DataSheet_1_Construction of a molecular inflammatory predictive model with histone modification-related genes and identification of CAMK2D as a potential response signature to infliximab in ulcerative colitis.docx

BackgroundUlcerative colitis (UC) is a lifelong inflammatory disease affecting the rectum and colon with numerous treatment options that require an individualized treatment plan. Histone modifications regulate chromosome structure and gene expression, resulting in effects on inflammatory and immune responses. However, the relationship between histone modification-related genes and UC remains unclear.
MethodsTranscriptomic data from GSE59071 and GSE66407 were obtained from the Gene Expression Omnibus (GEO), encompassing colonic biopsy expression profiles of UC patients in inflamed and non-inflamed status. Differentially expressed gene (DEG) analyses, functional enrichment analyses, weighted gene co-expression network analysis (WGCNA), and random forest were performed to identify histone modification-related core genes associated with UC inflammation. Features were screened through the least absolute shrinkage and selection operator (LASSO) and support vector machine‐recursive feature elimination (SVM‐RFE), establishing a molecular inflammatory predictive model using logistic regression. The model was validated in the GSE107499 dataset, and the performance of the features was assessed using receiver operating characteristic (ROC) and calibration curves. Immunohistochemistry (IHC) staining of colonic biopsy tissues from UC patients treated with infliximab was used to further confirm the clinical application value. Univariate logistic regression on GSE14580 highlighted features linked to infliximab response.
ResultsA total of 253 histone modification-related DEGs were identified between inflammatory and non-inflammatory patients with UC. Seven key genes (IL-1β, MSL3, HDAC7, IRF4, CAMK2D, AUTS2, and PADI2) were selected using WGCNA and random forest. Through univariate logistic regression, three core genes (CAMK2D, AUTS2, and IL-1β) were further incorporated to construct the molecular inflammatory predictive model. The area under the curve (AUC) of the model was 0.943 in the independent validation dataset. A significant association between CAMK2D protein expression and infliximab response was observed, which was validated in another independent verification set of GSE14580 from the GEO database.
ConclusionThe molecular inflammatory predictive model based on CAMK2D, AUTS2, and IL-1β could reliably distinguish the mucosal inflammatory status of UC patients. We further revealed that CAMK2D was a predictive marker of infliximab response. These findings are expected to provide a new evidence base for personalized treatment and management strategies for UC patients.

溃疡性结肠炎（Ulcerative colitis, UC）是一种累及直肠与结肠的终身性炎症性疾病，临床治疗手段多样，需制定个体化治疗方案。组蛋白修饰（Histone modifications）可调控染色体结构与基因表达，进而对炎症与免疫应答产生影响，但目前组蛋白修饰相关基因与UC的关联仍未明确。

方法：本研究从基因表达综合数据库（Gene Expression Omnibus, GEO）获取GSE59071与GSE66407的转录组数据，涵盖UC患者处于炎症状态与非炎症状态时的结肠活检组织表达谱。通过差异表达基因（Differentially expressed gene, DEG）分析、功能富集分析、加权基因共表达网络分析（Weighted gene co-expression network analysis, WGCNA）以及随机森林算法，筛选与UC炎症相关的组蛋白修饰相关核心基因。借助最小绝对收缩和选择算子（Least absolute shrinkage and selection operator, LASSO）与支持向量机-递归特征消除（Support vector machine-recursive feature elimination, SVM-RFE）进行特征筛选，采用逻辑回归构建分子炎症预测模型。在GSE107499数据集内对该模型进行验证，并通过受试者工作特征（Receiver operating characteristic, ROC）曲线与校准曲线评估特征性能。对接受英夫利昔单抗（infliximab）治疗的UC患者的结肠活检组织开展免疫组化（Immunohistochemistry, IHC）染色，以进一步验证模型的临床应用价值。针对GSE14580数据集进行单因素逻辑回归分析，筛选与英夫利昔单抗应答相关的特征。

结果：本研究共在UC炎症患者与非炎症患者间鉴定出253个组蛋白修饰相关差异表达基因。通过WGCNA与随机森林算法筛选得到7个关键基因：IL-1β、MSL3、HDAC7、IRF4、CAMK2D、AUTS2及PADI2。经单因素逻辑回归分析，最终纳入CAMK2D、AUTS2与IL-1β这3个核心基因构建分子炎症预测模型。在独立验证数据集内，该模型的曲线下面积（Area under the curve, AUC）达0.943。研究观察到CAMK2D蛋白表达与英夫利昔单抗应答存在显著关联，该结果在GEO数据库中的另一独立验证集GSE14580中得到验证。

结论：本研究构建的基于CAMK2D、AUTS2及IL-1β的分子炎症预测模型可有效区分UC患者的黏膜炎症状态。本研究进一步揭示CAMK2D可作为英夫利昔单抗应答的预测标志物。上述研究结果有望为UC患者的个体化治疗与管理策略提供新的循证依据。