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BMC Infectious Diseases
Published in: BMC Infectious Diseases 1/2022

Open Access 01-12-2022 | Septicemia | Research

Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis

Authors: Yonghua Fan, Qiufeng Han, Jinfeng Li, Gaige Ye, Xianjing Zhang, Tengxiao Xu, Huaqing Li

Published in: BMC Infectious Diseases | Issue 1/2022

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Abstract

Background

Sepsis is an inflammatory response caused by infection with pathogenic microorganisms. The body shock caused by it is called septic shock. In view of this, we aimed to identify potential diagnostic gene biomarkers of the disease.

Material and methods

Firstly, mRNAs expression data sets of septic shock were retrieved and downloaded from the GEO (Gene Expression Omnibus) database for differential expression analysis. Functional enrichment analysis was then used to identify the biological function of DEmRNAs (differentially expressed mRNAs). Machine learning analysis was used to determine the diagnostic gene biomarkers for septic shock. Thirdly, RT-PCR (real-time polymerase chain reaction) verification was performed. Lastly, GSE65682 data set was utilized to further perform diagnostic and prognostic analysis of identified superlative diagnostic gene biomarkers.

Results

A total of 843 DEmRNAs, including 458 up-regulated and 385 down-regulated DEmRNAs were obtained in septic shock. 15 superlative diagnostic gene biomarkers (such as RAB13, KIF1B, CLEC5A, FCER1A, CACNA2D3, DUSP3, HMGN3, MGST1 and ARHGEF18) for septic shock were identified by machine learning analysis. RF (random forests), SVM (support vector machine) and DT (decision tree) models were used to construct classification models. The accuracy of the DT, SVM and RF models were very high. Interestingly, the RF model had the highest accuracy. It is worth mentioning that ARHGEF18 and FCER1A were related to survival. CACNA2D3 and DUSP3 participated in MAPK signaling pathway to regulate septic shock.

Conclusion

Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.
Appendix
Available only for authorised users
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Metadata
Title
Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis
Authors
Yonghua Fan
Qiufeng Han
Jinfeng Li
Gaige Ye
Xianjing Zhang
Tengxiao Xu
Huaqing Li
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Infectious Diseases / Issue 1/2022
Electronic ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-022-07056-4

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