Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning

Li Ke; Yasu Lu; Han Gao; Chang Hu; Jiahao Zhang; Qiuyue Zhao; Zhongyi Sun; Zhiyong Peng

doi:10.1016/j.csbj.2023.03.034

Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning

Comput Struct Biotechnol J. 2023 Mar 22:21:2316-2331. doi: 10.1016/j.csbj.2023.03.034. eCollection 2023.

Authors

Li Ke^{1

2}, Yasu Lu^{1

2}, Han Gao³, Chang Hu^{1

2}, Jiahao Zhang^{1

2}, Qiuyue Zhao^{1

2}, Zhongyi Sun^{1

2}, Zhiyong Peng^{1

2}

Affiliations

¹ Department of Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China.
² Clinical Research Center of Hubei Critical Care Medicine, Wuhan, Hubei, China.
³ Department of Respiratory and Critical Care Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province 430071, China.

Abstract

Background: To identify potential diagnostic and prognostic biomarkers of the early stage of sepsis.

Methods: The differentially expressed genes (DEGs) between sepsis and control transcriptomes were screened from GSE65682 and GSE134347 datasets. The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The diagnostic and prognostic abilities of the markers were evaluated by plotting receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to further elucidate the molecular mechanisms and immune-related processes. Finally, the potential biomarkers were validated in a septic mouse model by qRT-PCR and western blotting.

Results: Eleven DEGs were identified between the sepsis and control samples, including YOD1, GADD45A, BCL11B, IL1R2, UGCG, TLR5, S100A12, ITK, HP, CCR7 and C19orf59 (all AUC>0.9). Furthermore, the survival analysis identified YOD1, GADD45A, BCL11B and IL1R2 as the prognostic biomarkers of sepsis. According to GSEA, four DEGs were significantly associated with immune-related processes. In addition, ssGSEA demonstrated a significant difference in the enriched immune cell populations between the sepsis and control groups (all P < 0.05). Moreover, YOD1, GADD45A and IL1R2 were upregulated, and BCL11B was downregulated in the heart, liver, lungs, and kidneys of the septic mice model.

Conclusions: We identified four potential immune-releated diagnostic and prognostic gene markers for sepsis that offer new insights into its underlying mechanisms.

Keywords: Biomarker; Diagnosis; Machine learning; Prognosis; Sepsis.