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other·ophthalmology, ocular immunology, biomedical research, research methods·PMC10611488

Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis

Frontiers in Endocrinology · 10 authors, 4 centres

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TL;DR

This computational study reanalyzed the GSE58331 microarray dataset to investigate TAO pathogenesis.

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This computational study reanalyzed the GSE58331 microarray dataset to investigate TAO pathogenesis. Researchers identified 366 differentially expressed genes and used weighted gene coexpression network analysis (WGCNA) and three machine learning algorithms to pinpoint six optimal feature genes (OFGs): KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697. CIBERSORT analysis of immune infiltration showed TAO samples had higher proportions of memory B cells, T follicular helper cells, and several macrophage/dendritic cell types compared to normal samples, while M2 macrophages and resting mast cells were lower. Single-gene set enrichment analysis (ssGSEA) linked these genes to lipid metabolism pathways. The study identifies potential diagnostic biomarkers and highlights the role of immune dysregulation and metabolic pathways in TAO, though validation through clinical research is needed.

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other

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ophthalmology

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ORIGINAL PAPER

PMC10611488

Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis

Frontiers in Endocrinology

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ABSTRACT

Abstract Background Thyroid associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on individuals and society. The etiology of TAO is complicated and poorly understood. Thus, the goal of this study was to use bioinformatics to look into the pathogenesis of TAO and to identify the optimum feature genes (OFGs) and immune infiltration patterns of TAO. Methods Firstly, the GSE58331 microarray data set was utilized to find 366 differentially expressed genes (DEGs). To find important modular genes, the dataset was evaluated using weighted gene coexpression network analysis (WGCNA). Then, the overlap genes of major module genes and DEGs were further assessed by applying three machine learning techniques to find the OFGs. The CIBERSORT approach was utilized to examine immune cell infiltration in normal and TAO samples, as well as the link between optimum characteristic genes and immune cells. Finally, the related pathways of the OFGs were predicted using single gene set enrichment analysis (ssGSEA). Results KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 were the six best feature genes that were employed to create a nomogram with high predictive performance. The immune cell infiltration investigation revealed that the development of TAO may include memory B cells, T cell follicular helper cells, resting NK cells, macrophages of type M0, macrophages of type M1, resting dendritic cells, active mast cells, and neutrophils. In addition, ssGSEA results found that these characteristic genes were closely associated with lipid metabolism pathways. Conclusion In this research, we found that KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 are intimately associated with the development and progression of TAO, as well as with lipid metabolism pathways. Keywords: thyroid associated ophthalmopathy, optimal feature genes, pathogenesis, immune infiltration, GEO

CITATION EXPORT

Chao Xiong, Yaohua Wang, Yue Li et al.. Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis. Frontiers in Endocrinology. (2023). https://doi.org/10.3389/fendo.2023.1203120

AUTHORS

10 · 4 CENTRES

Chao Xiong, Yaohua Wang, Yue Li, Jinhai Yu, Sha Wu, Lili Wu, Boyuan Zhang, Yunxiu Chen, Puying Gan, Hongfei Liao

1 Department of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, China
2 Jiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, China
3 Jiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, China
4 Jiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, China