Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis
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Le résumé fourni par la source
Small Ubiquitin-like MOdifier-mediated modification (SUMOylation) is associated with sepsis; however, its molecular mechanism remains unclear. Herein, hub genes and regulatory mechanisms in sepsis was investigated. The GSE65682 and GSE95233 datasets were extracted from public databases. Differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA) were conducted in GSE65682 to identify differentially expressed genes (DEGs) and key module genes. Candidate genes were derived by intersecting with SUMOylation-related genes (SUMO-RGs). The Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were utilized to identify significant feature genes. The convergence of those genes was utilized for diagnostic assessment and expression validation. Hub genes were defined as those exhibiting an area under the curve (AUC) greater than 0.7, significant gene expression, and a consistent trend. Localization and functional analyses of hub genes were conducted to enhance the understanding of these genes. Immune analysis, regulatory network construction, and drug prediction were performed. Six hub genes were identified: RORA, L3MBTL2, PHC1, RPA1, CHD3, and RANGAP1. These genes possessed considerable diagnostic significance for sepsis and were also markedly downregulated in the condition. Hub genes were predominantly enriched in the ribosome pathway and exhibited a strong correlation with differential immune cells. Activated CD8 + T cells exhibited a positive correlation with RORA. Based on the predicted and established regulatory network, AC004687.1 was observed to modulate PHC1 expression via hsa-miR- 142 - 5p. A total of six hub genes (RORA, L3MBTL2, PHC1, RPA1, CHD3, and RANGAP1) associated with SUMOylation was identified in sepsis in the current study. The findings are likely to aid in the differentiation between control and disease states, offering substantiation for the diagnosis of sepsis.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Integrating bioinformatics and machine learning to discover sumoylation associated signatures in sepsis
- Date Crossref
- 24/04/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Heilongjiang Provincial Hospital Department of Anesthesiology pays non établi dans la noticeÉtablissement de santé
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Education Department of Heilongjiang Province pays non établi dans la noticeOrganisme public
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Harbin Medical University Department of Colorectal Surgery pays non établi dans la noticeUniversité ou école supérieure
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Third Affiliated Hospital of Harbin Medical University pays non établi dans la noticeÉtablissement de santé
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Second Affiliated Hospital of Harbin Medical University pays non établi dans la noticeÉtablissement de santé
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Heilongjiang University pays non établi dans la noticeUniversité ou école supérieure
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The Key Laboratory of Anesthesiology and Intensive Care Research of Heilongjiang Province pays non établi dans la noticeStructure de recherche
Department of Anesthesiology — Heilongjiang Provincial Hospital, Education Department of Heilongjiang Province et Department of Colorectal Surgery — Harbin Medical University, avec 4 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.