Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis.

OBJECTIVES

N7-methylguanosine (m7G) modification is closely related to the occurrence of human diseases, but its roles in sepsis remain unclear. This study aimed to explore the patterns of lethality-related m7G regulatory factor-mediated RNA methylation modification and immune microenvironment regulatory features in sepsis.

METHODS

Three sepsis-related datasets (E-MTAB-4421 and E-MTAB-4451 as training sets and GSE185263 as a validation set) were collected, and differentially expressed m7G-related genes were analyzed between survivors and non-survivors. Lethality-related m7G signature genes were then screened using machine learning methods, followed by the construction of a survival recognition model. Additionally, differences in immune cell distribution were determined and differentially expressed genes (DEGs) between different subtypes were analyzed. Weighted gene co-expression network analysis (WGCNA) was used to select important modules and related hub genes.

RESULTS

In total, 10 differentially expressed m7G-related genes were identified between the survivors and non-survivors, and after further analysis, , , , , and were identified as the optimal lethality-related m7G genes. A survival status diagnostic model was then constructed with a combined AUC of 0.678. Fifteen types of immune cells were significantly different between survivors and non-survivors. Sepsis samples were classified into two subtypes, with 22 types of immune cells showing significant differences. Subsequently, 1707 DEGs were identified between the two subtypes, which were significantly enriched in 91 GO terms and 16 KEGG pathways. Finally, the green module with |correlation| > 0.3 was found to be closely related to the subtypes and survival status; further, the top10 hub genes were obtained.

CONCLUSION

The constructed survival status diagnostic model based on the five lethality-related m7G signature genes may help predict the survival status of patients, and the 10 hub genes obtained may be potential therapeutic targets for sepsis.