Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses
Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online G...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhang, Yihan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Differentially expressed genes |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Endocrine - [S.l.] : Springer, 1995, 78(2022), 2 vom: 13. Aug., Seite 306-314 |
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| Übergeordnetes Werk: |
volume:78 ; year:2022 ; number:2 ; day:13 ; month:08 ; pages:306-314 |
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| DOI / URN: |
10.1007/s12020-022-03156-y |
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| Katalog-ID: |
SPR048401552 |
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| 520 | |a Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. | ||
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| 650 | 4 | |a Differentially expressed genes |7 (dpeaa)DE-He213 | |
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10.1007/s12020-022-03156-y doi (DE-627)SPR048401552 (SPR)s12020-022-03156-y-e DE-627 ger DE-627 rakwb eng Zhang, Yihan verfasserin aut Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. Graves’ disease (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Immune cell infiltration analysis (dpeaa)DE-He213 Resting memory CD4 (dpeaa)DE-He213 T cells (dpeaa)DE-He213 Wei, Jia aut Zhou, Hong aut Li, Bingxin aut Chen, Ying aut Qian, Feng aut Liu, Jingting aut Xie, Xin aut Xu, Huanbai (orcid)0000-0002-4294-0274 aut Enthalten in Endocrine [S.l.] : Springer, 1995 78(2022), 2 vom: 13. Aug., Seite 306-314 (DE-627)343970171 (DE-600)2074043-8 1559-0100 nnns volume:78 year:2022 number:2 day:13 month:08 pages:306-314 https://dx.doi.org/10.1007/s12020-022-03156-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 2 13 08 306-314 |
| spelling |
10.1007/s12020-022-03156-y doi (DE-627)SPR048401552 (SPR)s12020-022-03156-y-e DE-627 ger DE-627 rakwb eng Zhang, Yihan verfasserin aut Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. Graves’ disease (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Immune cell infiltration analysis (dpeaa)DE-He213 Resting memory CD4 (dpeaa)DE-He213 T cells (dpeaa)DE-He213 Wei, Jia aut Zhou, Hong aut Li, Bingxin aut Chen, Ying aut Qian, Feng aut Liu, Jingting aut Xie, Xin aut Xu, Huanbai (orcid)0000-0002-4294-0274 aut Enthalten in Endocrine [S.l.] : Springer, 1995 78(2022), 2 vom: 13. Aug., Seite 306-314 (DE-627)343970171 (DE-600)2074043-8 1559-0100 nnns volume:78 year:2022 number:2 day:13 month:08 pages:306-314 https://dx.doi.org/10.1007/s12020-022-03156-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 2 13 08 306-314 |
| allfields_unstemmed |
10.1007/s12020-022-03156-y doi (DE-627)SPR048401552 (SPR)s12020-022-03156-y-e DE-627 ger DE-627 rakwb eng Zhang, Yihan verfasserin aut Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. Graves’ disease (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Immune cell infiltration analysis (dpeaa)DE-He213 Resting memory CD4 (dpeaa)DE-He213 T cells (dpeaa)DE-He213 Wei, Jia aut Zhou, Hong aut Li, Bingxin aut Chen, Ying aut Qian, Feng aut Liu, Jingting aut Xie, Xin aut Xu, Huanbai (orcid)0000-0002-4294-0274 aut Enthalten in Endocrine [S.l.] : Springer, 1995 78(2022), 2 vom: 13. Aug., Seite 306-314 (DE-627)343970171 (DE-600)2074043-8 1559-0100 nnns volume:78 year:2022 number:2 day:13 month:08 pages:306-314 https://dx.doi.org/10.1007/s12020-022-03156-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 2 13 08 306-314 |
| allfieldsGer |
10.1007/s12020-022-03156-y doi (DE-627)SPR048401552 (SPR)s12020-022-03156-y-e DE-627 ger DE-627 rakwb eng Zhang, Yihan verfasserin aut Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. Graves’ disease (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Immune cell infiltration analysis (dpeaa)DE-He213 Resting memory CD4 (dpeaa)DE-He213 T cells (dpeaa)DE-He213 Wei, Jia aut Zhou, Hong aut Li, Bingxin aut Chen, Ying aut Qian, Feng aut Liu, Jingting aut Xie, Xin aut Xu, Huanbai (orcid)0000-0002-4294-0274 aut Enthalten in Endocrine [S.l.] : Springer, 1995 78(2022), 2 vom: 13. Aug., Seite 306-314 (DE-627)343970171 (DE-600)2074043-8 1559-0100 nnns volume:78 year:2022 number:2 day:13 month:08 pages:306-314 https://dx.doi.org/10.1007/s12020-022-03156-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 2 13 08 306-314 |
| allfieldsSound |
10.1007/s12020-022-03156-y doi (DE-627)SPR048401552 (SPR)s12020-022-03156-y-e DE-627 ger DE-627 rakwb eng Zhang, Yihan verfasserin aut Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. Graves’ disease (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Immune cell infiltration analysis (dpeaa)DE-He213 Resting memory CD4 (dpeaa)DE-He213 T cells (dpeaa)DE-He213 Wei, Jia aut Zhou, Hong aut Li, Bingxin aut Chen, Ying aut Qian, Feng aut Liu, Jingting aut Xie, Xin aut Xu, Huanbai (orcid)0000-0002-4294-0274 aut Enthalten in Endocrine [S.l.] : Springer, 1995 78(2022), 2 vom: 13. Aug., Seite 306-314 (DE-627)343970171 (DE-600)2074043-8 1559-0100 nnns volume:78 year:2022 number:2 day:13 month:08 pages:306-314 https://dx.doi.org/10.1007/s12020-022-03156-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78 2022 2 13 08 306-314 |
