Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis
Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autop...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Fan, Dan-dan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Anmerkung: |
© The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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: Clinical rheumatology - London : Springer, 1982, 42(2022), 3 vom: 11. Okt., Seite 741-750 |
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| Übergeordnetes Werk: |
volume:42 ; year:2022 ; number:3 ; day:11 ; month:10 ; pages:741-750 |
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| DOI / URN: |
10.1007/s10067-022-06399-2 |
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| Katalog-ID: |
SPR049349724 |
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| 520 | |a Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. | ||
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10.1007/s10067-022-06399-2 doi (DE-627)SPR049349724 (SPR)s10067-022-06399-2-e DE-627 ger DE-627 rakwb eng Fan, Dan-dan verfasserin aut Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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. Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. Autophagy (dpeaa)DE-He213 Bioinformatic analyses (dpeaa)DE-He213 Gene expression, Gene expression Omnibus dataset (dpeaa)DE-He213 Rheumatoid arthritis (dpeaa)DE-He213 Tan, Peng-yu aut Jin, Li aut Qu, Yuan aut Yu, Qing-hong (orcid)0000-0003-3507-3667 aut Enthalten in Clinical rheumatology London : Springer, 1982 42(2022), 3 vom: 11. Okt., Seite 741-750 (DE-627)27159909X (DE-600)1480901-1 1434-9949 nnns volume:42 year:2022 number:3 day:11 month:10 pages:741-750 https://dx.doi.org/10.1007/s10067-022-06399-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 42 2022 3 11 10 741-750 |
| spelling |
10.1007/s10067-022-06399-2 doi (DE-627)SPR049349724 (SPR)s10067-022-06399-2-e DE-627 ger DE-627 rakwb eng Fan, Dan-dan verfasserin aut Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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. Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. Autophagy (dpeaa)DE-He213 Bioinformatic analyses (dpeaa)DE-He213 Gene expression, Gene expression Omnibus dataset (dpeaa)DE-He213 Rheumatoid arthritis (dpeaa)DE-He213 Tan, Peng-yu aut Jin, Li aut Qu, Yuan aut Yu, Qing-hong (orcid)0000-0003-3507-3667 aut Enthalten in Clinical rheumatology London : Springer, 1982 42(2022), 3 vom: 11. Okt., Seite 741-750 (DE-627)27159909X (DE-600)1480901-1 1434-9949 nnns volume:42 year:2022 number:3 day:11 month:10 pages:741-750 https://dx.doi.org/10.1007/s10067-022-06399-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 42 2022 3 11 10 741-750 |
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10.1007/s10067-022-06399-2 doi (DE-627)SPR049349724 (SPR)s10067-022-06399-2-e DE-627 ger DE-627 rakwb eng Fan, Dan-dan verfasserin aut Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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. Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. Autophagy (dpeaa)DE-He213 Bioinformatic analyses (dpeaa)DE-He213 Gene expression, Gene expression Omnibus dataset (dpeaa)DE-He213 Rheumatoid arthritis (dpeaa)DE-He213 Tan, Peng-yu aut Jin, Li aut Qu, Yuan aut Yu, Qing-hong (orcid)0000-0003-3507-3667 aut Enthalten in Clinical rheumatology London : Springer, 1982 42(2022), 3 vom: 11. Okt., Seite 741-750 (DE-627)27159909X (DE-600)1480901-1 1434-9949 nnns volume:42 year:2022 number:3 day:11 month:10 pages:741-750 https://dx.doi.org/10.1007/s10067-022-06399-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 42 2022 3 11 10 741-750 |
| allfieldsGer |
10.1007/s10067-022-06399-2 doi (DE-627)SPR049349724 (SPR)s10067-022-06399-2-e DE-627 ger DE-627 rakwb eng Fan, Dan-dan verfasserin aut Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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. Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. Autophagy (dpeaa)DE-He213 Bioinformatic analyses (dpeaa)DE-He213 Gene expression, Gene expression Omnibus dataset (dpeaa)DE-He213 Rheumatoid arthritis (dpeaa)DE-He213 Tan, Peng-yu aut Jin, Li aut Qu, Yuan aut Yu, Qing-hong (orcid)0000-0003-3507-3667 aut Enthalten in Clinical rheumatology London : Springer, 1982 42(2022), 3 vom: 11. Okt., Seite 741-750 (DE-627)27159909X (DE-600)1480901-1 1434-9949 nnns volume:42 year:2022 number:3 day:11 month:10 pages:741-750 https://dx.doi.org/10.1007/s10067-022-06399-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 42 2022 3 11 10 741-750 |
