Integrated Proteomics and Metabolomics to Study IgA Nephropathy on Early Stage and Identification of Biomarkers

Abstract

Abstract Background IgA nephropathy (IgAN) is the most common primary chronic glomerulopathy globally. For IgAN diagnosis, kidney biopsy is still the standard method, which is invasive. And there are no effective plasma biomarkers for the disease at the early stage. The research aimed to find potential biomarkers for diagnosing IgAN. Methods Plasma samples of 33 early-stage IgAN patients who were not taking hormonal drugs and 20 healthy controls were collected for proteomic and metabolomic analysis. The least absolute shrinkage and selection operator (LASSO) was used to construct a binary logistic regression model by combining proteomic and metabolomic data. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, and specificity test were performed to assess the model's performance. Results Proteomic analysis of IgAN plasma revealed that the complement and the immune system were activated. And the metabolomic result showed that energy and amino acid metabolism were disordered in IgAN patients. Through machine learning, PRKAR2A, IL6ST, SOS1, and palmitoleic acid have been identified as potential biomarkers. Based on the AUC value for the training and test sets, the classification performance was 0.994 and 0.977, respectively. The AUC of the external validation of the four biomarkers was 0.91. Conclusion In this study, we applied proteomics and metabolomics techniques to analyze the plasma of IgAN patients and find biomarkers. PRKAR2A, IL6ST, SOS1, and palmitoleic acid were combined to serve as potential biomarkers for early diagnosis of IgAN.