Clinical ulcerative colitis (UC) is a heterogeneous condition. Moreover, medical interventions are nonspecific, and thus, treatment responses are inconsistent. The aim of this study was to explore the molecular subtypes and biological characteristics of UC based on ferroptosis and neutrophil gene sets. Multiple intestinal mucosa gene expression profiles of UC patients in the Gene Expression Omnibus (GEO) database were downloaded. Unsupervised clustering methods were used to identify potential molecular subtypes based on ferroptosis and neutrophil gene sets. Multiple immune infiltration algorithms were used to evaluate the biological characteristics of the molecular subtypes. Machine learning identifies hub genes for molecular subtypes and analyses their diagnostic efficacy for UC and predictive performance for drug therapy. The relevant conclusions were verified by clinical samples and animal experiments. Four molecular subtypes were identified according to the ferroptosis and neutrophil gene sets: neutrophil, ferroptosis, mixed and quiescent. The subtypes have different biological characteristics and immune infiltration levels. Multiple machine learning methods jointly identified four hub genes (FTH1, AQP9, STEAP3 and STEAP4). Receiver operating characteristic (ROC) curve analysis revealed that the four hub genes could be used as diagnostic markers for UC. The clinical response profile data of infliximab treatment patients showed that AQP9 and STEPA4 were reliable predictors of infliximab treatment response. In human samples the AQP9 and STEAP4 protein were shown to be increased in UC intestinal samples. In animal experiments, the ferroptosis and neutrophil phenotype were confirmed. Dual analysis of ferroptosis and neutrophil gene expression revealed four subgroups of UC patients. The molecular subtype-associated hub genes can be used as diagnostic markers for UC and predict infliximab treatment response.

临床溃疡性结肠炎（UC）是一种异质性疾病。此外，医疗干预措施不具有特异性，因此治疗反应不一致。本研究的目的是基于铁死亡和中性粒细胞基因集探索 UC 的分子亚型和生物学特征。下载基因表达综合 (GEO) 数据库中 UC 患者的多个肠粘膜基因表达谱。使用无监督聚类方法根据铁死亡和中性粒细胞基因集来识别潜在的分子亚型。使用多种免疫浸润算法来评估分子亚型的生物学特性。机器学习识别分子亚型的中心基因，并分析它们对 UC 的诊断功效和药物治疗的预测性能。相关结论经临床样本和动物实验验证。根据铁死亡和中性粒细胞基因集鉴定出四种分子亚型：中性粒细胞、铁死亡、混合型和静止型。不同亚型具有不同的生物学特性和免疫浸润水平。多种机器学习方法联合鉴定了四个中心基因（FTH1、AQP9、STEAP3和STEAP4）。受试者工作特征（ROC）曲线分析表明，这四个中心基因可以作为 UC 的诊断标志物。英夫利昔单抗治疗患者的临床反应概况数据表明，AQP9和STEPA4是英夫利昔单抗治疗反应的可靠预测因子。在人类样本中，UC 肠道样本中的 AQP9 和 STEAP4 蛋白有所增加。在动物实验中，证实了铁死亡和中性粒细胞表型。铁死亡和中性粒细胞基因表达的双重分析揭示了 UC 患者的四个亚组。分子亚型相关的枢纽基因可用作 UC 的诊断标记并预测英夫利昔单抗治疗反应。