BMC infectious diseases · 2026
Integration of single-cell RNA-sequencing and machine learning identifies GRN and FCER1G as potential peroxisomal targets in influenza pathogenesis.
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Shan Ning, Chen Shibin, Liu Zhaoyu, Wen Zhe, Wang Junwei, Yu Yao, Liu Xining, Ning Shangwei, Chen Hong
Аннотация
BACKGROUND: Influenza, an acute respiratory infection caused by influenza viruses, imposes a significant burden on the healthcare system. Peroxisomes have been shown to be associated with various viral infections, including influenza virus infections, but the specific mechanisms of their involvement in influenza virus infection remain to be explored.
METHODS: In this re-analysis of publicly available single-cell and bulk transcriptomic datasets, single-cell scoring algorithms, including AUCell, UCell, singscore, ssgsea, and AddModuleScore, were used to determine the expression of peroxisome-related genes at the single-cell level. Differential gene expression and protein-protein interaction (PPI) analyses identified key peroxisome-related genes. Four machine learning methods, including XGBoost, Boruta, LASSO, and random forest, were integrated to identify optimal characteristic genes.
RESULTS: We observed heterogeneity in the expression of peroxisome-related genes in different cell types during influenza, with non-classical monocytes and classical monocytes having the highest gene expression scores. Compared with those in the control group, classical monocytes had higher scores in the influenza group. Through machine learning algorithms, GRN and FCER1G were identified as optimal characteristic genes, and their differential expression and diagnostic value for influenza were verified in bulk datasets, with upregulation in classical monocytes.
CONCLUSIONS: This study reveals the associations of GRN and FCER1G with peroxisomes in influenza and the heterogeneity of peroxisome-related genes at the single-cell level during influenza. Additionally, classical monocytes may play a crucial role in the functional realization of peroxisomes in the context of influenza. Our research enhances the understanding of the role of peroxisomes in influenza virus infection and may point to potential therapeutic targets for influenza.
METHODS: In this re-analysis of publicly available single-cell and bulk transcriptomic datasets, single-cell scoring algorithms, including AUCell, UCell, singscore, ssgsea, and AddModuleScore, were used to determine the expression of peroxisome-related genes at the single-cell level. Differential gene expression and protein-protein interaction (PPI) analyses identified key peroxisome-related genes. Four machine learning methods, including XGBoost, Boruta, LASSO, and random forest, were integrated to identify optimal characteristic genes.
RESULTS: We observed heterogeneity in the expression of peroxisome-related genes in different cell types during influenza, with non-classical monocytes and classical monocytes having the highest gene expression scores. Compared with those in the control group, classical monocytes had higher scores in the influenza group. Through machine learning algorithms, GRN and FCER1G were identified as optimal characteristic genes, and their differential expression and diagnostic value for influenza were verified in bulk datasets, with upregulation in classical monocytes.
CONCLUSIONS: This study reveals the associations of GRN and FCER1G with peroxisomes in influenza and the heterogeneity of peroxisome-related genes at the single-cell level during influenza. Additionally, classical monocytes may play a crucial role in the functional realization of peroxisomes in the context of influenza. Our research enhances the understanding of the role of peroxisomes in influenza virus infection and may point to potential therapeutic targets for influenza.