Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction.

Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): p-value < 0.05), but none showed significantly better performance than albumin. To improve the predictive power by multivariate analysis, five proteins (ACP2, CTSA, GM2A, MUC1, and SPARCL1) were selected as significant by an AUC-based random forest method. The application of two classifiers-support vector machine and random forest-showed that the multivariate model performed better than univariate analysis of mucin-1 (AUC: 0.935 vs. 0.791) and albumin (AUC: 1.0 vs. 0.722). The urinary proteome can reflect kidney function directly and can predict the prognosis of patients with chronic kidney dysfunction. Classification based on five urinary proteins may better predict the prognosis of DKD patients than urinary albumin concentration or eGFR.

The identification of new biomarkers for identifying and monitoring kidney disease and their translation into a rapid mass spectrometry-based test: evidence of presymptomatic kidney disease in pediatric Fabry and type-I diabetic patients.

Using label-free quantative proteomics, we have identified 2 potential protein biomarkers that indicate presymptomatic kidney disease in the urine of pediatric patients with type-I diabetes and Fabry disease (n = 20). Prosaposin and GM2 activator protein (GM2AP) were observed to be elevated in the urine of these patient groups compared to age- and sex-matched controls. These findings were validated by development of a rapid MRM-based tandem mass spectrometry test. Prosaposin was observed to be both significantly elevated in the urine of patients with Fabry disease compared to controls (p = 0.02) and reduced after 12 months enzyme replacement therapy (ERT, p = 0.01). Similarly, GM2AP concentrations were observed to be significantly higher compared to controls in the diabetic group (p = 0.049) and the pretreatment Fabry group (p = 0.003). In addition, this observed to be reduced significantly in the Fabry group following 12 months of ERT (p = 0.01). The process of detection of the biomarkers, development into a test and implications for monitoring patients and treatment are discussed.

Combined use of GM2AP and TCP1-eta urinary levels predicts recovery from intrinsic acute kidney injury.

Deficient recovery from acute kidney injury (AKI) has immediate and long-term health, clinical and economic consequences. Pre-emptive recovery estimation may improve nephrology referral, optimize decision making, enrollment in trials, and provide key information for subsequent clinical handling and follow-up. For this purpose, new biomarkers are needed that predict outcome during the AKI episode. We hypothesized that damage pattern-specific biomarkers are expected to more closely associate to outcome within distinct subpopulations (i.e. those affected by specific pathological processes determining a specific outcome), as biomarker pleiotropy (i.e. associated to phenomena unrelated to AKI) introduced by unselected, heterogeneous populations may blur statistics. A panel of urinary biomarkers was measured in patients with AKI and their capacity to associate to normal or abnormal recovery was studied in the whole cohort or after sub-classification by AKI etiology, namely pre-renal and intrinsic AKI. A combination of urinary GM2AP and TCP1-eta best associates with recovery from AKI, specifically within the sub-population of renal AKI patients. This two-step strategy generates a multidimensional space in which patients with specific characteristics (i.e. renal AKI patients with good or bad prognosis) can be identified based on a collection of biomarkers working serially, applying pathophysiology-driven criteria to estimate AKI recovery, to facilitate pre-emptive and personalized handling.

GM2-activator protein: a new biomarker for lung cancer.

INTRODUCTION: Effective biomarkers for early diagnosis of lung cancer are needed. A recent study demonstrated that urinary GM2-activator protein (GM2AP) level was increased in lung cancer patients. This study aims to validate the potential application of GM2AP as a biomarker for diagnosis of lung cancer. METHODS: Serum and urine samples were obtained from 189 participants (133 patients for treatment naive lung cancer, 26 healthy volunteers for urine, and 30 healthy volunteers for serum). GM2AP level was detected by Western blotting and quantified using enzyme-linked immunosorbent assay (ELISA). The GM2AP expression in tumors and nontumor parts of lung tissues from 143 nonsmall cell lung cancers was detected by immunohistochemical stains. RESULTS: There was an 8.11 ± 1.36 folds increase in urine and a 5.41 ± 0.73 folds increase in serum level of GM2AP in lung cancer patients compared with healthy volunteers (p < 0.0001), achieving a 0.89 AUC value in urine and 0.90 AUC value in serum for the receiver-operating characteristic curves. Both serum and urine levels of GM2AP correlated significantly with pathology stages (urine, p = 0.009; serum, p < 0.0001). Using immunohistochemical, positive expression of GM2AP was found at 83.9% of nonsmall cell lung cancers patients and none in normal tissue. The GM2AP expression was significantly correlated with pathology stage (p = 0.0001). Patients with higher GM2AP expression had shorter overall survival (p = 0.045) and disease-free survival (p = 0.049) than lower GM2AP expression. Moreover, the multivariate analysis suggested GM2AP as an independent predictors of disease-free survival and overall survival. CONCLUSIONS: Our study demonstrates that GM2AP might serve as potential diagnostic and prognostic biomarkers in patients with lung cancer.