ArticleBMC infectious diseases2022
Revealing potential diagnostic gene biomarkers of septic shock based on machine learning analysis.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 29 citations in OpenAlex.
- Unveiling preeclampsia: diagnostic value and potential molecular mechanisms of abnormally methylated immune-related genes.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologicas · 2026Article
- Research Progress on Sepsis Diagnosis and Monitoring Based on Omics Technologies: A Review.Diagnostics (Basel, Switzerland) · 2025Review
- An ultra-rare missense variant in the KIF1B gene linked to autoinflammatory Menière's disease.NPJ genomic medicine · 2025Article
- Construction of Diagnostic Model for Regulatory T Cell-Related Genes in Sepsis Based on Machine Learning.Biomedicines · 2025Article
- Exploring the Role of Circadian Rhythm-Related Genes in the Identification of Sepsis Subtypes and the Construction of Diagnostic Models Based on RNA-seq and scRNA-seq.International journal of molecular sciences · 2025Article
- Overexpression of miR-20a targeting DUSP3 inhibits OCLN ubiquitination levels and alleviates sepsis induced intestinal barrier dysfunction.In vitro cellular & developmental biology. Animal · 2025Article
- Identification of sepsis biomarkers through glutamine metabolism-mediated immune regulation: a comprehensive analysis employing mendelian randomization, multi-omics integration, and machine learning.Frontiers in immunology · 2025Article
- Identification of diagnostic candidate genes in COVID-19 patients with sepsis.Immunity, inflammation and disease · 2024Article
- RAB13 regulates macrophage polarization in sepsis.Scientific reports · 2024Article
- Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms.Scientific reports · 2024Article
- Genetic polymorphisms, biomarkers and signaling pathways associated with septic shock: from diagnosis to therapeutic targets.Burns & trauma · 2024Review
- Role of lipid metabolism gene KLF4 in osteoarthritis.Clinical rheumatology · 2024Article
- Comprehensive Analysis of Immune Cell Infiltration and M2-Like Macrophage Biomarker Expression Patterns in Atrial Fibrillation.International journal of general medicine · 2024Article
- Article
- The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review.Translational pediatrics · 2023Article
- Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine.International journal of molecular sciences · 2023Article
- Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis.Frontiers in immunology · 2023Article
- Identification of Potential Biomarkers of Septic Shock Based on Pathway and Transcriptome Analyses of Immune-Related Genes.Genetics research · 2023Article
- Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis.Frontiers in medicine · 2023Article
- High Mobility Group Proteins in Sepsis.Frontiers in immunology · 2022Review
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSepsis 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
methodsFirstly, 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.
resultsA 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.
conclusionIdentified diagnostic gene biomarkers may be helpful in the diagnosis and therapy of patients with septic shock.
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