Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity
Background: Keloid is a skin fibroproliferative disease with unknown pathogenesis. Metabolomics provides a new perspective for revealing biomarkers related to metabolites and their metabolic mechanisms.Method: Metabolomics and transcriptomics were used for data analysis. Quality control of the data...
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Frontiers Media S.A.
2022-02-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2021.804248/full |
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| author | Mengjie Shan Mengjie Shan Hao Liu Hao Liu Yan Hao Yan Hao Kexin Song Tian Meng Cheng Feng Youbin Wang Yongsheng Huang |
| author_facet | Mengjie Shan Mengjie Shan Hao Liu Hao Liu Yan Hao Yan Hao Kexin Song Tian Meng Cheng Feng Youbin Wang Yongsheng Huang |
| author_sort | Mengjie Shan |
| collection | DOAJ |
| description | Background: Keloid is a skin fibroproliferative disease with unknown pathogenesis. Metabolomics provides a new perspective for revealing biomarkers related to metabolites and their metabolic mechanisms.Method: Metabolomics and transcriptomics were used for data analysis. Quality control of the data was performed to standardize the data. Principal component analysis (PCA), PLS-DA, OPLS-DA, univariate analysis, CIBERSORT, neural network model, and machine learning correlation analysis were used to calculate differential metabolites. The molecular mechanisms of characteristic metabolites and differentially expressed genes were identified through enrichment analysis and topological analysis.Result: Compared with normal tissue, lipids have a tendency to decrease in keloids, while peptides have a tendency to increase in keloids. Significantly different metabolites between the two groups were identified by random forest analysis, including 1-methylnicotinamide, 4-hydroxyproline, 5-hydroxylysine, and l-prolinamide. The metabolic pathways which play important roles in the pathogenesis of keloids included arachidonic acid metabolism and d-arginine and d-ornithine metabolism. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide are metabolic indicators of keloid severity. The high-risk early warning index for 5-hydroxylysine is 4 × 108-6.3×108 (p = 0.0008), and the high-risk predictive index for 1-methylnicotinamide is 0.95 × 107-1.6×107 (p = 0.0022).Conclusion: This study was the first to reveal the metabolome profile and transcriptome of keloids. Differential metabolites and metabolic pathways were calculated by machine learning. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide may be metabolic indicators of keloid severity. |
| first_indexed | 2024-12-13T13:23:49Z |
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| language | English |
| last_indexed | 2024-12-13T13:23:49Z |
| publishDate | 2022-02-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj.art-3333db41d2ca4ea583a2fd59d7b30edd2022-12-21T23:44:21ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-02-011210.3389/fgene.2021.804248804248Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid SeverityMengjie Shan0Mengjie Shan1Hao Liu2Hao Liu3Yan Hao4Yan Hao5Kexin Song6Tian Meng7Cheng Feng8Youbin Wang9Yongsheng Huang10Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing, ChinaGraduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Plastic Surgery, Peking Union Medical College Hospital, Beijing, ChinaGraduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Plastic Surgery, Peking Union Medical College Hospital, Beijing, ChinaGraduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Plastic Surgery, Peking Union Medical College Hospital, Beijing, ChinaDepartment of Plastic Surgery, Peking Union Medical College Hospital, Beijing, ChinaDepartment of Plastic Surgery, Peking Union Medical College Hospital, Beijing, ChinaDepartment of Plastic Surgery, Peking Union Medical College Hospital, Beijing, ChinaGraduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaBackground: Keloid is a skin fibroproliferative disease with unknown pathogenesis. Metabolomics provides a new perspective for revealing biomarkers related to metabolites and their metabolic mechanisms.Method: Metabolomics and transcriptomics were used for data analysis. Quality control of the data was performed to standardize the data. Principal component analysis (PCA), PLS-DA, OPLS-DA, univariate analysis, CIBERSORT, neural network model, and machine learning correlation analysis were used to calculate differential metabolites. The molecular mechanisms of characteristic metabolites and differentially expressed genes were identified through enrichment analysis and topological analysis.Result: Compared with normal tissue, lipids have a tendency to decrease in keloids, while peptides have a tendency to increase in keloids. Significantly different metabolites between the two groups were identified by random forest analysis, including 1-methylnicotinamide, 4-hydroxyproline, 5-hydroxylysine, and l-prolinamide. The metabolic pathways which play important roles in the pathogenesis of keloids included arachidonic acid metabolism and d-arginine and d-ornithine metabolism. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide are metabolic indicators of keloid severity. The high-risk early warning index for 5-hydroxylysine is 4 × 108-6.3×108 (p = 0.0008), and the high-risk predictive index for 1-methylnicotinamide is 0.95 × 107-1.6×107 (p = 0.0022).Conclusion: This study was the first to reveal the metabolome profile and transcriptome of keloids. Differential metabolites and metabolic pathways were calculated by machine learning. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide may be metabolic indicators of keloid severity.https://www.frontiersin.org/articles/10.3389/fgene.2021.804248/fullkeloidmetabolomicrandom forestPLS-DAmachine learningneural network model |
| spellingShingle | Mengjie Shan Mengjie Shan Hao Liu Hao Liu Yan Hao Yan Hao Kexin Song Tian Meng Cheng Feng Youbin Wang Yongsheng Huang Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity Frontiers in Genetics keloid metabolomic random forest PLS-DA machine learning neural network model |
| title | Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity |
| title_full | Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity |
| title_fullStr | Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity |
| title_full_unstemmed | Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity |
| title_short | Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity |
| title_sort | metabolomic profiling reveals that 5 hydroxylysine and 1 methylnicotinamide are metabolic indicators of keloid severity |
| topic | keloid metabolomic random forest PLS-DA machine learning neural network model |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2021.804248/full |
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