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The impact of SUMOylation, a post\u2010translational modification, on sepsis is largely unexplored. We integrated three GEO datasets to construct a large\u2010scale sepsis cohort and applied three machine learning algorithms to screen hub genes from differentially expressed genes (DEGs) associated with SUMOylation in sepsis. Unsupervised consensus clustering was performed to identify sepsis subtypes. Using single\u2010sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA), we analysed the immunological and functional features of these subtypes. We assembled the regulatory network of hub genes and performed drug prediction analysis. The expression of hub genes was confirmed in a murine caecal ligation and puncture (CLP) sepsis model through qRT\u2010PCR. Bioinformatics analysis identified a total of 43 SUMOylation\u2010associated DEGs. The machine learning pipeline further pinpointed eight hub genes: TOP2B, HDAC4, NUP43, HNRNPK, BCL11A, RPA1, RORA and XRCC4. Each gene exhibited high diagnostic potential. Based on this eight\u2010gene signature, sepsis patients were stratified into two subtypes. Subtype A, known as immune suppressive, was characterised by high infiltration of regulatory T cells, along with suppressed activity in immune pathways. The hyper\u2010inflammatory subtype B displayed large infiltration of effector lymphocytes and extensive activation of inflammatory pathways. Drug prediction analysis revealed possible therapeutic compounds, particularly the epigenetic modulator vorinostat. Experimental validation ultimately confirmed the dysregulation of these hub genes. In conclusion, our study discovered a novel eight\u2010gene signature associated with SUMOylation that supports new diagnostic strategies, and uncovers sepsis heterogeneity. The identification of two sepsis subtypes with different immunological and functional characteristics emphasises the role of SUMOylation in sepsis pathophysiology and provides a new strategy for advancing precision diagnostics and personalised therapy.<\/jats:p>","DOI":"10.1049\/syb2.70053","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T22:31:14Z","timestamp":1769812274000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning\u2010Based Integrative Analysis Identifies SUMOylation\u2010Related Genes Underlying the Immune Heterogeneity of Sepsis"],"prefix":"10.1049","volume":"20","author":[{"given":"Zeqian","family":"Li","sequence":"first","affiliation":[{"name":"Department of Hepatobiliary Surgery Beijing Chao\u2010Yang Hospital Capital Medical University  Beijing China"},{"name":"Beijing Institute of Heart Lung and Blood Vessel Diseases Beijing Anzhen Hospital 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