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pmid:34332599
Extracellular vesicle-derived AEBP1 mRNA as a novel candidate biomarker for diabetic kidney disease.
Yiying Tao · Xing Wei · Yue Yue · Jiaxin Wang · Jianzhong Li · Lei Shen · Guoyuan Lu · Yang He · Shidi Zhao · Fan Zhao · Zhen Weng · Xiahong Shen · Ling Zhou
Journal of translational medicine · 2021 doi:10.1186/s12967-021-03000-3
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Complete file
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The aim of the present study was to identify novel biomarkers using extracellular vesicle (EV)-derived mRNA based on kidney tissue microarray data.\nMETHODS: Candidate genes were identified by intersecting the differentially expressed genes (DEGs) and eGFR-correlated genes using the GEO datasets GSE30528 and GSE96804, followed by clinical parameter correlation and diagnostic efficacy assessment.\nRESULTS: Fifteen intersecting genes, including 8 positively correlated genes, B3GALT2, CDH10, MIR3916, NELL1, OCLM, PRKAR2B, TREM1 and USP46, and 7 negatively correlated genes, AEBP1, CDH6, HSD17B2, LUM, MS4A4A, PTN and RASSF9, were confirmed. The expression level assessment results revealed significantly increased levels of AEBP1 in DKD-derived EVs compared to those in T2DM and control EVs. Correlation analysis revealed that AEBP1 levels were positively correlated with Cr, 24-h urine protein and serum CYC and negatively correlated with eGFR and LDL, and good diagnostic efficacy for DKD was also found using AEBP1 levels to differentiate DKD patients from T2DM patients or controls.\nCONCLUSIONS: Our results confirmed that the AEBP1 level from plasma EVs could differentiate DKD patients from T2DM patients and control subjects and was a good indication of the function of multiple critical clinical parameters. The AEBP1 level of EVs may serve as a novel and efficacious biomarker for DKD diagnosis.", "claims": [{"id": "claim-method", "statement": "Correlation analysis between the clinical parameters and AEBP1 expression level was performed using Pearson correlation. 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"state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#Tab3", "version": null, "note": "Significantly up and down-regulated genes in different microarray datasets among the 15 intersected genes", "structure": {"pmcid": "PMC8325821", "section_index": 11, "section_title": "Identification of the expression pattern of 15 intersecting genes", "row_count": 6, "column_count": 4}, "table": {"label": "Table 3", "caption": "Significantly up and down-regulated genes in different microarray datasets among the 15 intersected genes", "rows": [["Microarray set no.", "Sample type", "Up", "Down"], ["GSE30528", "Human DKD and control glomeruli samples", "AEBP1, CDH6, LUM, MS4A4A", "B3GALT2, CDH10, NELL1, PRKAR2B, USP46"], ["GSE30529", "Human DKD and control tubuli samples", "AEBP1, CDH6, LUM, MS4A4A, PRKAR2B", "B3GALT2, CDH10, NELL1, OCLM"], ["GSE33744", "Glomeruli samples from Diabetic BKS db/db mice", "AEBP1, B3GALT2, NELL1, PRKAR2B", "HSD17B2"], ["GSE33744", "Glomeruli samples from Diabetic DBA-STZ mice", "AEBP1", "HSD17B2"], ["GSE33744", "Glomeruli samples from Diabetic BKS db/db eNOS −/− mice", "", "HSD17B2, PTN, RASSF9, USP46"]], "row_count": 6, "column_count": 4, "numeric_cells": 10, "header_rows": 1, "locator": "section[11]/table-wrap[1]#Tab3"}}, {"id": "asset:table:pmid:34332599:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:fd864b7f8df7f4ba3835cf1edc613dfd6377e8b572bad5bfa2d0e66f6dbefbc7", "bytes": 1379, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[13]/table-wrap[1]#Tab4", "version": null, "note": "Correlation between exosome AEBP1 level and clinical parameters in DKD patients", "structure": {"pmcid": "PMC8325821", "section_index": 13, "section_title": "Correlation between the expression level of EV-derived AEBP1 and clinical indexes of DKD and exploration of the diagnostic efficacy of AEBP1", "row_count": 29, "column_count": 3}, "table": {"label": "Table 4", "caption": "Correlation between exosome AEBP1 level and clinical parameters in DKD patients", "rows": [["Clinical parameters", "Exosome AEBP1 level"], ["R value", "P value"], ["Age (yrs)", "− 0.283", "0.307"], ["BMI", "− 0.242", "0.384"], ["SBP (mmHg)", "− 0.309", "0.263"], ["DBP (mmHg)", "− 0.140", "0.619"], ["HbA1c (%)", "− 0.062", "0.827"], ["eGFR (MDRD, ml/min/1.73m2)", "− 0.614", "0.0015"], ["eGFR (CKD-EPI, ml/min/1.73m2)", "− 0.59", "0.020"], ["BUN (mmol/L)", "0.298", "0.280"], ["Creatinine (µmol/L)", "0.629", "0.012"], ["TC (mmol/L)", "− 0.170", "0.545"], ["TG (mmol)", "− 0.161", "0.567"], ["LDL (mmol/L)", "− 0.556", "0.031"], ["HDL (mmol/L)", "− 0.034", "0.905"], ["24-h urine protein (g/d)", "0.559", "0.0304"], ["Urine protein to creatinine ratio", "− 0.079", "0.778"], ["Albumin (g/L)", "− 0.335", "0.222"], ["Albumin/creatinine ratio (mg/g)", "0.258", "0.374"], ["Urine β2MG 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The diagnostic efficacy was calculated by receiver operator characteristic (ROC) curves and area under the curve (AUC) (survivalROC and ROCR R packages)."}}, {"id": "method-2", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "ANOVA"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["pearson_r"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pearson."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE96804", "GSE30528", "GSE30529", "GSE33744"], "resolved_accessions": ["GSE96804", "GSE30528", "GSE30529", "GSE33744"], "statements": ["All data generated or analysed during this study are included in this published article [and its additional files]."], "basis": "The paper names GSE96804, GSE30528, GSE30529, GSE33744 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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