Background. Multiple sclerosis (MS) is a chronic debilitating disorder characterized by persisting damage to the brain caused by autoreactive leukocytes. Leukocyte activation is regulated by cytokines, which are readily detected in MS serum and cerebrospinal fluid (CSF). Objective. Serum and CSF levels of forty-five cytokines were analyzed to identify MS diagnostic markers. Methods. Cytokines were analyzed using multiplex immunoassay. ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS. Results. Twenty-two and twenty cytokines were altered in CSF and serum, respectively. The MS diagnosis accuracy was ≥92% when any randomly selected five of these biomarkers were used. Interestingly, the highest accuracy (99%) of MS diagnosis was demonstrated when CCL27, IFN-γ, and IL-4 were part of the five selected cytokines, suggesting their important role in MS pathogenesis. Also, these binary classifier models had the accuracy in the range of 70-78% (serum) and 60-69% (CSF) to discriminate between the progressive (primary and secondary progressive) and relapsing-remitting forms of MS. Conclusion. We identified the set of cytokines from the serum and CSF that could be used for the MS diagnosis and classification.

中文翻译：

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用于诊断多发性硬化症的血清和脑脊液细胞因子生物标志物

背景。多发性硬化症 (MS) 是一种慢性衰弱性疾病，其特征是由自身反应性白细胞引起的大脑持续损伤。白细胞活化受细胞因子调节，细胞因子很容易在 MS 血清和脑脊液 (CSF) 中检测到。客观。分析了 45 种细胞因子的血清和 CSF 水平以鉴定 MS 诊断标志物。方法。使用多重免疫测定法分析细胞因子。计算基于 ANOVA 的特征和 Pearson 相关系数分数，以选择用作机器学习模型输入的特征，对 MS 进行预测和分类。结果. 脑脊液和血清中分别有 22 和 20 种细胞因子发生改变。当使用任何随机选择的五种生物标志物时，MS 诊断准确率≥92%。有趣的是，当 CCL27、IFN- γ和 IL-4 是五种选定细胞因子的一部分时，MS 诊断的准确性最高 (99%)，表明它们在 MS 发病机制中的重要作用。此外，这些二元分类器模型具有 70-78%（血清）和 60-69%（CSF）范围内的准确度来区分 MS 的进行性（原发性和继发性进行性）和复发-缓解形式。结论。我们从血清和脑脊液中确定了一组可用于 MS 诊断和分类的细胞因子。