Integrated bioinformatics reveals tubulointerstitial immune microenvironment signatures and machine learning-driven prognostication of clinical treatment response in lupus nephritis

Lupus nephritis (LN) represents the most severe renal manifestation of systemic lupus erythematosus (SLE), contributing to significant morbidity. While current assessments focus on glomerular pathology, tubulointerstitial lesions may offer critical insights into disease progression and treatment response. This study develops a clinical prediction model integrating tubulointerstitial molecular signatures. We performed bioinformatics analysis using two independent tubulointerstitial gene expression datasets (GSE113342 and GSE200306), applying batch effect correction and principal component analysis (PCA) to identify differentially expressed genes (DEGs). A protein‒protein interaction (PPI) network isolated hub genes, and least absolute shrinkage and selection operator (LASSO) regression defined the novel “Nscore” parameter predictive of treatment response. The Nscore, incorporating seven key genes (EGR1, IL6R, TFRC, CCL19, IFI16, IFI35, and Fra1), showed a significant positive correlation with 24-h proteinuria and effectively distinguished complete-response (CR)/partial-response (PR) from non-response (NR). Immune deconvolution using the CIBERSORT algorithm revealed an increased abundance of T follicular helper (Tfh) cells and M1 macrophages in NR samples. A clinical nomogram integrating Nscore and sex demonstrated excellent discrimination. This model combines molecular biomarkers with clinical parameters to improve personalized therapeutic stratification, advancing treatment strategies beyond traditional glomerulocentric paradigms and identifying immune cell signatures as potential targets for immunomodulatory interventions.

狼疮肾炎（Lupus nephritis, LN）是系统性红斑狼疮（systemic lupus erythematosus, SLE）最严重的肾脏受累表现，可引发显著的不良临床结局。当前的病情评估多聚焦于肾小球病理改变，而肾小管间质病变或可为疾病进展与治疗应答情况提供关键参考信息。本研究构建了一款整合肾小管间质分子特征的临床预测模型。研究团队利用两项独立的肾小管间质基因表达数据集（GSE113342与GSE200306）开展生物信息学分析，通过批次效应校正与主成分分析（principal component analysis, PCA）筛选差异表达基因（differentially expressed genes, DEGs）；借助蛋白质相互作用（protein‒protein interaction, PPI）网络获取核心基因，并通过最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归构建了全新的"Nscore"评分参数，用于预测治疗应答情况。该评分纳入了EGR1、IL6R、TFRC、CCL19、IFI16、IFI35及Fra1共7个关键基因，与24小时尿蛋白定量呈显著正相关，且可有效区分完全缓解（complete-response, CR）/部分缓解（partial-response, PR）与无应答（non-response, NR）样本。采用CIBERSORT算法进行免疫细胞解卷积分析发现，无应答样本中滤泡辅助性T（T follicular helper, Tfh）细胞与M1型巨噬细胞的丰度显著升高。整合"Nscore"与性别的临床列线图展现出优异的模型鉴别能力。本模型将分子生物标志物与临床参数相结合，可实现更精准的个体化治疗分层，突破了传统以肾小球为中心的诊疗范式，并明确了免疫细胞特征可作为免疫调节干预的潜在靶点。