Identifying SUMOylation-related genes in liver fibrosis with bioinformatics and experimental models for diagnostic insights.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41233500", "kind": "science-record", "revision": 4, "content_hash": "sha256:f8b38f8f9531c2c2bfaaa03a05a107934d682951da433bb66adfe41e66fe99fa", "created_at": "2026-11-01T20:31:16Z", "updated_at": "2026-11-01T22:57:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying SUMOylation-related genes in liver fibrosis with bioinformatics and experimental models for diagnostic insights.", "doi": "10.1038/s41598-025-23516-8", "pmid": "41233500", "authors": ["Zhiwei Su", "Yuxue Ding", "Juan Xue", "Jun Sun", "Chunyan Ji"], "journal": "Scientific reports", "year": 2025, "abstract": "Liver fibrosis (LF) is a medical disorder caused by prolonged chronic liver injury, which, if left untreated, can progress to cirrhosis or liver cancer, posing significant risks to patient health. In recent years, the increase in liver diseases, including alcoholic liver disease, non-alcoholic fatty liver disease, and viral hepatitis, has significantly heightened the prevalence of LF. SUMOylation, an important post-translational modification, is essential for regulating cellular functions and may play a critical role in the progression of LF; however, its exact mechanisms remain poorly understood. This study conducted a thorough examination of the expression patterns of SUMOylation-related genes in patients with LF for the first time. We obtained two LF datasets (GSE130970 and GSE84044) from the GEO database, integrated the data for DEG analysis and functional enrichment analysis, and employed machine learning techniques to identify pivotal genes. Furthermore, we utilized ssGSEA and immune cell infiltration analysis to evaluate the roles of these genes within the immunological context of LF. To validate the bioinformatics findings, we established a CCl₄-induced C57BL/6 mouse model of LF to investigate the expression of relevant genes. A total of 1,583 differentially expressed genes were identified, 13 of which were associated with SUMOylation. These genes were primarily enriched in biological processes related to cell signal transduction, cell adhesion, and inflammatory responses. Utilizing machine learning approaches, we found eight crucial genes (NR3C2, PCNA, THRB, CDKN2A, DNMT1, MDM2, SMC6, and RXRA) that have significant diagnostic potential in the progression of LF. Additionally, we observed a significant increase in the infiltration of several immune cell types, with evident correlations between the expression of SUMOylation-related genes and specific immune cell types. The results of the animal experiments validated the bioinformatics analysis, as key SUMOylation-related genes exhibited expression patterns consistent with our expectations in the CCl₄-induced LF mouse model. This study elucidates the critical roles of SUMOylation-related genes in LF, highlighting their influence on liver damage and the progression of fibrosis through the regulation of cytokine synthesis, facilitation of hepatic stellate cell activation, and enhancement of immune cell infiltration. The identified significant genes exhibit potential as novel biomarkers for therapeutic applications. These findings clarify the pathogenic mechanisms of SUMOylation in LF and establish a foundation for the development of innovative therapeutic targets and diagnostic markers, thereby aiding in the prevention and treatment of LF.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) between the mild-fibrosis and advanced-fibrosis groups were subsequently identified using the limma package in R, based on criteria of an adjusted P-value less than 0.05 and absolute log-fold changes greater than 0.2 across the two datasets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41233500", "locator": "sections[3] \"Identification of differentially expressed genes between mild and advanced LF\" paragraph 4", "quote": "Differentially expressed genes (DEGs) between the mild-fibrosis and advanced-fibrosis groups were subsequently identified using the limma package in R, based on criteria of an adjusted P-value less than 0.05 and absolute log-fold changes greater than 0.2 across the two datasets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available on request from the corresponding author. 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