Effects of sodium-glucose co-transporter-2 inhibitors on kidney, cardiovascular, and safety outcomes in patients with advanced chronic kidney disease: a systematic review and meta-analysis of randomized controlled trials.

AIMS: The overall effects of sodium-glucose co-transporter 2 (SGLT2) inhibitors in patients with advanced chronic kidney disease (CKD) (estimated glomerular filtration rate (eGFR), 15-30 ml/min per 1.73 m2) remain unclear, and we thus conducted a systematic review and meta-analysis to evaluate the effects of SGLT2 inhibitors on kidney, cardiovascular (CV), and safety outcomes in patients with advanced CKD. METHODS: The Medline, Embase, and Cochrane Library databases were systematically searched for randomized controlled trials (RCTs) published up to March 3, 2022, and reporting effects of SGLT2 inhibitors on kidney, CV, or safety outcomes in patients with advanced CKD. RESULTS: From 2675 records, six RCTs with 2167 participants were included in the quantitative analyses. In patients with advanced CKD, SGLT2 inhibitors reduced the risk of the primary kidney outcome (a composite of worsening kidney function, end-stage kidney disease (ESKD), or kidney death) by 23% (RR 0.77, 95% CI 0.61-0.98, p = 0.04, I2 = 0 for the heterogeneity) and slowed the annual decline in eGFR slope, with the difference between SGLT2 inhibitor group and placebo group being 1.24 mL/min/1.73m2 per year (95% CI 0.06-2.42, p = 0.04). SGLT2 inhibitors were also associated with a decreased risk of primary CV outcome (a composite of CV death or hospitalization for heart failure) (HR 0.71, 95% CI 0.53-0.96, p = 0.03, I2 = 0 for the heterogeneity) and with similar risks of adverse events (such as acute kidney injury, fracture, amputation, and urinary tract infection). CONCLUSIONS: Among patients with advanced CKD, SGLT2 inhibitors reduced the risks of primary kidney and CV outcomes and attenuated the progressive decrease in eGFR compared with placebo, with no evidence of additional safety concerns. These observed benefits may support continuing the use of SGLT2 inhibitors in patients with advanced CKD before initiating maintenance dialysis or kidney transplantation. Future large-scale RCTs are needed to confirm the robustness of these results.

Identification of tubulointerstitial genes and ceRNA networks involved in diabetic nephropathy via integrated bioinformatics approaches.

BACKGROUND: Diabetic nephropathy (DN) is the major cause of end-stage renal disease worldwide. The mechanism of tubulointerstitial lesions in DN is not fully elucidated. This article aims to identify novel genes and clarify the molecular mechanisms for the progression of DN through integrated bioinformatics approaches. METHOD: We downloaded microarray datasets from Gene Expression Omnibus (GEO) database and identified the differentially expressed genes (DEGs). Enrichment analyses, construction of Protein-protein interaction (PPI) network, and visualization of the co-expressed network between mRNAs and microRNAs (miRNAs) were performed. Additionally, we validated the expression of hub genes and analyzed the Receiver Operating Characteristic (ROC) curve in another GEO dataset. Clinical analysis and ceRNA networks were further analyzed. RESULTS: Totally 463 DEGs were identified, and enrichment analyses demonstrated that extracellular matrix structural constituents, regulation of immune effector process, positive regulation of cytokine production, phagosome, and complement and coagulation cascades were the major enriched pathways in DN. Three hub genes (CD53, CSF2RB, and LAPTM5) were obtained, and their expression levels were validated by GEO datasets. Pearson analysis showed that these genes were negatively correlated with the glomerular filtration rate (GFR). After literature searching, the ceRNA networks among circRNAs/IncRNAs, miRNAs, and mRNAs were constructed. The predicted RNA pathway of NEAT1/XIST-hsa-miR-155-5p/hsa-miR-486-5p-CSF2RB provides an important perspective and insights into the molecular mechanism of DN. CONCLUSION: In conclusion, we identified three genes, namely CD53, CSF2RB, and LAPTM5, as hub genes of tubulointerstitial lesions in DN. They may be closely related to the pathogenesis of DN and the predicted RNA regulatory pathway of NEAT1/XIST-hsa-miR-155-5p/hsa-miR-486-5p-CSF2RB presents a biomarker axis to the occurrence and development of DN.

Exploring the pathogenesis of diabetic kidney disease by microarray data analysis.

Diabetic kidney disease (DKD) is a major complication of diabetes mellitus, and the leading contributor of end-stage renal disease. Hence, insights into the molecular pathogenesis of DKD are urgently needed. The purpose of this article is to reveal the molecular mechanisms underlying the pathogenesis of DKD. The microarray datasets of GSE30528 and GSE30529 were downloaded from the NCBI Gene Expression Omnibus (GEO) database to identify the common differentially expressed genes (DEGs) between the glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the function and pathways of the common DEGs. After constructing the protein-protein interaction (PPI) network and subnetwork analysis, three types of analyses were performed, namely, identification of hub genes, analysis of the coexpressed network, and exploration of transcription factors (TFs). Totally, 348 and 463 DEGs were identified in GDKD and TDKD, respectively. Then, 66 common DEGs (63 upregulated DEGs and three downregulated DEGs) were obtained in DKD patients. GO and KEGG pathway analyses revealed the importance of inflammation response, immune-related pathways, and extracellular matrix-related pathways, especially chemokines and cytokines, in DKD. Fifteen hub genes from the 66 common DEGs, namely, IL10RA, IRF8, LY86, C1QA, C1QB, CD53, CD1C, CTSS, CCR2, CD163, CCL5, CD48, RNASE6, CD52, and CD2 were identified. In summary, through the microarray data analysis, the common functions and hub genes greatly contribute to the elucidation of the molecular pathogenesis associated with DKD.

NEAT1/MALAT1/XIST/PKD--Hsa-Mir-101-3p--DLGAP5 Axis as a Novel Diagnostic and Prognostic Biomarker Associated With Immune Cell Infiltration in Bladder Cancer.

Background: The clinical value of the biomarkers of bladder cancer (BC) is limited due to their low sensitivity or specificity. As a biomarker, DLG associated protein 5 (DLGAP5) is a potential cell cycle regulator in cancer cell carcinogenesis. However, its functional part in BC remains unclear. Therefore, this study aims to identify DLGAP5 expression in BC and its potential diagnostic and prognostic values. Eventually, it predicts the possible RNA regulatory pathways of BC. Methods: Data on DLGAP5 expression levels in BC and normal bladder tissues were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The receiver operating characteristic (ROC), Kaplan-Meier survival curves, and the univariate and multivariate Cox regression analysis determined the diagnostic and prognostic values of DLGAP5 in BC patients. Finally, the StarBase predicted the target RNAs and constructed networks using Cytoscape. Results: DLGAP5 expression was significantly upregulated in BC tissue, verified by the TCGA (p < 0.001), GSE3167, GSE7476, and GSE65635 datasets (p < 0.01). BC patients with increased DLGAP5 had poor overall survival (OS) (p = 0.01), disease specific survival (DSS) (p = 0.006) and progress free interval (DFI) (p = 0.007). The area under the ROC curve (AUC) was 0.913. The multivariate Cox analysis identified that lymphovascular invasion (p = 0.007) and DLGAP5 (p = 0.002) were independent prognostic factors. Conclusion: Increased DLGAP5 expression was closely associated with a poor prognosis in BC patients. In this case, DLGAP5 might be a diagnostic and prognostic biomarker for BC. DLGAP5 expression might be regulated by NEAT1/MALAT1/XIST/PKD--Hsa-mir-101-3p pathways.