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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
Published on: November 11, 2014
Identification and validation of key biomarkers based on RNA methylation genes in sepsis
Qianqian Zhang1,2, Xiaowei Bao1,2, Mintian Cui3
1Department of Internal Emergency Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
Background:
RNA methylation is closely involved in immune regulation, but its role in sepsis remains unknown. Here, we aim to investigate the role of RNA methylation-associated genes (RMGs) in classifying and diagnosing of sepsis.
Methods:
Five types of RMGs (m1A, m5C, m6Am, m7G and Ψ) were used to identify sepsis subgroups based on gene expression profile data obtained from the GEO database (GSE57065, GSE65682, and GSE95233). Unsupervised clustering analysis was used to identify distinct RNA modification subtypes. The CIBERSORT, WGCNA, GO and KEGG analysis were performed to explore immune infiltration pattern and biological function of each cluster. RF, SVM, XGB, and GLM algorithm were applied to identify the diagnostic RMGs in sepsis. Finally, the expression levels of the five key RMGs were verified by collecting PBMCs from septic patients using qRT-PCR, and their diagnostic efficacy for sepsis was verified in combination with clinical data using ROC analysis.
Results:
Sepsis was divided into three subtypes (cluster 1 to 3). Cluster 1 highly expressed NSUN7 and TRMT6, with the characteristic of neutrophil activation and upregulation of MAPK signaling pathways. Cluster 2 highly expressed NSUN3, and was featured by the regulation of mRNA stability and amino acid metabolism. NSUN5 and NSUN6 were upregulated in cluster 3 which was involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism pathways. In addition, we identified that five RMGs (NSUN7, NOP2, PUS1, PUS3 and FTO) could function as biomarkers for clinic diagnose of sepsis. For validation, we determined that the relative expressions of NSUN7, NOP2, PUS1 and PUS3 were upregulated, while FTO was downregulated in septic patients. The area under the ROC curve (AUC) of NSUN7, NOP2, PUS1, PUS3 and FTO was 0.828, 0.707, 0.846, 0.834 and 0.976, respectively.
Conclusions:
Our study uncovered that dysregulation of RNA methylation genes (m1A, m5C, m6Am, m7G and Ψ) was closely involved in the pathogenesis of sepsis, providing new insights into the classification of sepsis endotypes. We also revealed that five hub RMGs could function as novel diagnostic biomarkers and potential targets for treatment.
Insights
This study reveals RNA methylation genes are involved in sepsis pathogenesis, classifying sepsis into subtypes and identifying five key genes as potential diagnostic biomarkers for sepsis. These findings offer new insights into sepsis endotypes and potential therapeutic targets.
Area of Science:
- Molecular biology
- Immunology
- Genetics
Background:
- RNA methylation plays a role in immune regulation, but its function in sepsis is not well understood.
- Investigating RNA methylation-associated genes (RMGs) offers a potential avenue for sepsis classification and diagnosis.
Purpose of the Study:
- To explore the role of RNA methylation-associated genes (RMGs) in classifying sepsis subtypes.
- To identify novel diagnostic biomarkers for sepsis based on RMGs.
Main Methods:
- Utilized gene expression data from the GEO database (GSE57065, GSE65682, GSE95233) for five RMG types (m1A, m5C, m6Am, m7G, Ψ).
- Employed unsupervised clustering, CIBERSORT, WGCNA, GO, and KEGG analyses to identify sepsis subtypes, immune infiltration, and biological functions.
- Applied machine learning algorithms (RF, SVM, XGB, GLM) to identify diagnostic RMGs and validated their expression and diagnostic efficacy in septic patients' PBMCs using qRT-PCR and ROC analysis.
Main Results:
- Sepsis was classified into three distinct subtypes based on RMG expression profiles.
- Cluster 1 showed high expression of NSUN7 and TRMT6, linked to neutrophil activation and MAPK signaling.
- Cluster 2 highlighted NSUN3, associated with mRNA stability and amino acid metabolism.
- Cluster 3 exhibited elevated NSUN5 and NSUN6, involved in ribonucleoprotein complex biogenesis and carbohydrate metabolism.
- Five RMGs (NSUN7, NOP2, PUS1, PUS3, FTO) were identified as potential diagnostic biomarkers for sepsis, with high AUC values (0.707–0.976).
- Validation confirmed upregulation of NSUN7, NOP2, PUS1, PUS3 and downregulation of FTO in septic patients.
Conclusions:
- Dysregulation of RNA methylation genes is implicated in sepsis pathogenesis, aiding in the classification of sepsis endotypes.
- Five hub RMGs (NSUN7, NOP2, PUS1, PUS3, FTO) demonstrate potential as novel diagnostic biomarkers and therapeutic targets for sepsis.
