Bae Kyung-Jin, Bae Jun-Hyung, Oh Ae-Chin, Cho Chi-Hyun
Department of Medicine, Korea University College of Medicine, Seoul 02841, Republic of Korea.
Department of Laboratory Medicine, Korea Cancer Center Hospital, Seoul 01812, Republic of Korea.
Diagnostics (Basel). 2025 Mar 20;15(6):791. doi: 10.3390/diagnostics15060791.
: Recent studies have analyzed some cytokines in patients with papillary thyroid carcinoma (PTC), but simultaneous analysis of multiple cytokines remains rare. Nonetheless, the simultaneous assessment of multiple cytokines is increasingly recognized as crucial for understanding the cytokine characteristics and developmental mechanisms in PTC. In addition, studies applying artificial intelligence (AI) to discriminate patients with PTC based on serum multiple cytokine data have been performed rarely. Here, we measured and compared 46 cytokines in patients with PTC and healthy individuals, applying AI algorithms to classify the two groups. : Blood serum was isolated from 63 patients with PTC and 63 control individuals. Forty-six cytokines were analyzed simultaneously using Luminex assay Human XL Cytokine Panel. Several laboratory findings were identified from electronic medical records. Student's -test or the Mann-Whitney U test were performed to analyze the difference between the two groups. As AI classification algorithms to categorize patients with PTC, K-nearest neighbor function, Naïve Bayes classifier, logistic regression, support vector machine, and eXtreme Gradient Boosting (XGBoost) were employed. The SHAP analysis assessed how individual parameters influence the classification of patients with PTC. : Cytokine levels, including GM-CSF, IFN-γ, IL-1ra, IL-7, IL-10, IL-12p40, IL-15, CCL20/MIP-α, CCL5/RANTES, and TNF-α, were significantly higher in PTC than in controls. Conversely, CD40 Ligand, EGF, IL-1β, PDGF-AA, and TGF-α exhibited significantly lower concentrations in PTC compared to controls. Among the five classification algorithms evaluated, XGBoost demonstrated superior performance in terms of accuracy, precision, sensitivity (recall), specificity, F1-score, and ROC-AUC score. Notably, EGF and IL-10 were identified as critical cytokines that significantly contributed to the differentiation of patients with PTC. : A total of 5 cytokines showed lower levels in the PTC group than in the control, while 10 cytokines showed higher levels. While XGBoost demonstrated the best performance in discriminating between the PTC group and the control group, EGF and IL-10 were considered to be closely associated with PTC.
近期研究分析了甲状腺乳头状癌（PTC）患者的一些细胞因子，但同时分析多种细胞因子的情况仍然少见。尽管如此，同时评估多种细胞因子对于了解PTC中的细胞因子特征和发病机制越来越被认为至关重要。此外，基于血清多种细胞因子数据应用人工智能（AI）鉴别PTC患者的研究也很少。在此，我们检测并比较了PTC患者和健康个体中的46种细胞因子，并应用AI算法对两组进行分类。：从63例PTC患者和63例对照个体中分离出血清。使用Luminex检测人XL细胞因子组同时分析46种细胞因子。从电子病历中获取了一些实验室检查结果。采用Student's检验或Mann-Whitney U检验分析两组之间的差异。作为对PTC患者进行分类的AI分类算法，采用了K近邻函数、朴素贝叶斯分类器、逻辑回归、支持向量机和极端梯度提升（XGBoost）。SHAP分析评估了各个参数如何影响PTC患者的分类。：PTC患者中细胞因子水平，包括粒细胞-巨噬细胞集落刺激因子（GM-CSF）、干扰素-γ（IFN-γ）、白细胞介素-1受体拮抗剂（IL-1ra）、白细胞介素-7（IL-7）、白细胞介素-10（IL-10）、白细胞介素-12p40、白细胞介素-15、CC趋化因子配体20/巨噬细胞炎性蛋白-α（CCL20/MIP-α）、CC趋化因子配体5/调节激活正常T细胞表达和分泌因子（CCL5/RANTES）和肿瘤坏死因子-α（TNF-α）显著高于对照组。相反，与对照组相比，PTC中CD40配体、表皮生长因子（EGF）、白细胞介素-1β（IL-1β）、血小板衍生生长因子-AA（PDGF-AA）和转化生长因子-α（TGF-α）浓度显著降低。在评估的五种分类算法中，XGBoost在准确性、精确性、敏感性（召回率）、特异性、F1分数和ROC曲线下面积（ROC-AUC）分数方面表现出卓越性能。值得注意的是，EGF和IL-10被确定为对PTC患者鉴别有显著贡献的关键细胞因子。：共有5种细胞因子在PTC组中的水平低于对照组，而10种细胞因子水平更高。虽然XGBoost在区分PTC组和对照组方面表现最佳，但EGF和IL-10被认为与PTC密切相关。
