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Abstract：
BACKGROUND: Optimizing treatment with biological agents is an ideal goal for patients with ulcerative colitis (UC). Recent data suggest that mucosal inflammation patterns and serum cytokine profiles differ between patients who respond and those who do not. Ustekinumab, a monoclonal antibody targeting the p40 subunit of interleukin (IL)-12 and IL-23, has shown promise, but predicting treatment response remains a challenge. We aimed to identify prognostic markers of response to ustekinumab in patients with active UC, utilizing information from their mucosal transcriptome.
METHODS: We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data.
RESULTS: Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes.
CONCLUSIONS: Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.
METHODS: We performed a prospective observational study of 36 UC patients initiating treatment with ustekinumab. Colonic mucosal biopsies were obtained before treatment initiation for a gene expression analysis using a microarray panel of 84 inflammatory genes. A differential gene expression analysis (DGEA), correlation analysis, and network centrality analysis on co-expression networks were performed to identify potential biomarkers. Additionally, machine learning (ML) models were employed to predict treatment response based on gene expression data.
RESULTS: Seven genes, including BCL6, CXCL5, and FASLG, were significantly upregulated, while IL23A and IL23R were downregulated in non-responders compared to responders. The co-expression analysis revealed distinct patterns between responders and non-responders, with key genes like BCL6 and CRP highlighted in responders and CCL11 and CCL22 in non-responders. The ML algorithms demonstrated a high predictive power, emphasizing the significance of the IL23R, IL23A, and BCL6 genes.
CONCLUSIONS: Our study identifies potential biomarkers associated with ustekinumab response in UC patients, shedding light on its underlying mechanisms and variability in treatment outcomes. Integrating transcriptomic approaches, including gene expression analyses and ML, offers valuable insights for personalized treatment strategies and highlights avenues for further research to enhance therapeutic outcomes for patients with UC.
摘要：
背景：优化生物制剂治疗是溃疡性结肠炎（UC）患者的理想目标。最近的数据表明，有反应的患者和无反应的患者之间的粘膜炎症模式和血清细胞因子谱有所不同。Ustekinumab,一种针对白细胞介素（IL）-12和IL-23的p40亚基的单克隆抗体已显示出希望，但是预测治疗反应仍然是一个挑战。我们旨在确定活动性UC患者对ustekinumab反应的预后标志物，利用粘膜转录组的信息。
方法：我们对36例开始使用ustekinumab治疗的UC患者进行了一项前瞻性观察性研究。在治疗开始之前获得结肠粘膜活检，用于使用84个炎症基因的微阵列面板进行基因表达分析。差异基因表达分析（DGEA），相关分析，并对共表达网络进行网络中心性分析以鉴定潜在的生物标志物。此外,机器学习(ML)模型用于基于基因表达数据预测治疗反应。
结果：七个基因，包括BCL6、CXCL5和FASLG，显着上调，而与应答者相比，非应答者中IL23A和IL23R下调。共表达分析揭示了反应者和非反应者之间的不同模式，关键基因如BCL6和CRP在应答者中突出显示，CCL11和CCL22在非应答者中突出显示。ML算法表现出很高的预测能力，强调IL23R的重要性，IL23a,和BCL6基因。
结论：我们的研究确定了与UC患者ustekinumab反应相关的潜在生物标志物，阐明其潜在机制和治疗结果的变异性。整合转录组学方法，包括基因表达分析和ML，为个性化治疗策略提供了宝贵的见解，并为进一步研究以提高UC患者的治疗结果提供了重要的途径。
方法：我们对36例开始使用ustekinumab治疗的UC患者进行了一项前瞻性观察性研究。在治疗开始之前获得结肠粘膜活检，用于使用84个炎症基因的微阵列面板进行基因表达分析。差异基因表达分析（DGEA），相关分析，并对共表达网络进行网络中心性分析以鉴定潜在的生物标志物。此外,机器学习(ML)模型用于基于基因表达数据预测治疗反应。
结果：七个基因，包括BCL6、CXCL5和FASLG，显着上调，而与应答者相比，非应答者中IL23A和IL23R下调。共表达分析揭示了反应者和非反应者之间的不同模式，关键基因如BCL6和CRP在应答者中突出显示，CCL11和CCL22在非应答者中突出显示。ML算法表现出很高的预测能力，强调IL23R的重要性，IL23a,和BCL6基因。
结论：我们的研究确定了与UC患者ustekinumab反应相关的潜在生物标志物，阐明其潜在机制和治疗结果的变异性。整合转录组学方法，包括基因表达分析和ML，为个性化治疗策略提供了宝贵的见解，并为进一步研究以提高UC患者的治疗结果提供了重要的途径。