| allfieldsSound |
10.1007/s10067-022-06399-2 doi (DE-627)SPR049349724 (SPR)s10067-022-06399-2-e DE-627 ger DE-627 rakwb eng Fan, Dan-dan verfasserin aut Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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. Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. Autophagy (dpeaa)DE-He213 Bioinformatic analyses (dpeaa)DE-He213 Gene expression, Gene expression Omnibus dataset (dpeaa)DE-He213 Rheumatoid arthritis (dpeaa)DE-He213 Tan, Peng-yu aut Jin, Li aut Qu, Yuan aut Yu, Qing-hong (orcid)0000-0003-3507-3667 aut Enthalten in Clinical rheumatology London : Springer, 1982 42(2022), 3 vom: 11. Okt., Seite 741-750 (DE-627)27159909X (DE-600)1480901-1 1434-9949 nnns volume:42 year:2022 number:3 day:11 month:10 pages:741-750 https://dx.doi.org/10.1007/s10067-022-06399-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER 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_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_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_711 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 42 2022 3 11 10 741-750 |
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Fan, Dan-dan @@aut@@ Tan, Peng-yu @@aut@@ Jin, Li @@aut@@ Qu, Yuan @@aut@@ Yu, Qing-hong @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR049349724</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230510063234.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230217s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10067-022-06399-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR049349724</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10067-022-06399-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Fan, Dan-dan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. 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Fan, Dan-dan |
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bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis |
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Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis |
| abstract |
Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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 |
Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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 |
Objectives Rheumatoid arthritis (RA) is a chronic systemic autoimmune disorder characterized by progressive synovial inflammation and joint destruction, with a largely unknown etiology. Studies have suggested that autophagy and its expression may be involved in the pathogenesis of RA; however, autophagy-related genes in RA are still largely unidentified. Therefore, in this study, we aimed to identify and validate autophagy-related genes in RA. Methods We identified differentially expressed autophagy-related genes between patients with RA and healthy individuals using gene expression profiles in the GSE55235 dataset and R software. Subsequently, correlation analysis, protein–protein interaction, gene ontology enrichment, and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were carried out using these differentially expressed autophagy-related genes. Finally, our results were validated by examining the expression of differentially expressed autophagy-related hub genes in clinical samples using qRT-PCR. Results We identified 52 potential autophagy-related genes in RA based on bioinformatic analyses. Ten hub genes, CASP8, CTSB, TNFSF10, FADD, BAX, MYC, FOS, CDKN1A, GABARAPL1, and BNIP3, were validated to be differentially expressed and may serve as valuable prognostic markers and new potential therapeutic targets for RA via the regulation of autophagy. Conclusions Our results may help improve the understanding of RA pathogenesis. Autophagy-related genes in RA could be valuable biomarkers for diagnosis and prognosis and they might be exploited clinically as therapeutic targets in the future. © The Author(s), under exclusive licence to International League of Associations for Rheumatology (ILAR) 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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Bioinformatic identification and validation of autophagy-related genes in rheumatoid arthritis |
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Tan, Peng-yu Jin, Li Qu, Yuan Yu, Qing-hong |
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| score |
7.400448 |