The Study Of Early IgA Nephropathy And Identification Of Diagnostic Markers Based On Multi-omics Data

IgAN(Immunoglobulin A nephropathy)is the most common primary glomerular disease and one of the main causes of end-stage renal disease.Without effective intervention,about 30 to 40 percent of patients will develop uremia within 20 years.At present,the pathogenesis of IgAN has not been fully clarified.The gold standard for diagnosis of IgAN depends on kidney biopsy.However,kidney biopsy is not suitable for early diagnosis of IgAN due to its invasive nature and potential risks of bleeding and infection.Combined analysis of multiple omics has become one of the important means to study diseases.Genomics,transcriptomics,proteomics and metabolomics are important directions of systems biology research.They mainly detect all through high-throughput sequencing technology and high-resolution mass spectrometry technology to obtain huge amounts of biological data.Through bioinformatics method,the data were deeply mined to find the potential markers of disease diagnosis,process and prognosis.And the pathogenesis was elaborated.In this paper,transcriptomics combined with proteomics and metabolomics were used to analyze blood samples from patients with early IgAN.The main research contents and results are as follows:(1)Gene expression analysis of peripheral blood mononuclear cells(PBMC)in IgAN patientsPBMC gene expression was analyzed by RNA sequencing(RNA-Seq)in 29 patients with early IgAN and 15 healthy controls.A total of 13392 genes were obtained,among which 46 genes were different between IgAN and healthy controls.Differentially expressed gens(DEGs)were mainly enriched in leukocyte migration,regulation of inflammatory reactions,leukocyte chemotaxis,cytokine-cytokine receptor interactions,and IL-17 signaling pathway.Six hub genes,ARPC5,MPO,MMP9,IL1 RN,S100A9,and FPR1,were selected by WGCNA and PPI.(2)Plasma proteomics and metabolomics of IgAN patientsThis research was based on ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)technology.Data-independent acquisition(DIA)label-free quantification proteomics and non-targeted metabolomics quantitative analysis were used to screen the pathogenesis and markers of early IgAN.A total of 5267 proteins and 540 metabolites were identified,of which 71 proteins and 90 metabolites were different between IgAN and healthy controls.The analysis of differentially expressed proteins(DEPs)revealed a significant correlation between complement activation,humoral immune response,and immunoregulation with early IgAN.Differentially abundant metabolites(DAMs)enriched metabolic pathways indicate that alteration in metabolism of various amino acids and energy metabolism are associated with IgAN.(3)Construction and verification of early diagnosis model of IgANIn this study,the LASSO machine learning algorithm was utilized to identify potential markers for distinguishing IgAN patients from healthy controls.Three proteins,namely c AMP-dependent protein kinase type II regulatory subunit α(PRKAR2A),interleukin-6cytokine family signal sensor(IL6ST),and SOS Ras/Rac guanine nucleotide exchange factor 1(SOS1),as well as one DAM,palmitoleic acid,were identified as significant biomarkers.The receiver operator characteristic curve ROC(ROC)showed that the diagnostic markers of proteomics combined with metabolomics were superior to that of a single marker.Enzyme-linked immunosorbent assay(ELISA)was used to verify potential diagnostic markers in an independent cohort,and the results indicated that the combination of markers had good diagnostic efficacy.The potential pathophysiology of early IgAN was elucidated by PBMC transcriptome sequencing,and hub genes were screened.By integrating the plasma proteomics and metabolomics data of IgAN,the combined markers of proteins and metabolites were selected.It provides new insights into the clinical study of IgAN and offers potential novel molecular diagnostics.