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Updated: May 7, 2025
A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune
1Department of Respiratory Medicine, The Second Hospital of Shanxi Medical University, 382 Wuyi Road, Xinghualing Area, 030000, Taiyuan, China.
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, EIF4G3, EIF4E3, NSUN2, NUDT4, and GEMIN5 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.
Insights
This study identifies five key N7-methylguanosine (m7G) genes that predict sepsis survival and potential therapeutic targets. The findings offer insights into RNA methylation
Area of Science:
- Molecular Biology
- Genomics
- Immunology
Background:
- N7-methylguanosine (m7G) RNA modification is implicated in various human diseases.
- The specific role of m7G modifications and their regulatory factors in sepsis pathogenesis remains largely unexplored.
- Understanding these mechanisms is crucial for developing novel diagnostic and therapeutic strategies for sepsis.
Purpose of the Study:
- To investigate the patterns of m7G regulatory factor-mediated RNA methylation in sepsis.
- To explore the association between m7G modifications and the immune microenvironment in sepsis.
- To identify lethality-related m7G signature genes and their potential as biomarkers for sepsis survival.
Main Methods:
- Utilized three sepsis patient datasets for training and validation.
- Employed machine learning to screen lethality-related m7G signature genes and construct a survival model.
- Analyzed immune cell distribution, differentially expressed genes (DEGs), and performed Weighted Gene Co-expression Network Analysis (WGCNA).
Main Results:
- Identified 10 differentially expressed m7G-related genes, pinpointing EIF4G3, EIF4E3, NSUN2, NUDT4, and GEMIN5 as optimal lethality-related genes.
- Developed a survival diagnostic model with an AUC of 0.678.
- Revealed significant differences in immune cell composition between survivors and non-survivors, classifying sepsis into two subtypes with distinct immune profiles and 1707 DEGs.
Conclusions:
- A diagnostic model based on five m7G signature genes may aid in predicting sepsis patient survival.
- The identified hub genes present potential therapeutic targets for sepsis intervention.
- The study highlights the intricate link between m7G modification, immune microenvironment, and sepsis outcomes.