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Enthalten in Endocrine 78(2022), 2 vom: 13. Aug., Seite 306-314 volume:78 year:2022 number:2 day:13 month:08 pages:306-314 |
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Enthalten in Endocrine 78(2022), 2 vom: 13. Aug., Seite 306-314 volume:78 year:2022 number:2 day:13 month:08 pages:306-314 |
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Zhang, Yihan @@aut@@ Wei, Jia @@aut@@ Zhou, Hong @@aut@@ Li, Bingxin @@aut@@ Chen, Ying @@aut@@ Qian, Feng @@aut@@ Liu, Jingting @@aut@@ Xie, Xin @@aut@@ Xu, Huanbai @@aut@@ |
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Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Graves’ disease</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Differentially expressed genes</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Immune cell infiltration analysis</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Resting memory CD4</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">T cells</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wei, Jia</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Hong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Li, Bingxin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Ying</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Qian, Feng</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Jingting</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xie, Xin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Xu, Huanbai</subfield><subfield code="0">(orcid)0000-0002-4294-0274</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Endocrine</subfield><subfield code="d">[S.l.] : Springer, 1995</subfield><subfield code="g">78(2022), 2 vom: 13. 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Zhang, Yihan |
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Zhang, Yihan misc Graves’ disease misc Differentially expressed genes misc Immune cell infiltration analysis misc Resting memory CD4 misc T cells Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses |
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Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses Graves’ disease (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Immune cell infiltration analysis (dpeaa)DE-He213 Resting memory CD4 (dpeaa)DE-He213 T cells (dpeaa)DE-He213 |
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identification of two potential immune-related biomarkers of graves’ disease based on integrated bioinformatics analyses |
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Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses |
| abstract |
Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Background Graves’ disease (GD) is an autoimmune disease, the incidence of which is increasing yearly. GD requires long-life therapy. Therefore, the potential immune-related biomarkers of GD need to be studied. Method In our study, differentially expressed genes (DEGs) were derived from the online Gene Expression Omnibus (GEO) microarray expression dataset GSE71956. Protein‒protein interaction (PPI) network analyses were used to identify hub genes, which were validated by qPCR. GSEA was used to screen potential pathways and related immune cells. Next, CIBERSORT analysis was used to further explore the immune subtype distribution pattern among hub genes. ROC curves were used to analyze the specificity and sensitivity of hub genes. Result 44 DEGs were screened from the GEO dataset. Two hub genes, EEF1A1 and EIF4B, were obtained from the PPI network and validated by qPCR (p < 0.05). GSEA was conducted to identify potential pathways and immune cells related to these the two hub genes. Immune cell subtype analysis revealed that hub genes had extensive associations with many different types of immune cells, particularly resting memory $ CD4^{+} $ T cells. AUCs of ROC analysis were 0.687 and 0.733 for EEF1A1 and EIF4B, respectively. Conclusion Our study revealed two hub genes, EEF1A1 and EIF4B, that are associated with resting memory $ CD4^{+} $ T cells and potential immune-related molecular biomarkers and therapeutic targets of GD. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| collection_details |
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| container_issue |
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| title_short |
Identification of two potential immune-related biomarkers of Graves’ disease based on integrated bioinformatics analyses |
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https://dx.doi.org/10.1007/s12020-022-03156-y |
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Wei, Jia Zhou, Hong Li, Bingxin Chen, Ying Qian, Feng Liu, Jingting Xie, Xin Xu, Huanbai |
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Wei, Jia Zhou, Hong Li, Bingxin Chen, Ying Qian, Feng Liu, Jingting Xie, Xin Xu, Huanbai |
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10.1007/s12020-022-03156-y |
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2024-07-03T18:58:32.943Z |
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| score |
7.400139 |