuses with normal karyotype. We identified more than 50 differentially expressed proteins between the trisomy 21 group and healthy group, and most of these proteins were associated with the embryonic development. Importantly, tissue inhibitor of metalloproteinases 2 (TIMP2) and lysosomal-associated membrane protein 2 (LAMP2) were further selected as potential urinary protein biomarkers. We found that the combination of TIMP2 and LAMP2 could differentiate fetuses with trisomy 21 from healthy controls with a sensitivity of 74%, a specificity of 82%, and an area under the receiver operating characteristic curves (AUC) value of 0.82 (95% confidence interval, 0.69-0.95). We conclude that TIMP2 and LAMP2 offer promise as biomarker candidates and warrant further clinical research in larger study samples. These findings further our understanding of the pathological processes involved in fetal trisomy 21 and are poised to accelerate the development of new noninvasive potential biomarkers for trisomy 21 prenatal screening.

Discovery of bladder cancer biomarkers in paired pre- and postoperative urine samples.

BACKGROUND: Bladder cancer (BC), a common cancer of the urinary system, has a low mortality but an extremely high recurrence rate. Patients who have undergone initial surgical treatment often undergo frequent prognostic examinations with a substantial burden of discomfort and costs. Urine samples can reflect early disease processes in the urinary system and may be an excellent source of biomarkers. METHODS: In the present study, we used the liquid chromatography with tandem mass spectrometry (LC-MS/MS) to perform proteomic analysis of pre- and postoperative urine samples from patients with stage III BC to identify biomarkers of cancer prognosis. Candidate biomarkers from proteomic analysis were simultaneously validated using western blotting in an independent cohort and immunohistochemical (IHC) staining, combined with gene expression data of BC samples in The Cancer Genome Atlas (TCGA). RESULTS: The comparison of pre- and postoperative urine samples from the same patients led to the discovery of several significantly differentially expressed proteins, whose functions could be closely related to the occurrence and development of BC. We confirmed a representative group of candidate biomarker molecules, such as cadherin-related family member 2 (CDHR2), heat shock protein beta-1 (HSP27), and heterogeneous nuclear ribonucleoproteins A2/B1 (HNRNPA2B1). CONCLUSIONS: The candidate biomarker molecules can distinguish between pre- and postoperative urine samples, and alterations in their expression levels are significantly associated with recurrence rates in patients with BC. Therefore, these molecules may become useful biomarkers for the monitoring and prognosis of BC.

A Novel Classifier Based on Urinary Proteomics for Distinguishing Between Benign and Malignant Ovarian Tumors.

BACKGROUND: Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types. METHODS: In total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis. RESULTS: In total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively. CONCLUSION: The novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.

Kidney stone proteomics: an update and perspectives.

INTRODUCTION: Main problems of kidney stone disease are its increasing prevalence and high recurrence rate after calculi removal in almost all areas around the globe. Despite enormous efforts in the past, its pathogenic mechanisms remain unclear and need further elucidations. Proteomics has thus become an essential tool to unravel such sophisticated disease mechanisms at cellular, subcellular, molecular, tissue, and whole organism levels. AREAS COVERED: This review provides abrief overview of kidney stone disease followed by updates on proteomics for investigating urinary stone modulators, matrix proteins, cellular responses to different types/doses of calcium oxalate (CaOx) crystals, sex hormones and other stimuli, crystal-cell interactions, crystal receptors, secretome, and extracellular vesicles (EVs), all of which lead to better understanding of the disease mechanisms. Finally, the future challenges and translation of these obtained data to the clinic are discussed. EXPERT OPINION: Knowledge from urinary proteomics for exploring the important stone modulators (either inhibitors or promoters) will be helpful for early detection of asymptomatic cases for prompt prevention of symptoms, complications, and new stone formation. Moreover, these modulators may serve as the new therapeutic targets in the future for successful treatment and prevention of kidney stone disease by medications or other means of intervention.

Urinary proteomics for kidney dysfunction: insights and trends.

Introduction: Kidney dysfunction poses a high burden on patients and health care systems. Early detection and accurate prediction of kidney disease progression remains a major challenge. Compared to existing clinical parameters, urinary proteomics has the potential to reveal molecular alterations within the kidney that may alter its function before the onset of clinical symptoms. Thus, urinary proteomics has greater prognostic potential for assessment of kidney dysfunction progression.Areas covered: Advances in urinary proteomics for major causes of kidney dysfunction are discussed. The application of urinary extracellular vesicles for studying kidney dysfunction are discussed. Technological advances in urinary proteomics are discussed. The literature was identified using a database search for titles containing 'proteom*' and 'urin*' and published within the past 5 years. Retrieved literature was manually filtered to retain kidney dysfunctions-related studies.Expert opinion: Despite major advances, diagnosis by urinary proteomics has not been fully applied in any clinical settings. This could be attributed to the complex nature of kidney diseases, in addition to the constraints on study power and feasibility of incorporating mass spectrometry techniques in daily routine analysis. Nevertheless, we are confident that advances in urinary proteomics will soon provide superior insights into kidney disease beyond existing clinical parameters.

Urinary proteomic analysis to identify a potential protein biomarker panel for the diagnosis of tuberculosis.

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and is one of the primary causes of death worldwide. Rapid and accurate diagnosis of TB is one of the most direct means to reduce the incidence of TB. In this study, urinary proteomic profiling of TB patients and non-TB individual controls (HCs) was performed, and differentially expressed urinary proteins between TB and HCs were compared and exclusively expressed proteins in TB patients were selected to establish a clinically useful disease marker panel. In total, these top 11 targeted proteins with 265 peptides were scheduled for multiple reaction monitoring validation analysis by using urine samples from 52 TB patients and 52 HCs. The result demonstrated that a three-protein combination out of the five-protein panel (namely P22352, Q9P121, P15151, Q13291, and Q8NDA2) exhibited sensitivity rate of 82.7% in the diagnosis of TB. Furthermore, the three-protein combination could differentiate TB from the latent tuberculosis (LTB) effectively, which exhibited specificity rate of 92.3% for the diagnosis of TB from the LTB category. Although more numbers of clinical samples are required for further verification, the results provided preliminary evidence that this "three-protein combination" out of the five-protein panel could probably be a novel TB diagnostic biomarker in clinical application.