高级检索
当前位置: 首页 > 详情页

Machine learning-based prediction of diagnostic markers for Graves' orbitopathy

文献详情

资源类型:
WOS体系:

收录情况: ◇ SCIE

机构: [1]Department of Endocrinology, The First People’s Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, Kunming City, Yunnan Provence, China [2]Department of Ophthalmology, The First People’s Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, Kunming City, Yunnan Provence, China
出处:
ISSN:

关键词: Graves' orbitopathy Methylation Immune cell infiltration Machine learning Bioinformatics

摘要:
PurposeThe pathogenesis of Graves' orbitopathy/thyroid-associated orbitopathy (TAO) is still unclear, and abnormal DNA methylation in TAO has been reported. Thus, selecting and exploring TAO biomarkers associated with DNA methylation may provide a reference for new therapeutic targets.MethodsThe TAO-associated expression data and methylation data were downloaded from The Gene Expression Omnibus database. Firstly, weighted gene co-expression network analysis was used to obtain the TAO-related genes, which were intersected with differentially methylated genes (DMGs), and differentially expressed genes between TAO samples and normal samples to obtain TAO-associated DMGs (TA-DMGs). Thereafter, the functions of the TA-DMGs were analyzed, and diagnostic markers were screened by least absolute shrinkage and selection operator (Lasso) regression analysis and support vector machine (SVM) analysis. The expression levels and diagnostic values of the diagnostic markers were also analyzed. Furthermore, single gene pathway enrichment analysis was performed for each diagnostic marker separately using gene set enrichment analysis (GSEA) software. Next, we also performed immune infiltration analysis for each sample in the GSE58331 dataset using the single-sample GSEA algorithm, and the correlation between diagnostic markers and differential immune cells was explored. Lastly, the expressions of diagnostic markers were explored by quantitative real-time polymerase chain reaction (qRT-PCR).ResultsA total of 125 TA-DMGs were obtained. The enrichment analysis results indicated that these TA-DMGs were mainly involved in immune-related pathways, such as Th1 and Th2 cell differentiation and the regulation of innate immune response. Moreover, two diagnostic markers, including S100A11 and NKD2, were obtained by Lasso regression analysis and SVM analysis. Single gene pathway enrichment analysis showed that S100A11 was involved in protein polyufmylation, pancreatic-mediated proteolysis, and NKD2 was involved in innate immune response in mucosa, Wnt signaling pathway, etc. Meanwhile, immune cell infiltration analysis screened 12 immune cells, including CD56 dim natural killer cells and Neutrophil cells that significantly differed between TAO and normal samples, with the strongest positive correlation between NKD2 and CD56 dim natural killer cells. Finally, the qRT-PCR illustrated the expressions of NKD2 and S100A11 between normal and TAO.ConclusionNKD2 and S100A11 were screened as biomarkers of TAO and might be regulated by DNA methylation in TAO, providing a new reference for the diagnosis and treatment of TAO patients.

基金:
语种:
被引次数:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2023]版:
大类 | 3 区 医学
小类 | 3 区 内分泌学与代谢
最新[2023]版:
大类 | 3 区 医学
小类 | 3 区 内分泌学与代谢
JCR分区:
出版当年[2022]版:
Q3 ENDOCRINOLOGY & METABOLISM
最新[2023]版:
Q2 ENDOCRINOLOGY & METABOLISM

影响因子: 最新[2023版] 最新五年平均 出版当年[2022版] 出版当年五年平均 出版前一年[2021版] 出版后一年[2023版]

第一作者:
第一作者机构: [1]Department of Endocrinology, The First People’s Hospital of Yunnan Province. The Affiliated Hospital of Kunming University of Science and Technology, Kunming City, Yunnan Provence, China
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:82490 今日访问量:0 总访问量:681 更新日期:2025-01-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 云南省第一人民医院 技术支持:重庆聚合科技有限公司 地址:云南省昆明市西山区金碧路157号