Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) with poor treatment outcomes. The role and underlying mechanisms of ferroptosis in LN remain largely unknown. We aimed to explore ferroptosis-related molecular subtypes and assess their prognostic value in LN patients. Molecular subtypes were classified on the basis of differentially expressed ferroptosis-related genes (FRGs) via the Consensus ClusterPlus package. The enriched functions and pathways, immune infiltrating levels, immune scores, and immune checkpoints were compared between the subgroups. A scoring algorithm based on the subtype-specific feature genes identified by artificial neural network machine learning, referred to as the NeuraLN, was established, and its immunological features, clinical value, and predictive value were evaluated in patients with LN. Finally, immunohistochemical analysis was performed to validate the expression and role of feature genes in glomerular tissues from LN patients and controls. A total of 10 differentially expressed FRGs were identified, most of which showed significant correlation. Based on the 10 FRGs, LN patients were classified into two ferroptosis subtypes, which exhibited significant differences in immune cell abundances, immune scores, and immune checkpoint expression. A NeuraLN-related protective model was established based on nine subtype-specific genes, and it exhibited a robustly predictive value in LN. The nomogram and calibration curves demonstrated the clinical benefits of the protective model. The high-NeuraLN group was closely associated with immune activation. Clinical specimens demonstrated the alterations of ALB, BHMT, GAMT, GSTA1, and HAO2 were in accordance with bioinformatics analysis results, GSTA1 and BHMT were negatively correlated with the severity of LN. The classification of ferroptosis subtypes and the establishment of a protective model may form a foundation for the personalized treatment of LN patients.

中文翻译：

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基于人工神经网络学习的狼疮性肾炎铁死亡相关分子亚型及免疫学特征研究


狼疮性肾炎（LN）是系统性红斑狼疮（SLE）的严重并发症，治疗效果不佳。 LN 中铁死亡的作用和潜在机制仍然很大程度上未知。我们的目的是探索铁死亡相关的分子亚型并评估其对 LN 患者的预后价值。通过 Consensus ClusterPlus 软件包根据差异表达的铁死亡相关基因 (FRG) 对分子亚型进行分类。比较各亚组之间的丰富功能和途径、免疫浸润水平、免疫评分和免疫检查点。建立了基于人工神经网络机器学习识别的亚型特异性特征基因的评分算法（简称NeuraLN），并在LN患者中评估其免疫学特征、临床价值和预测价值。最后，进行免疫组织化学分析以验证 LN 患者和对照肾小球组织中特征基因的表达和作用。共鉴定出10个差异表达的FRG，其中大部分显示出显着相关性。基于10个FRG，LN患者被分为两种铁死亡亚型，它们在免疫细胞丰度、免疫评分和免疫检查点表达方面表现出显着差异。基于九个亚型特异性基因建立了 NeuraLN 相关保护模型，该模型在 LN 中表现出稳健的预测价值。列线图和校准曲线证明了保护模型的临床益处。高 NeuraLN 组与免疫激活密切相关。 临床标本显示ALB、BHMT、GAMT、GSTA1、HAO2的变化与生物信息学分析结果一致，GSTA1、BHMT与LN严重程度呈负相关。铁死亡亚型的分类和保护模型的建立可能为LN患者的个体化治疗奠定基础。