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基于机器学习的 Graves 眼病诊断标志物预测。

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

机构信息

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.

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.

出版信息

Endocrine. 2023 Aug;81(2):277-289. doi: 10.1007/s12020-023-03349-z. Epub 2023 Apr 15.

DOI:10.1007/s12020-023-03349-z
PMID:37059863
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10293385/
Abstract

PURPOSE

The 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.

METHODS

The 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).

RESULTS

A 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.

CONCLUSION

NKD2 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.

摘要

目的

格雷夫斯眼病/甲状腺相关性眼病(TAO)的发病机制尚不清楚,已有报道称 TAO 中存在异常的 DNA 甲基化。因此,选择和探索与 DNA 甲基化相关的 TAO 生物标志物可能为新的治疗靶点提供参考。

方法

从基因表达综合数据库中下载 TAO 相关的表达数据和甲基化数据。首先,采用加权基因共表达网络分析获得 TAO 相关基因,与 TAO 样本和正常样本之间差异甲基化基因(DMGs)和差异表达基因进行交集,获得与 TAO 相关的 DMGs(TA-DMGs)。然后,分析 TA-DMGs 的功能,通过最小绝对收缩和选择算子(Lasso)回归分析和支持向量机(SVM)分析筛选诊断标志物。分析诊断标志物的表达水平和诊断价值。此外,使用基因集富集分析(GSEA)软件分别对每个诊断标志物进行单基因通路富集分析。接下来,我们还使用单样本 GSEA 算法对 GSE58331 数据集的每个样本进行免疫浸润分析,探讨诊断标志物与差异免疫细胞的相关性。最后,通过定量实时聚合酶链反应(qRT-PCR)探索诊断标志物的表达。

结果

共获得 125 个 TA-DMGs。富集分析结果表明,这些 TA-DMGs主要参与免疫相关途径,如 Th1 和 Th2 细胞分化和固有免疫反应的调节。此外,通过 Lasso 回归分析和 SVM 分析获得了两个诊断标志物,包括 S100A11 和 NKD2。单基因通路富集分析表明,S100A11 参与了蛋白质多聚化、胰腺介导的蛋白水解,而 NKD2 则参与了固有免疫反应在粘膜、Wnt 信号通路等。同时,免疫细胞浸润分析筛选出 12 种免疫细胞,包括 CD56dim 自然杀伤细胞和中性粒细胞,它们在 TAO 和正常样本之间有显著差异,其中 NKD2 与 CD56dim 自然杀伤细胞的相关性最强。最后,qRT-PCR 表明了 NKD2 和 S100A11 在正常和 TAO 之间的表达。

结论

筛选出 NKD2 和 S100A11 作为 TAO 的生物标志物,它们可能受 TAO 中 DNA 甲基化的调控,为 TAO 患者的诊断和治疗提供了新的参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/a2e6fc4ec9e7/12020_2023_3349_Fig9_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/229dfcee0dcc/12020_2023_3349_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/faee8e9bdbce/12020_2023_3349_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/ac61a36688e1/12020_2023_3349_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/b5c29db35888/12020_2023_3349_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/a70c646dda03/12020_2023_3349_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/5b8c73f38ae7/12020_2023_3349_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95f4/10293385/a2e6fc4ec9e7/12020_2023_3349_Fig9_HTML.jpg

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