Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis
BackgroundThyroid 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 an...
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Frontiers Media S.A.
2023-10-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2023.1203120/full |
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| author | Chao Xiong Chao Xiong Chao Xiong Chao Xiong Yaohua Wang Yaohua Wang Yaohua Wang Yaohua Wang Yue Li Yue Li Yue Li Yue Li Jinhai Yu Jinhai Yu Jinhai Yu Jinhai Yu Sha Wu Sha Wu Sha Wu Sha Wu Lili Wu Lili Wu Lili Wu Lili Wu Boyuan Zhang Boyuan Zhang Boyuan Zhang Boyuan Zhang Yunxiu Chen Yunxiu Chen Yunxiu Chen Yunxiu Chen Puying Gan Puying Gan Puying Gan Puying Gan Hongfei Liao Hongfei Liao Hongfei Liao Hongfei Liao |
| author_facet | Chao Xiong Chao Xiong Chao Xiong Chao Xiong Yaohua Wang Yaohua Wang Yaohua Wang Yaohua Wang Yue Li Yue Li Yue Li Yue Li Jinhai Yu Jinhai Yu Jinhai Yu Jinhai Yu Sha Wu Sha Wu Sha Wu Sha Wu Lili Wu Lili Wu Lili Wu Lili Wu Boyuan Zhang Boyuan Zhang Boyuan Zhang Boyuan Zhang Yunxiu Chen Yunxiu Chen Yunxiu Chen Yunxiu Chen Puying Gan Puying Gan Puying Gan Puying Gan Hongfei Liao Hongfei Liao Hongfei Liao Hongfei Liao |
| author_sort | Chao Xiong |
| collection | DOAJ |
| description | BackgroundThyroid 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.MethodsFirstly, 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).ResultsKLB, 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.ConclusionIn 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. |
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| spelling | doaj.art-1ce4738f5c9a4f1cb3b72278f6269e392023-10-13T07:29:38ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922023-10-011410.3389/fendo.2023.12031201203120Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysisChao Xiong0Chao Xiong1Chao Xiong2Chao Xiong3Yaohua Wang4Yaohua Wang5Yaohua Wang6Yaohua Wang7Yue Li8Yue Li9Yue Li10Yue Li11Jinhai Yu12Jinhai Yu13Jinhai Yu14Jinhai Yu15Sha Wu16Sha Wu17Sha Wu18Sha Wu19Lili Wu20Lili Wu21Lili Wu22Lili Wu23Boyuan Zhang24Boyuan Zhang25Boyuan Zhang26Boyuan Zhang27Yunxiu Chen28Yunxiu Chen29Yunxiu Chen30Yunxiu Chen31Puying Gan32Puying Gan33Puying Gan34Puying Gan35Hongfei Liao36Hongfei Liao37Hongfei Liao38Hongfei Liao39Department of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaDepartment of Ophthalmology, Affiliated Eye Hospital of Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Clinical Research Center for Ophthalmic Disease, Nanchang, Jiangxi, ChinaJiangxi Research Institute of Ophthalmology and Visual Science, Nanchang, Jiangxi, ChinaJiangxi Provincial Key Laboratory for Ophthalmology, Nanchang, Jiangxi, ChinaBackgroundThyroid 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.MethodsFirstly, 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).ResultsKLB, 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.ConclusionIn 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.https://www.frontiersin.org/articles/10.3389/fendo.2023.1203120/fullthyroid associated ophthalmopathyoptimal feature genespathogenesisimmune infiltrationGEO |
| spellingShingle | Chao Xiong Chao Xiong Chao Xiong Chao Xiong Yaohua Wang Yaohua Wang Yaohua Wang Yaohua Wang Yue Li Yue Li Yue Li Yue Li Jinhai Yu Jinhai Yu Jinhai Yu Jinhai Yu Sha Wu Sha Wu Sha Wu Sha Wu Lili Wu Lili Wu Lili Wu Lili Wu Boyuan Zhang Boyuan Zhang Boyuan Zhang Boyuan Zhang Yunxiu Chen Yunxiu Chen Yunxiu Chen Yunxiu Chen Puying Gan Puying Gan Puying Gan Puying Gan Hongfei Liao Hongfei Liao Hongfei Liao Hongfei Liao Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis Frontiers in Endocrinology thyroid associated ophthalmopathy optimal feature genes pathogenesis immune infiltration GEO |
| title | Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis |
| title_full | Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis |
| title_fullStr | Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis |
| title_full_unstemmed | Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis |
| title_short | Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis |
| title_sort | identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration a bioinformatics analysis |
| topic | thyroid associated ophthalmopathy optimal feature genes pathogenesis immune infiltration GEO |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2023.1203120/full |
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