Computational biology and chemistry · 2026
Identification of shared cuproptosis- and mitophagy-related hub genes and construction of a diagnostic model in sepsis-induced acute lung injury.
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Tan Ruimin, Jin Yonggang, Yuan Yuan, Zhang Shujia, Zhang Zhenyin
Аннотация
BACKGROUND: Sepsis-induced acute lung injury (SI-ALI) is a life-threatening complication of sepsis. It is characterized by complex pathophysiology and the absence of reliable early biomarkers. Cuproptosis and mitophagy are both closely linked to mitochondrial homeostasis, but their potential molecular association in SI-ALI remains insufficiently characterized.
METHODS: Transcriptomic datasets from the Gene Expression Omnibus were analyzed, with GSE32707 serving as the discovery dataset and GSE10361 as an independent validation dataset. Differentially expressed genes (DEGs) were intersected with cuproptosis- and mitophagy-related gene sets to identify candidate genes. Biological functions were explored using Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analyses and a protein-protein interaction network. Least absolute shrinkage and selection operator regression and random forest were used to identify hub genes. A three-gene logistic regression model and nomogram were constructed and internally evaluated in the GSE32707 discovery cohort using receiver operating characteristic analysis, bootstrap calibration, and decision curve analysis. The individual hub genes were further evaluated in GSE10361. Immune infiltration was assessed using CIBERSORT, and single-cell RNA-sequencing data from GSE151263 were used to characterize cell-type-specific expression of the hub genes.
RESULTS: From 296 DEGs, 24 cuproptosis- and mitophagy-related candidate genes were identified. Machine learning identified RPL22, FTH1, and MMP8 as hub genes. MMP8 was upregulated, whereas RPL22 and FTH1 were downregulated. In the discovery cohort, the individual areas under the curve (AUCs) of RPL22, FTH1, and MMP8 were 0.826, 0.762, and 0.720, respectively, whereas the combined three-gene model achieved an AUC of 0.911. The individual hub genes showed preliminary discriminatory performance in the independent validation cohort. Immune-cell infiltration analysis showed higher estimated proportions of neutrophils and monocytes and lower proportions of selected T- and NK-cell subsets in SI-ALI. Single-cell analysis revealed cell-type-specific expression patterns of RPL22 and FTH1.
CONCLUSION: This study identifies RPL22, MMP8, and FTH1 as candidate hub genes associated with both cuproptosis- and mitophagy-related molecular signatures in SI-ALI. The three-gene model showed promising discriminatory performance in the discovery cohort, while the identified genes represent candidate molecular features associated with SI-ALI that require further validation.
METHODS: Transcriptomic datasets from the Gene Expression Omnibus were analyzed, with GSE32707 serving as the discovery dataset and GSE10361 as an independent validation dataset. Differentially expressed genes (DEGs) were intersected with cuproptosis- and mitophagy-related gene sets to identify candidate genes. Biological functions were explored using Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analyses and a protein-protein interaction network. Least absolute shrinkage and selection operator regression and random forest were used to identify hub genes. A three-gene logistic regression model and nomogram were constructed and internally evaluated in the GSE32707 discovery cohort using receiver operating characteristic analysis, bootstrap calibration, and decision curve analysis. The individual hub genes were further evaluated in GSE10361. Immune infiltration was assessed using CIBERSORT, and single-cell RNA-sequencing data from GSE151263 were used to characterize cell-type-specific expression of the hub genes.
RESULTS: From 296 DEGs, 24 cuproptosis- and mitophagy-related candidate genes were identified. Machine learning identified RPL22, FTH1, and MMP8 as hub genes. MMP8 was upregulated, whereas RPL22 and FTH1 were downregulated. In the discovery cohort, the individual areas under the curve (AUCs) of RPL22, FTH1, and MMP8 were 0.826, 0.762, and 0.720, respectively, whereas the combined three-gene model achieved an AUC of 0.911. The individual hub genes showed preliminary discriminatory performance in the independent validation cohort. Immune-cell infiltration analysis showed higher estimated proportions of neutrophils and monocytes and lower proportions of selected T- and NK-cell subsets in SI-ALI. Single-cell analysis revealed cell-type-specific expression patterns of RPL22 and FTH1.
CONCLUSION: This study identifies RPL22, MMP8, and FTH1 as candidate hub genes associated with both cuproptosis- and mitophagy-related molecular signatures in SI-ALI. The three-gene model showed promising discriminatory performance in the discovery cohort, while the identified genes represent candidate molecular features associated with SI-ALI that require further validation.