Clinico-demographic and biochemical correlation of inflammatory gene expression in pediatric nephrotic syndrome.

backgroundNephrotic syndrome (NS) is a common kidney disease in children. While Steroid-Sensitive Nephrotic Syndrome (SSNS) is frequently observed, Steroid-Resistant Nephrotic Syndrome (SRNS) has a poor prognosis and often leads to chronic kidney disease. The pathogenesis of SRNS is complex, with immunological modulation of T helper subtypes 1 and 2 cytokines increasing susceptibility to the disease. Currently, no established biomarkers can accurately predict SRNS. However, a group of cytokines might serve as potential indicators of responsiveness, aiding in the identification of patients with SRNS. The discovery of these cytokines as novel biomarkers for early diagnosis could greatly benefit patients. This includes preventing the adverse effects of glucocorticoid treatment and enabling a timely transition to more effective therapeutic alternatives.

methodsThis study aims to investigate the association between the gene expression patterns of cytokines, including IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-13, IL-17A, NF-κB, and TNFα, in healthy participants (n = 100), SSNS patients (n = 100), and SRNS patients (n = 100). Using qRT-PCR, followed by Receiver-operating characteristic analysis, the study assesses their potential as biomarkers. Additionally, clinicodemographic data were analyzed, and bioinformatic analyses such as coexpression analysis, gene enrichment, pathway analysis, and Cytoscape were performed to enhance our understanding of the inflammatory cascade initiating podocyte injury in NS.

resultsThe results of our study suggest that specific candidate genes, including IL-2, IL-5, IL-6, IL-9, IL-17A, IL-10, IL-13, and TNFα, exhibit upregulation and hold significant importance, with an Area Under the Curve value of 0.9.

conclusionThese genes have the potential to serve as valuable prognostic and management tools for NS, forming a promising panel of inflammatory gene biomarkers. Furthermore, conducting an extensive analysis that integrates cytokine genes with their respective targeted microRNAs could offer deeper insights into the pathogenesis of the disease.