Identification and validation of key biomarkers based on RNA methylation genes in sepsis

Qianqian Zhang; Xiaowei Bao; Mintian Cui; Chunxue Wang; Jinlu Ji; Jiongjie Jing; Xiaohui Zhou; Kun Chen; Lunxian Tang

doi:10.3389/fimmu.2023.1231898

Identification and validation of key biomarkers based on RNA methylation genes in sepsis

Front Immunol. 2023 Aug 28:14:1231898. doi: 10.3389/fimmu.2023.1231898. eCollection 2023.

Authors

Qianqian Zhang^{1

2}, Xiaowei Bao^{1

2}, Mintian Cui³, Chunxue Wang^{1

2}, Jinlu Ji^{1

2}, Jiongjie Jing³, Xiaohui Zhou⁴, Kun Chen^{3

5}, Lunxian Tang^{1

2}

Affiliations

¹ Department of Internal Emergency Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
² School of Medicine, Tongji University, Shanghai, China.
³ Translational Medical Center for Stem Cell Therapy, Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
⁴ Research Center for Translational Medicine, Shanghai Heart Failure Research Center, Shanghai East Hospital, Tongji University, Shanghai, China.
⁵ Shanghai Key Laboratory of Signaling and Disease Research, Frontier Science Center for Stem Cell Research, School of Life Sciences and Technology, Tongji University, Shanghai, China.

Abstract

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.

Keywords: RNA methylation; biomarkers; machine learning; sepsis; unsupervised clustering.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Alpha-Ketoglutarate-Dependent Dioxygenase FTO
Biomarkers
Humans
Methylation
RNA
Sepsis* / diagnosis
Sepsis* / genetics
tRNA Methyltransferases

Substances

Biomarkers
RNA
FTO protein, human
Alpha-Ketoglutarate-Dependent Dioxygenase FTO
NSUN6 protein, human
tRNA Methyltransferases

Grants and funding

This work was supported by grants from the National Natural Science Foundation of China (81970072 to LT), the leading medical talent project of Shanghai Pudong heath bureau (PWRI2019‐05 to LT), the action plan for scientific and technological innovation of Shanghai Scientific Committee of China (20Y11901200 to LT), the municipal Natural Science Foundation of Shanghai Scientific Committee of China (22ZR1451000 to LT), the key clinical discipline of Shanghai Pudong heath bureau (PWZxk2022-17 to LT), the clinical peak discipline of Shanghai Pudong heath bureau (PWYgf2021-03), Shanghai Rising-Star Program (21QA1407600 to KC), Fundamental Research Funds for the Central Universities (22120220082, 22120230020 to KC).