JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers
Objective: Gout is a prevalent form of chronic inflammatory arthritis, and its etiology remains incompletely understood. Ferroptosis is a form of cell death that relies on iron. As of now, the relationship between ferroptosis and gout is not entirely clear. Hence, the primary objective of this study...
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Elsevier
2024-11-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024159889 |
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| author | Yang Li ChengCheng Huang Yuhan Xie WenBin Liu MengJuan Wei Shudong Li Zhenguo Yang JingWu Wang Gang Li |
| author_facet | Yang Li ChengCheng Huang Yuhan Xie WenBin Liu MengJuan Wei Shudong Li Zhenguo Yang JingWu Wang Gang Li |
| author_sort | Yang Li |
| collection | DOAJ |
| description | Objective: Gout is a prevalent form of chronic inflammatory arthritis, and its etiology remains incompletely understood. Ferroptosis is a form of cell death that relies on iron. As of now, the relationship between ferroptosis and gout is not entirely clear. Hence, the primary objective of this study is to employ bioinformatics methods for the analysis and identification of potential genes associated with ferroptosis in the context of gout. Methods: Utilizing both bioinformatics analysis and machine learning algorithms to systematically identify biomarkers for gout. The gout-related dataset (GSE160170) was acquired from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were extracted from the FerrDb database. subsequently, we identified DEGs associated with ferroptosis in the context of gout. Following that, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the DEGs. Subsequently, SVM-RFE analysis and the LASSO regression model were employed for biomarker screening. Additionally, CIBERSORT software was utilized to assess the composition of twenty-two immune cells in gout, and correlation analyses between hub genes and immune cells were conducted. Results: This study screened a total of twenty-five DEGs related to Ferroptosis in healthy population and gout patient. The KEGG analysis indicates that these DEGs are predominantly enriched in: the AGE-RAGE signaling pathway, nod like receptor signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, etc. The intersection of the top 10 genes identified through PPI network, SVM-RFE analysis, and LASSO regression model resulted in two hub genes, namely JUN and ATF3. Analysis of immunocyte infiltration revealed that JUN exhibited associations with various immune cells, including NK cells resting, Monocytes, Mast cells resting, etc. ATF3, on the other hand, showed associations with immune cells Mast cells resting and Eosinophels. Conclusions: The outcomes of our study pinpointed JUN and ATF3, genes associated with ferroptosis, as promising biomarkers for both diagnosing and treating gout, providing additional evidence to support the important role of ferroptosis in gout and providing potential therapeutic methods for clinical targeted ferroptosis prevention and treatment of gout. |
| format | Article |
| id | doaj-art-c034455336c146b0ada1e02da99ca8a7 |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
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| series | Heliyon |
| spelling | doaj-art-c034455336c146b0ada1e02da99ca8a72024-11-30T07:11:39ZengElsevierHeliyon2405-84402024-11-011022e39957JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkersYang Li0ChengCheng Huang1Yuhan Xie2WenBin Liu3MengJuan Wei4Shudong Li5Zhenguo Yang6JingWu Wang7Gang Li8Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, China; Department of Orthopedic, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, ChinaDepartment of Endocrinology and Metabology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, ChinaShandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, ChinaDepartment of Orthopedic, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, ChinaDepartment of Endocrinology and Metabology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, ChinaShandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, ChinaDepartment of Orthopedic, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, ChinaDepartment of Endocrinology and Metabology, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, China; Corresponding author.Department of Orthopedic, The Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250000, China; Corresponding author.Objective: Gout is a prevalent form of chronic inflammatory arthritis, and its etiology remains incompletely understood. Ferroptosis is a form of cell death that relies on iron. As of now, the relationship between ferroptosis and gout is not entirely clear. Hence, the primary objective of this study is to employ bioinformatics methods for the analysis and identification of potential genes associated with ferroptosis in the context of gout. Methods: Utilizing both bioinformatics analysis and machine learning algorithms to systematically identify biomarkers for gout. The gout-related dataset (GSE160170) was acquired from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were extracted from the FerrDb database. subsequently, we identified DEGs associated with ferroptosis in the context of gout. Following that, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses on the DEGs. Subsequently, SVM-RFE analysis and the LASSO regression model were employed for biomarker screening. Additionally, CIBERSORT software was utilized to assess the composition of twenty-two immune cells in gout, and correlation analyses between hub genes and immune cells were conducted. Results: This study screened a total of twenty-five DEGs related to Ferroptosis in healthy population and gout patient. The KEGG analysis indicates that these DEGs are predominantly enriched in: the AGE-RAGE signaling pathway, nod like receptor signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, etc. The intersection of the top 10 genes identified through PPI network, SVM-RFE analysis, and LASSO regression model resulted in two hub genes, namely JUN and ATF3. Analysis of immunocyte infiltration revealed that JUN exhibited associations with various immune cells, including NK cells resting, Monocytes, Mast cells resting, etc. ATF3, on the other hand, showed associations with immune cells Mast cells resting and Eosinophels. Conclusions: The outcomes of our study pinpointed JUN and ATF3, genes associated with ferroptosis, as promising biomarkers for both diagnosing and treating gout, providing additional evidence to support the important role of ferroptosis in gout and providing potential therapeutic methods for clinical targeted ferroptosis prevention and treatment of gout.http://www.sciencedirect.com/science/article/pii/S2405844024159889GoutFerroptosisBioinformaticsMachine learningBiomarkers |
| spellingShingle | Yang Li ChengCheng Huang Yuhan Xie WenBin Liu MengJuan Wei Shudong Li Zhenguo Yang JingWu Wang Gang Li JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers Heliyon Gout Ferroptosis Bioinformatics Machine learning Biomarkers |
| title | JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers |
| title_full | JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers |
| title_fullStr | JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers |
| title_full_unstemmed | JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers |
| title_short | JUN and ATF3 in Gout: Ferroptosis-related potential diagnostic biomarkers |
| title_sort | jun and atf3 in gout ferroptosis related potential diagnostic biomarkers |
| topic | Gout Ferroptosis Bioinformatics Machine learning Biomarkers |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024159889 |
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