Zaorska Katarzyna, Zawierucha Piotr, Świerczewska Monika, Ostalska-Nowicka Danuta, Zachwieja Jacek, Nowicki Michał
Department of Histology and Embryology, University of Medical Sciences, Swiecickiego St 6, 60-781, Poznan, Poland.
Institute of Bioorganic Chemistry, Department of RNA Metabolism, Polish Academy of Sciences, Zygmunta Noskowskiego St 12/14, 61-704, Poznan, Poland.
J Transl Med. 2021 Mar 30;19(1):130. doi: 10.1186/s12967-021-02790-w.
Steroid resistant (SR) nephrotic syndrome (NS) affects up to 30% of children and is responsible for fast progression to end stage renal disease. Currently there is no early prognostic marker of SR and studied candidate variants and parameters differ highly between distinct ethnic cohorts.
Here, we analyzed 11polymorphic variants, 6 mutations, SOCS3 promoter methylation and biochemical parameters as prognostic markers in a group of 124 Polish NS children (53 steroid resistant, 71 steroid sensitive including 31 steroid dependent) and 55 controls. We used single marker and multiple logistic regression analysis, accompanied by prediction modeling using neural network approach.
We achieved 92% (AUC = 0.778) SR prediction for binomial and 63% for multinomial calculations, with the strongest predictors ABCB1 rs1922240, rs1045642 and rs2235048, CD73 rs9444348 and rs4431401, serum creatinine and unmethylated SOCS3 promoter region. Next, we achieved 80% (AUC = 0.720) in binomial and 63% in multinomial prediction of SD, with the strongest predictors ABCB1 rs1045642 and rs2235048. Haplotype analysis revealed CD73_AG to be associated with SR while ABCB1_AGT was associated with SR, SD and membranoproliferative pattern of kidney injury regardless the steroid response.
We achieved prediction of steroid resistance and, as a novelty, steroid dependence, based on early markers in NS children. Such predictions, prior to drug administration, could facilitate decision on a proper treatment and avoid diverse effects of high steroid doses.
激素抵抗（SR）型肾病综合征（NS）影响多达30%的儿童，并导致快速进展至终末期肾病。目前尚无SR的早期预后标志物，且不同种族队列中研究的候选变异和参数差异很大。
在此，我们分析了11个多态性变异、6个突变、SOCS3启动子甲基化和生化参数作为一组124名波兰NS儿童（53例激素抵抗型、71例激素敏感型包括31例激素依赖型）和55名对照的预后标志物。我们使用单标记和多因素逻辑回归分析，并采用神经网络方法进行预测建模。
二项式计算的SR预测准确率为92%（AUC = 0.778），多项式计算为63%，最强预测因子为ABCB1 rs1922240、rs1045642和rs2235048、CD73 rs9444348和rs4431401、血清肌酐和未甲基化的SOCS3启动子区域。接下来，我们在二项式SD预测中达到80%（AUC = 0.720），多项式预测中为63%，最强预测因子为ABCB1 rs1045642和rs2235048。单倍型分析显示，无论激素反应如何，CD73_AG与SR相关，而ABCB1_AGT与SR、SD和肾损伤的膜增生模式相关。
我们基于NS儿童的早期标志物实现了对激素抵抗以及新颖的激素依赖的预测。在给药前进行此类预测有助于做出适当治疗的决策，并避免高剂量激素的各种影响。
