Downregulation of PDZK1 by TGF-β1 promotes renal fibrosis via inducing epithelial-mesenchymal transition of renal tubular cells

Lu, Shuanghui; Chen, Xiu; Chen, Yujia; Zhang, Yingqiong; Luo, Jun; Jiang, Huidi; Fang, Luo; Zhou, Hui

doi:10.1016/j.bcp.2023.116015

Cited by 3 publications

(1 citation statement)

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“…MYH7 [228], WNT3A [229], ANGPTL3 [230], CYP3A5 [231], FGF1 [232], HMGCS1 [233], NOX4 [234], AQP2 [235], EGF (epidermal growth factor) [236], NR4A1 [237], DPP4 [238], STC1 [239], ABCB1 [240], MTTP (microsomal triglyceride transfer protein) [241], F8 [242], VCAM1 [243] and ALB (albumin) [244] are participate in pathogenic processes of hypercholesterolemia. BMI1 [245], CCL5 [246], GREM1 [247], ANGPTL3 [248], ARG2 [249], MSRA (methionine sulfoxidereductase A) [250], SNCA (synuclein alpha) [251], NOX4 [252], PFKFB2 [253], PDZK1 [254], SUCNR1 [255], LYVE1 [256], AZGP1 [257], ERBB4 [258] and PLAT (plasminogen activator, tissue type) [259] might serve as molecular markers for kidney fibrosis. BMI1 [260], IGF2 [261], IRF7 [262], CCL5 [263], ACTN3 [264], E2F1 [265], PF4 [266], TEAD4 [267], TBX4 [268], GREM1 [269], CYP11B2 [270], WNT3A [124], COMP (cartilage oligomeric matrix protein) [271], FLI1 [272], RAP1B [273], ANGPTL3 [274], CYP3A5 [275], HSD11B2 [276], HMGCS2 [277], AGXT2 [278], SLC22A12 [279], FGF1 [280], CRY1 [281], PPARGC1A [282], SLC19A3 [283], CYP2C8 [284], ACOX2 [285], SLC2A9 [286], MSRA (methionine sulfoxidereductase A) [287], VNN1 [288], EPHX2 [289], CROT (carnitine O-octanoyltransferase) [290], SCNN1B [291], NR4A3 [292], HSD17B7 [293], SLC22A2 [294], AQP2 [295], SLC2A2 [296], EGF (epidermal growth factor) [297], ANGPT1 [298], SLC...…”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in acute kidney injury using bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2024

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Acute kidney injury (AKI) is a type of renal disease occurs frequently in hospitalized patients, which may cause abnormal renal function and structure with increase in serum creatinine level with or without reduced urine output. With the incidence of AKI is increasing. However, the molecular mechanisms of AKI have not been elucidated. It is significant to further explore the molecular mechanisms of AKI. We downloaded the GSE139061 next generation sequencing (NGS) dataset from the Gene Expression Omnibus (GEO) database. Limma R bioconductor package was used to screen the differentially expressed genes (DEGs). Then, the enrichment analysis of DEGs in Gene Ontology (GO) function and REACTOME pathways was analyzed by g:Profiler. Next, the protein-protein interaction (PPI) network and modules was constructed and analyzed, and the hub genes were identified. Next, the miRNA-hub gene regulatory network and TF-hub gene regulatory network were built. We also validated the identified hub genes via receiver operating characteristic (ROC) curve analysis. Overall, 956 DEGs were identified, including 478 up regulated and 478 down regulated genes. The enrichment functions and pathways of DEGs involve primary metabolic process, small molecule metabolic process, striated muscle contraction and metabolism. Topological analysis of the PPI network and module revealed that hub genes, including PPP1CC, RPS2, MDFI, BMI1, RPL23A, VCAM1, ALB, SNCA, DPP4 and RPL26L1, might be involved in the development of AKI. miRNA-hub gene and TF-hub gene regulatory networks revealed that miRNAs and TFs including hsa-mir-510-3p, hsa-mir-6086 and mir-146a-5p, MAX and PAX2, might be involved in the development of AKI. Various known and newtherapeutic targets were obtained via network analysis. The results of the current investigation might be beneficial for the diagnosis and treatment of AKI.

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