The NEAT1/miR-124-3p/CCL2 axis in chronic kidney disease progression: integrated bioinformatics analysis and experimental validation

<h4>Background</h4>Chronickidney disease (CKD) is a major global health burden lacking effectivetherapies. Renal interstitial fibrosis (RIF) is a key pathological driver ofCKD progression. This study aimed to identify novel diagnostic biomarkers and therapeutictargets.<h4>Research design and methods</h4>Weanalyzed the GEO dataset GSE137570 to identify differentially expressed genes(DEGs). Protein-protein interaction (PPI) networks were constructed to screen HubGenes. A competing endogenous RNA (ceRNA) network was predicted. Validationincluded single-cell sequencing, in vitro epithelial-mesenchymal transition(EMT) models using Transforming growth factor-β 1 (TGF-β1)-treated TCMK1 cells,clinical samples (64 CKD patients, 20 healthy controls), and dual-luciferasereporter assays (DLRA).<h4>Results</h4>FiveHub Genes (EGF, VCAN, CXCL1, MMP7, CCL2) were identified, with CCL2 being themost central. Enrichment analyses linked them to immune/inflammatory responses.DLRA confirmed specific targeting between miR-124-3p and both NEAT1 and CCL2,supporting the NEAT1/miR-124-3p/CCL2 axis. Clinically, serum CCL2 increasedwhile miR-124-3p and NEAT1 decreased with CKD progression; all three showedgood diagnostic accuracy for staging.<h4>Conclusions</h4>EGF,VCAN, CXCL1, MMP7, and particularly CCL2 are potential CKDbiomarkers/therapeutic targets. The NEAT1/miR-124-3p/CCL2 axis is a keyregulatory pathway in CKD. Key limitations include the moderate sample sizes inbioinformatics and clinical cohorts.