A host immune-related LncRNA and mRNA signature as a discriminant classifier for bacterial from non-bacterial sepsis in children

Chunxia Wang; Ting Sun; Yiping Zhou; Tiantian Liu; Shuyun Feng; Xi Xiong; Jiao Fan; Qiming Liang; Yun Cui; Yucai Zhang

doi:10.1016/j.heliyon.2024.e38728

A host immune-related LncRNA and mRNA signature as a discriminant classifier for bacterial from non-bacterial sepsis in children

Heliyon. 2024 Sep 30;10(21):e38728. doi: 10.1016/j.heliyon.2024.e38728. eCollection 2024 Nov 15.

Authors

Chunxia Wang^{1

2

3}, Ting Sun¹, Yiping Zhou^{1

2

3}, Tiantian Liu^{1

2}, Shuyun Feng^{1

2}, Xi Xiong^{1

2

3}, Jiao Fan⁴, Qiming Liang^{5

6}, Yun Cui^{1

2

3}, Yucai Zhang^{1

2

3}

Affiliations

¹ Department of Critical Care Medicine, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200062, China.
² Institute of Pediatric Infection, Immunity, and Critical Care Medicine, Shanghai Jiao Tong University School of Medicine, Shanghai, 200062, China.
³ Institute of Pediatric Critical Care, Shanghai Jiao Tong University, Shanghai, 200062, China.
⁴ Institute of Geriatrics, National Clinical Research Center of Geriatrics Disease, Second Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
⁵ Research Center of Translational Medicine, Shanghai Children's Hospital, Shanghai Jiao Tong University, Shanghai, 200062, China.
⁶ Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.

Abstract

Background: The variations in non-coding RNA alterations and the host immune response between patients with bacterial and non-bacterial sepsis, along with their clinical characteristics, are largely unknown.

Methods: The landscape of long non-coding RNA (lncRNA) and mRNA in whole blood cells from pediatric patients with bacterial sepsis or non-bacterial sepsis were characterized using an Arraystar human LncRNA microarray. Weighted correlation network analysis (WGCNA) were conducted to identify immune-related LncRNA-mRNA signatures. Least absolute shrinkage and selection operator (Lasso) regression and Ridge regression analysis were employed to develop a specific LncRNA-mRNA signature, serving as a discriminant classifier for bacterial and non-bacterial sepsis in children.

Results: A total of 33 differentially expressed lncRNAs and 52 mRNAs were identified in pediatric patients with either bacterial sepsis or non-bacterial sepsis. Among these, 69 lncRNAs and mRNAs were pinpointed using WGCNA and found to be significantly correlated with clinical parameters. Further intersection analysis identified 12 lncRNAs and 16 mRNAs as immune-related signature for discerning bacterial infections in children with sepsis. Additionaly, the lncRNA-mRNA co-expression network highlighted the key lncRNAs (AC090159.1 and AC080129.2) and mRNAs (S100A8 and TCF7L2) as an infection score model. Lasso regression analysis revealed that this infection score model achieved an area under the received operating curve (AUROC) of 0.96 in the training set and 0.86 in the validation set. Ultimately, the expression levels of these 4 key lncRNAs and mRNAs showed significant correlation with CRP or PCT levels.

Conclusion: The machine learning model, developed utilizing key lncRNAs (AC090159.1 and AC080129.2) and mRNAs (S100A8 and TCF7L2), demonstrates robust discrimination and calibration capabilities for distinguishing between bacterial and non-bacterial sepsis in children.

Keywords: Bacterial sepsis; Children; Discrimination; Immune-related lncRNA and mRNA signature; Machine learning.