Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis

GuoHua You; XueGang Zhao; JianRong Liu; Kang Yao; XiaoMeng Yi; HaiTian Chen; XuXia Wei; YiNong Huang; XingYe Yang; YunGuo Lei; ZhiPeng Lin; YuFeng He; MingMing Fan; YuLing An; TongYu Lu; HaiJin Lv; Xin Sui; HuiMin Yi

doi:10.3389/fimmu.2023.1253833

Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis

Front Immunol. 2023 Oct 13:14:1253833. doi: 10.3389/fimmu.2023.1253833. eCollection 2023.

Authors

GuoHua You^{1

2

3}, XueGang Zhao^{1

2

3}, JianRong Liu^{1

2

3}, Kang Yao^{2

3}, XiaoMeng Yi¹, HaiTian Chen^{2

3}, XuXia Wei¹, YiNong Huang^{2

3}, XingYe Yang^{2

3}, YunGuo Lei^{2

3}, ZhiPeng Lin¹, YuFeng He¹, MingMing Fan¹, YuLing An¹, TongYu Lu^{2

3}, HaiJin Lv^{1

2

3}, Xin Sui^{1

2

3}, HuiMin Yi^{1

2

3}

Affiliations

¹ Department of Surgical Intensive Care Unit, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
² Guangdong Key Laboratory of Liver Disease Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
³ Key Laboratory of Liver Disease Biotherapy and Translational Medicine of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.

Abstract

Objective: Sepsis related injury has gradually become the main cause of death in non-cardiac patients in intensive care units, but the underlying pathological and physiological mechanisms remain unclear. The Third International Consensus Definitions for Sepsis and Septic Shock (SEPSIS-3) definition emphasized organ dysfunction caused by infection. Neutrophil extracellular traps (NETs) can cause inflammation and have key roles in sepsis organ failure; however, the role of NETs-related genes in sepsis is unknown. Here, we sought to identify key NETs-related genes associate with sepsis.

Methods: Datasets GSE65682 and GSE145227, including data from 770 patients with sepsis and 54 healthy controls, were downloaded from the GEO database and split into training and validation sets. Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. A machine learning approach was applied to identify key genes, which were used to construct functional networks. Key genes associated with diagnosis and survival of sepsis were screened out. Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN).

Results: Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms. Of these, CYBB and FCAR were independent predictors of poor survival in patients with sepsis. Administration of SFN significantly alleviated murine lung NETs expression and injury, accompanied by whole blood CYBB mRNA level.

Conclusion: CYBB and FCAR may be reliable biomarkers of survival in patients with sepsis, as well as potential targets for sepsis treatment. SFN significantly alleviated NETs-related organs injury, suggesting the therapeutic potential by targeting CYBB in the future.

Keywords: CYBB; FCAR; NETs; Sepsis; Sulforaphane.

Publication types

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

MeSH terms

Animals
Biomarkers
Extracellular Traps* / metabolism
Gene Expression Profiling
Humans
Mice
NADPH Oxidase 2 / genetics
Sepsis* / diagnosis
Sepsis* / drug therapy
Sepsis* / genetics
Shock, Septic* / genetics

Substances

Biomarkers
CYBB protein, human
NADPH Oxidase 2

Grants and funding

This work was supported by the grants from the National Natural Science Foundation of China (81901943,82270690,82200732, 82100693), Guangdong Natural Science Foundation (2022A1515011919, 2022A1515012519, 2022A1515012453). Guangdong Medical Foundation (A2021160),Medical Scientific Research Foundation of Guangdong Province (A2020120), Guangdong Basic and Applied Basic Research Foundation (2020A1515110687).