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.