tetano
Editor, Senior Moderator
Ann Med
. 2025 Dec;57(1):2477301.
doi: 10.1080/07853890.2025.2477301. Epub 2025 Mar 12. Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients
Wenchao Zhou[SUP] 1 2 [/SUP], Hui Li[SUP] 1 [/SUP], Juan Zhang[SUP] 1 [/SUP], Changsheng Liu[SUP] 3 [/SUP], Dan Liu[SUP] 1 [/SUP], Xupeng Chen[SUP] 2 [/SUP], Jing Ouyang[SUP] 4 [/SUP], Tian Zeng[SUP] 3 [/SUP], Shuang Peng[SUP] 4 [/SUP], Fan Ouyang[SUP] 5 [/SUP], Yunzhu Long[SUP] 4 [/SUP], Yukun Li[SUP] 1 [/SUP]
Affiliations
Background: Butyrate may inhibit SARS-CoV-2 replication and affect the development of COVID-19. However, there have been no systematic comprehensive analyses of the role of butyrate metabolism-related genes (BMRGs) in COVID-19.
Methods: We performed differential expression analysis of BMRGs in the brain, liver and pancreas of COVID-19 patients and controls in GSE157852 and GSE151803. The differentially expressed genes (DEGs) and module genes between COVID-19 patients and healthy controls in GSE171110 were screened through 'limma' and 'WGANA' R package, respectively, followed by an intersection with BMRGs via 'ggvenn' R package. Six machine learning algorithms were employed to determine the best model for identifying biomarkers, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of the biomarkers in COVID-19. Moreover, the differences in immune-infiltrating cells between the COVID-19 and control groups were compared using CIBERSORT. The differences in immune cells and expression levels of biomarkers in immune cells among different tissues were analysed using GSE171668.
Results: The BMRGs were the most different in the brain between the COVID-19 and control groups, including 21 upregulated and 16 downregulated genes. Five important common BMRGs were screened as biomarkers for COVID-19 using XGBoost, namely CCNB1, CCNA2, BRCA1, HBB and HSPA5, with increased diagnostic performance. Enrichment analysis revealed that these five genes were related to the cell cycle, cell proliferation and cell senescence. The infiltrating abundance of 12 immune cells was different between the COVID-19 and control groups. Finally, the expression levels of HSPA5, BRCA1 and HBB were higher in annotated cells than in CCNB1 and CCNA2, and there were four different types of immune cells in the liver, heart, lungs and kidneys.
Conclusions: These five genes may be potential biomarkers of butyrate metabolism in COVID-19 patients. These findings provide a direction for further studies on the molecular mechanisms underlying COVID-19.
Keywords: COVID-19; biomarkers; butyrate metabolism; organoid; single-cell sequencing.
. 2025 Dec;57(1):2477301.
doi: 10.1080/07853890.2025.2477301. Epub 2025 Mar 12. Identification and mechanism analysis of biomarkers related to butyrate metabolism in COVID-19 patients
Wenchao Zhou[SUP] 1 2 [/SUP], Hui Li[SUP] 1 [/SUP], Juan Zhang[SUP] 1 [/SUP], Changsheng Liu[SUP] 3 [/SUP], Dan Liu[SUP] 1 [/SUP], Xupeng Chen[SUP] 2 [/SUP], Jing Ouyang[SUP] 4 [/SUP], Tian Zeng[SUP] 3 [/SUP], Shuang Peng[SUP] 4 [/SUP], Fan Ouyang[SUP] 5 [/SUP], Yunzhu Long[SUP] 4 [/SUP], Yukun Li[SUP] 1 [/SUP]
Affiliations
- PMID: 40074706
- DOI: 10.1080/07853890.2025.2477301
Background: Butyrate may inhibit SARS-CoV-2 replication and affect the development of COVID-19. However, there have been no systematic comprehensive analyses of the role of butyrate metabolism-related genes (BMRGs) in COVID-19.
Methods: We performed differential expression analysis of BMRGs in the brain, liver and pancreas of COVID-19 patients and controls in GSE157852 and GSE151803. The differentially expressed genes (DEGs) and module genes between COVID-19 patients and healthy controls in GSE171110 were screened through 'limma' and 'WGANA' R package, respectively, followed by an intersection with BMRGs via 'ggvenn' R package. Six machine learning algorithms were employed to determine the best model for identifying biomarkers, and receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of the biomarkers in COVID-19. Moreover, the differences in immune-infiltrating cells between the COVID-19 and control groups were compared using CIBERSORT. The differences in immune cells and expression levels of biomarkers in immune cells among different tissues were analysed using GSE171668.
Results: The BMRGs were the most different in the brain between the COVID-19 and control groups, including 21 upregulated and 16 downregulated genes. Five important common BMRGs were screened as biomarkers for COVID-19 using XGBoost, namely CCNB1, CCNA2, BRCA1, HBB and HSPA5, with increased diagnostic performance. Enrichment analysis revealed that these five genes were related to the cell cycle, cell proliferation and cell senescence. The infiltrating abundance of 12 immune cells was different between the COVID-19 and control groups. Finally, the expression levels of HSPA5, BRCA1 and HBB were higher in annotated cells than in CCNB1 and CCNA2, and there were four different types of immune cells in the liver, heart, lungs and kidneys.
Conclusions: These five genes may be potential biomarkers of butyrate metabolism in COVID-19 patients. These findings provide a direction for further studies on the molecular mechanisms underlying COVID-19.
Keywords: COVID-19; biomarkers; butyrate metabolism; organoid; single-cell sequencing.