Molecular biology reports · 2026
Machine learning methods identified cellular senescence-related hub molecules in sepsis-induced acute respiratory distress syndrome (ARDS) and their upstream regulatory network.
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Bao Peng, Sun Chenliang, Lu Yang, Wang Yiping
Аннотация
BACKGROUND: Sepsis-induced ARDS demonstrated greater severity and higher mortality compared to ARDS triggered by other factors. In this article, we comprehensively explored the roles of cellular senescence (CS)-related genes in sepsis-induced ARDS, highlighting their hub molecules and upstream regulatory network via machine learning (ML) methods.
METHODS: RNA-sequencing data of sepsis-induced ARDS and CS-related genes were obtained from online accessible databases. A total of three machine learning methods were applied to identify CS-related hub molecules in sepsis-induced ARDS. The RegNetwork database was used to explore the upstream regulatory network of CS-related hub molecules. Single-cell RNA-sequencing (scRNA-seq) data was also used to verify the expressions of these CS-related hub molecules. We further constructed a sepsis-induced ALI/ARDS mouse model and validated these hub molecules by qRT-PCR.
RESULTS: A total of 40 CS-related differentially expressed genes (DEGs) were identified in sepsis-induced ARDS. Based on these, consensus clustering identified two potential molecular subtypes (Cluster A and B), and a total of six intersected genes (UBE2C, RPS6KA2, CCNA2, EZH2, CALM1, and E2F2) were finally regarded as hub molecules in sepsis-induced ARDS via three machine learning methods. These CS-related hub molecules were verified for their expressions at scRNA-seq levels. Correlations between these six hub molecules and 23 immune infiltration cell types were further revealed, and we also established the mRNA regulatory network of the miRNA/TF-six CS-related hub molecules. Besides, qRT-PCR results showed that the E2f2, Ezh2, and Ube2c genes had a higher expression in the sepsis-induced ALI/ARDS subgroup than in the control subgroup, while others did not (P-value < 0.05).
CONCLUSIONS: Our study highlighted the CS-related E2F2, EZH2, and UBE2C genes as hub molecules in sepsis-induced ARDS and their upstream regulatory network by means of machine learning methods.
METHODS: RNA-sequencing data of sepsis-induced ARDS and CS-related genes were obtained from online accessible databases. A total of three machine learning methods were applied to identify CS-related hub molecules in sepsis-induced ARDS. The RegNetwork database was used to explore the upstream regulatory network of CS-related hub molecules. Single-cell RNA-sequencing (scRNA-seq) data was also used to verify the expressions of these CS-related hub molecules. We further constructed a sepsis-induced ALI/ARDS mouse model and validated these hub molecules by qRT-PCR.
RESULTS: A total of 40 CS-related differentially expressed genes (DEGs) were identified in sepsis-induced ARDS. Based on these, consensus clustering identified two potential molecular subtypes (Cluster A and B), and a total of six intersected genes (UBE2C, RPS6KA2, CCNA2, EZH2, CALM1, and E2F2) were finally regarded as hub molecules in sepsis-induced ARDS via three machine learning methods. These CS-related hub molecules were verified for their expressions at scRNA-seq levels. Correlations between these six hub molecules and 23 immune infiltration cell types were further revealed, and we also established the mRNA regulatory network of the miRNA/TF-six CS-related hub molecules. Besides, qRT-PCR results showed that the E2f2, Ezh2, and Ube2c genes had a higher expression in the sepsis-induced ALI/ARDS subgroup than in the control subgroup, while others did not (P-value < 0.05).
CONCLUSIONS: Our study highlighted the CS-related E2F2, EZH2, and UBE2C genes as hub molecules in sepsis-induced ARDS and their upstream regulatory network by means of machine learning methods.