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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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Material and methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Identified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.<\/jats:p><\/jats:sec>","DOI":"10.1186\/s12879-022-07056-4","type":"journal-article","created":{"date-parts":[[2022,1,19]],"date-time":"2022-01-19T16:03:25Z","timestamp":1642608205000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis"],"prefix":"10.1186","volume":"22","author":[{"given":"Yonghua","family":"Fan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiufeng","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinfeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaige","family":"Ye","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianjing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengxiao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9380-8441","authenticated-orcid":false,"given":"Huaqing","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,19]]},"reference":[{"issue":"36","key":"7056_CR1","doi-asserted-by":"publisher","first-page":"E1058","DOI":"10.1503\/cmaj.170149","volume":"190","author":"SM Fernando","year":"2018","unstructured":"Fernando SM, Rochwerg B, Seely AJE. 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