Comparison of 46 Cytokines in Peripheral Blood Between Patients with Papillary Thyroid Cancer and Healthy Individuals with AI-Driven Analysis to Distinguish Between the Two Groups.

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Kyung-Jin Bae, Jun-Hyung Bae, Ae-Chin Oh, Chi-Hyun Cho
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引用次数: 0

Abstract

Background: 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. Methods: 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 t-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. Results: 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. Conclusions: 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.

甲状腺乳头状癌患者与健康人群外周血46种细胞因子的比较及ai驱动分析
背景:最近的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仍然很少见。尽管如此,同时评估多种细胞因子对于理解PTC的细胞因子特征和发育机制至关重要。此外,基于血清多种细胞因子数据,应用人工智能(AI)识别PTC患者的研究很少。在这里,我们测量并比较了PTC患者和健康个体的46种细胞因子,并应用AI算法对两组进行分类。方法:分离63例PTC患者和63例对照者的血清。使用Luminex试剂盒同时分析46种细胞因子。从电子病历中确定了几项实验室检查结果。采用学生t检验或Mann-Whitney U检验分析两组间差异。采用k近邻函数、Naïve贝叶斯分类器、逻辑回归、支持向量机、极限梯度增强(XGBoost)等人工智能分类算法对PTC患者进行分类。SHAP分析评估了个体参数如何影响PTC患者的分类。结果:细胞因子水平,包括GM-CSF、IFN-γ、IL-1ra、IL-7、IL-10、IL-12p40、IL-15、CCL20/MIP-α、CCL5/RANTES和TNF-α在PTC组均显著高于对照组。相反,CD40配体、EGF、IL-1β、PDGF-AA和TGF-α在PTC中的浓度显著低于对照组。在评估的五种分类算法中,XGBoost在准确率、精密度、灵敏度(召回率)、特异性、f1评分和ROC-AUC评分方面表现优异。值得注意的是,EGF和IL-10被确定为对PTC患者分化有重要贡献的关键细胞因子。结论:PTC组有5种细胞因子水平低于对照组,10种细胞因子水平高于对照组。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。
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来源期刊
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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