Renal ion channel transport and electrolyte disturbances play an important role in the process of functional impairment and fibrosis in the kidney. It is well known that there are limited effective drugs for the treatment of renal fibrosis, and since a large number of ion channels are involved in the renal fibrosis process, understanding the mechanisms of ion channel transport and the complex network of signaling cascades between them is essential to identify potential therapeutic approaches to slow down renal fibrosis. This review summarizes the current work of ion channels in renal fibrosis. We pay close attention to the effect of cystic fibrosis transmembrane conductance regulator (CFTR), transmembrane Member 16A (TMEM16A) and other Cl− channel mediated signaling pathways and ion concentrations on fibrosis, as well as the various complex mechanisms for the action of Ca2+ handling channels including Ca2+-release-activated Ca2+ channel (CRAC), purinergic receptor, and transient receptor potential (TRP) channels. Furthermore, we also focus on the contribution of Na+ transport such as epithelial sodium channel (ENaC), Na+, K+-ATPase, Na+-H+ exchangers, and K+ channels like Ca2+-activated K+ channels, voltage-dependent K+ channel, ATP-sensitive K+ channels on renal fibrosis. Proposed potential therapeutic approaches through further dissection of these mechanisms may provide new therapeutic opportunities to reduce the burden of chronic kidney disease.

Background: Chronic kidney disease (CKD) is a heterogeneous disease with multiple etiologies, risk factors, clinical manifestations, and prognosis. The aim of this study was to identify different immune-related molecular clusters in CKD, their functional immunological properties, and to screen for promising diagnostic markers.Methods: Datasets of 440 CKD patients were obtained from the comprehensive gene expression database. The core immune-related genes (IRGs) were identified by weighted gene co-expression network analysis. We used unsupervised clustering to divide CKD samples into two immune-related subclusters. Then, functional enrichment analysis was performed for differentially expressed genes (DEGs) between clusters. Three machine learning methods (LASSO, RF, and SVM-RFE) and Venn diagrams were applied to filter out 5 significant IRGs with distinguished subtypes. A nomogram diagnostic model was developed, and the prediction effect was verified using calibration curve, decision curve analysis. CIBERSORT was applied to assess the variation in immune cell infiltration among clusters. The expression levels, immune characteristics and immune cell correlation of core diagnostic markers were investigated. Finally, the Nephroseq V5 was used to assess the correlation among core diagnostic markers and renal function.Results: The 15 core IRGs screened were differentially expressed in normal and CKD samples. CKD was classified into two immune-related molecular clusters. Cluster 2 is significantly enriched in biological functions such as leukocyte adhesion and regulation as well as immune activation, and has a severe immune prognosis compared to cluster 1. A nomogram diagnostic model with reliable prediction of immune-related clusters was developed based on five signature genes. The core diagnostic markers LYZ, CTSS, and ISG20 were identified as playing an important role in the immune microenvironment and were shown to correlate meaningfully with immune cell infiltration and renal function.Conclusion: Our study identifies two subtypes of CKD with distinct immune gene expression patterns and provides promising predictive models. Along with the exploration of the role of three promising diagnostic markers in the immune microenvironment of CKD, it is anticipated to provide novel breakthroughs in potential targets for disease treatment.