Shajari Elmira, Gagné David, Malick Mandy, Roy Patricia, Noël Jean-François, Gagnon Hugo, Brunet Marie A, Delisle Maxime, Boisvert François-Michel, Beaulieu Jean-François
Laboratory of Intestinal Physiopathology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada.
Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC J1H 5N4, Canada.
Biomedicines. 2024 Feb 1;12(2):333. doi: 10.3390/biomedicines12020333.
Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.
炎症性肠病（IBD）发作时表现出的症状与其他疾病和病症相似，这使得诊断和治疗变得复杂。目前，诊断和监测IBD的金标准是结肠镜检查和活检，这两种方法具有侵入性且会给患者带来不适，而粪便钙卫蛋白检测的准确性又不够高。因此，有必要开发一种替代方法。在本研究中，我们的目的是为全理论质谱顺序窗口采集-质谱法（SWATH-MS）和机器学习的应用提供概念验证，以开发一种利用粪便蛋白质组区分活动性IBD患者和有症状的非IBD患者的非侵入性且准确的预测模型。我们获得了123个样本的蛋白质组图谱，并优化了数据处理程序以选择合适的流程。差异丰度分析确定了48种蛋白质。利用基于相关性的特征选择（Cfs），选择了7种蛋白质用于后续步骤。为了确定最合适的预测机器学习模型，评估了五种最流行的方法，包括支持向量机（SVM）、随机森林、逻辑回归、朴素贝叶斯和k近邻（KNN）。通过在45个前瞻性未知数据集上实施该算法对生成的模型进行验证；结果显示灵敏度为96%，特异性为76%，表明了其性能。总之，本研究说明了利用通过SWATH-MS获得的粪便蛋白质组通过机器学习模型准确诊断活动性IBD的有效性。
