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Identification of Potential Early Diagnostic Biomarkers of Sepsis

Authors Li Z, Huang B, Yi W, Wang F, Wei S, Yan H, Qin P, Zou D, Wei R, Chen N

Received 22 December 2020

Accepted for publication 22 February 2021

Published 3 March 2021 Volume 2021:14 Pages 621—631

DOI https://doi.org/10.2147/JIR.S298604

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Ning Quan


Zhenhua Li,1,2,* Bin Huang,2,* Wenfeng Yi,2,* Fei Wang,1 Shizhuang Wei,1 Huaixing Yan,1 Pan Qin,1 Donghua Zou,1 Rongguo Wei,3 Nian Chen4

1Department of Emergency Medicine, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China; 2Intensive Care Unit, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China; 3Department of Clinical Laboratory, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China; 4Department of Infectious Diseases, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, 530022, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Rongguo Wei; Nian Chen Email [email protected]; [email protected]

Objective: The goal of this article was to identify potential biomarkers for early diagnosis of sepsis in order to improve their survival.
Methods: We analyzed differential gene expression between adult sepsis patients and controls in the GSE54514 dataset. Coexpression analysis was used to cluster coexpression modules, and enrichment analysis was performed on module genes. We also analyzed differential gene expression between neonatal sepsis patients and controls in the GSE25504 dataset, and we identified the subset of differentially expressed genes (DEGs) common to neonates and adults. All samples in the GSE54514 dataset were randomly divided into training and validation sets, and diagnostic signatures were constructed using least absolute shrink and selection operator (LASSO) regression. The key gene signature was screened for diagnostic value based on area under the receiver operating characteristic curve (AUC). STEM software identified dysregulated genes associated with sepsis-associated mortality. The ssGSEA method was used to quantify differences in immune cell infiltration between sepsis and control samples.
Results: A total of 6316 DEGs in GSE54514 were obtained spanning 10 modules. Module genes were mainly enriched in immune and metabolic responses. Screening 51 genes from among common genes based on AUC > 0.7 led to a LASSO model for the training set. We obtained a 25-gene signature, which we validated in the validation set and in the GSE25504 dataset. Among the signature genes, SLC2A6, C1ORF55, DUSP5 and RHOB were recognized as key genes (AUC > 0.75) in both the GSE54514 and GSE25504 datasets. SLC2A6 was identified by STEM as associated with sepsis-associated mortality and showed the strongest positive correlation with infiltration levels of Th1 cells.
Conclusion: In summary, our four key genes may have important implications for the early diagnosis of sepsis patients. In particular, SLC2A6 may be a critical biomarker for predicting survival in sepsis.

Keywords: sepsis, early diagnosis, LASSO model, SLC2A6, WGCNA, diagnostic biomarker

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