Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning
BackgroundIn the pathogenesis of osteoarthritis (OA) and metabolic syndrome (MetS), the immune system plays a particularly important role. The purpose of this study was to find key diagnostic candidate genes in OA patients who also had metabolic syndrome.MethodsWe searched the Gene Expression Omnibu...
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1134412/full |
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| author | Junchen Li Genghong Wang Xilin Xv Xilin Xv Zhigang Li Yiwei Shen Cheng Zhang Xiaofeng Zhang Xiaofeng Zhang |
| author_facet | Junchen Li Genghong Wang Xilin Xv Xilin Xv Zhigang Li Yiwei Shen Cheng Zhang Xiaofeng Zhang Xiaofeng Zhang |
| author_sort | Junchen Li |
| collection | DOAJ |
| description | BackgroundIn the pathogenesis of osteoarthritis (OA) and metabolic syndrome (MetS), the immune system plays a particularly important role. The purpose of this study was to find key diagnostic candidate genes in OA patients who also had metabolic syndrome.MethodsWe searched the Gene Expression Omnibus (GEO) database for three OA and one MetS dataset. Limma, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify and analyze the immune genes associated with OA and MetS. They were evaluated using nomograms and receiver operating characteristic (ROC) curves, and finally, immune cells dysregulated in OA were investigated using immune infiltration analysis.ResultsAfter Limma analysis, the integrated OA dataset yielded 2263 DEGs, and the MetS dataset yielded the most relevant module containing 691 genes after WGCNA, with a total of 82 intersections between the two. The immune-related genes were mostly enriched in the enrichment analysis, and the immune infiltration analysis revealed an imbalance in multiple immune cells. Further machine learning screening yielded eight core genes that were evaluated by nomogram and diagnostic value and found to have a high diagnostic value (area under the curve from 0.82 to 0.96).ConclusionEight immune-related core genes were identified (FZD7, IRAK3, KDELR3, PHC2, RHOB, RNF170, SOX13, and ZKSCAN4), and a nomogram for the diagnosis of OA and MetS was established. This research could lead to the identification of potential peripheral blood diagnostic candidate genes for MetS patients who also suffer from OA. |
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| language | English |
| last_indexed | 2024-03-13T03:10:42Z |
| publishDate | 2023-04-01 |
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| series | Frontiers in Immunology |
| spelling | doaj.art-47f3883930714696adf968500d4ada202023-06-26T13:42:30ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-04-011410.3389/fimmu.2023.11344121134412Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learningJunchen Li0Genghong Wang1Xilin Xv2Xilin Xv3Zhigang Li4Yiwei Shen5Cheng Zhang6Xiaofeng Zhang7Xiaofeng Zhang8The Graduate School, Heilongjiang University of Chinese Medicine, Harbin, ChinaThe Graduate School, Heilongjiang University of Chinese Medicine, Harbin, ChinaThe Third Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, ChinaTeaching and Research Section of Orthopedics and Traumatology, Heilongjiang University of Chinese Medicine, Harbin, ChinaThe Second Department of Orthopedics and Traumatology, The Second Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, ChinaThe Graduate School, Heilongjiang University of Chinese Medicine, Harbin, ChinaThe Graduate School, Heilongjiang University of Chinese Medicine, Harbin, ChinaTeaching and Research Section of Orthopedics and Traumatology, Heilongjiang University of Chinese Medicine, Harbin, ChinaThe Bone Injury Teaching Laboratory, Heilongjiang University of Chinese Medicine, Harbin, ChinaBackgroundIn the pathogenesis of osteoarthritis (OA) and metabolic syndrome (MetS), the immune system plays a particularly important role. The purpose of this study was to find key diagnostic candidate genes in OA patients who also had metabolic syndrome.MethodsWe searched the Gene Expression Omnibus (GEO) database for three OA and one MetS dataset. Limma, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify and analyze the immune genes associated with OA and MetS. They were evaluated using nomograms and receiver operating characteristic (ROC) curves, and finally, immune cells dysregulated in OA were investigated using immune infiltration analysis.ResultsAfter Limma analysis, the integrated OA dataset yielded 2263 DEGs, and the MetS dataset yielded the most relevant module containing 691 genes after WGCNA, with a total of 82 intersections between the two. The immune-related genes were mostly enriched in the enrichment analysis, and the immune infiltration analysis revealed an imbalance in multiple immune cells. Further machine learning screening yielded eight core genes that were evaluated by nomogram and diagnostic value and found to have a high diagnostic value (area under the curve from 0.82 to 0.96).ConclusionEight immune-related core genes were identified (FZD7, IRAK3, KDELR3, PHC2, RHOB, RNF170, SOX13, and ZKSCAN4), and a nomogram for the diagnosis of OA and MetS was established. This research could lead to the identification of potential peripheral blood diagnostic candidate genes for MetS patients who also suffer from OA.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1134412/fulldifferentially expressed genesosteoarthritismetabolic syndromemachine learningimmune infiltration |
| spellingShingle | Junchen Li Genghong Wang Xilin Xv Xilin Xv Zhigang Li Yiwei Shen Cheng Zhang Xiaofeng Zhang Xiaofeng Zhang Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning Frontiers in Immunology differentially expressed genes osteoarthritis metabolic syndrome machine learning immune infiltration |
| title | Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_full | Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_fullStr | Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_full_unstemmed | Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_short | Identification of immune-associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| title_sort | identification of immune associated genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning |
| topic | differentially expressed genes osteoarthritis metabolic syndrome machine learning immune infiltration |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1134412/full |
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