Comprehensive analysis of cuproptosis-related genes involved in immune infiltration and their use in the diagnosis of hepatic ischemia-reperfusion injury: an experimental study.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38935114", "kind": "science-record", "revision": 3, "content_hash": "sha256:050d4dc5c24aaecc91fa2b3dcd80ef8e893b7e5ffd57fdc1ecfd343ce7affd51", "created_at": "2026-11-01T20:29:00Z", "updated_at": "2026-11-01T22:12:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of cuproptosis-related genes involved in immune infiltration and their use in the diagnosis of hepatic ischemia-reperfusion injury: an experimental study.", "doi": "10.1097/JS9.0000000000001893", "pmid": "38935114", "authors": ["Xiaopeng Cai", "Jingwen Deng", "Xiaohu Zhou", "Kaiyue Wang", "Huiqiang Cai", "Yingcai Yan", "Jun Jiang", "Jia Yang", "Jin Gu", "Yuan Zhang", "Yuan Ding", "Qiang Sun", "Weilin Wang"], "journal": "International journal of surgery (London, England)", "year": 2025, "abstract": "BACKGROUND: Hepatic ischemia-reperfusion injury (HIRI) is a common injury not only during liver transplantation but also during major hepatic surgery. HIRI causes severe complications and affects the prognosis and survival of patients. Cuproptosis, a newly identified form of cell death, plays an important role in a variety of illnesses. However, its role in HIRI remains unknown.\nMATERIALS AND METHODS: The GSE151648 dataset was mined from the Gene Expression Omnibus (GEO) database, and differences were analyzed for intersections. Based on the differentially expressed genes (DEGs), functional annotation, differentially expressed cuproptosis-related genes (DE-CRGs) identification and lasso logistic regression were conducted. Correlation analysis of DE-CRGs and immune infiltration was further conducted, and DE-CRGs were applied to construct an HIRI diagnostic model. The hierarchical clustering method was used to classify the specimens of HIRI, and functional annotation was conducted to verify the accuracy of these DE-CRGs in predicting HIRI progression. The GSE14951 microarray dataset and GSE171539 single-cell sequencing dataset were chosen as validation datasets. At the same time, the significance of DE-CRGs was verified using a mouse model of HIRI with cuproptosis inhibitors and inducers. Finally, a network of transcription-factor-DE-CRGs and miRNA-DE-CRGs was constructed to reveal the regulation mechanisms. And potential drugs for DE-CRGs were predicted using Drug-Gene Interaction Database (DGIdb).\nRESULTS: Overall, 2390 DEGs and 19 DE-CRGs were identified. Through machine learning algorithms, 8 featured DE-CRGs (GNL3, ALAS1, TSC22D2, KLF5, GTF2B, DNTTIP2, SLFN11 and HNRNPU) were screened, and 2 cuproptosis-related subclusters were defined. Based on the 8 DE-CRGs obtained from the HIRI model [area under the curve (AUC)=0.97], the nomogram model demonstrated accuracy in predicting HIRI. Eight DE-CRGs were highly expressed in HIRI samples and were negatively related to immune cell infiltration. A higher level of immune infiltration and expression of CRG group B was found in the HIRI population. Differences in cell death and immune regulation were found between the 2 groups. The diagnostic value of the 8 DE-CRGs was confirmed in the validation of two datasets. The identification of 7 DE-CRGs (SLFN11 excluded) by HIRI animal model experiments was also confirmed. Using hTFtarget, miRWalk and DGIDB database, we predicted that 17 transcription factors, 192 miRNAs and 10 drugs might interact with the DE-CRGs.\nCONCLUSION: This study shows that cuproptosis may occur in HIRI and is correlated with immune infiltration. Additionally, a cuproptosis-related predictive model was constructed for studying the causes of HIRI and developing targeted treatment options for HIRI.", "claims": [{"id": "claim-method", "statement": "Then, featured DE-CRGs were screened from the DE-CRGs by least absolute shrinkage and selection operation (LASSO) analysis using the R package “glmnet”19.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38935114", "locator": "sections[5] \"Identification and validation of diagnostic genes for DE-CRGs\" paragraph 2", "quote": "Then, featured DE-CRGs were screened from the DE-CRGs by least absolute shrinkage and selection operation (LASSO) analysis using the R package “glmnet”19."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38935114", "locator": "sections[32] \"Availability of data and materials\" paragraph 2", "quote": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using GSE151648 and LASSO logistic regression, the study identified eight DE-CRGs and developed a model with a ROC-AUC of 0.970 for distinguishing HIRI from non-HIRI samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38935114", "locator": "sections[3] \"Data acquisition\" paragraph 2", "quote": "Gene expression data of HIRI and non-HIRI samples was mined from the Gene Expression Omnibus database (GEO) (https://www.ncbi.nlm.nih.gov/geo/): the RNA-seq data of GSE151648 (23 preischemic samples and 23 postischemic samples), microarray data of GSE14951 (5 preischemic samples and 5 postischemic samples) and single-cell sequencing data of GSE171539 (1 preprocurement sample and 1 post-reperfusion sample)."}, {"asset": "source:pmid:38935114", "locator": "sections[5] \"Identification and validation of diagnostic genes for DE-CRGs\" paragraph 2", "quote": "Then, featured DE-CRGs were screened from the DE-CRGs by least absolute shrinkage and selection operation (LASSO) analysis using the R package “glmnet”19."}, {"asset": "source:pmid:38935114", "locator": "sections[16] \"Identification of featured DE-CRGs via machine learning\" paragraph 2", "quote": "Similarly, the ROC-AUC of the risk score was 0.970, which is indicative of excellent model discrimination (Fig. 2J)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using GSE151648 and LASSO logistic regression, the study identified eight DE-CRGs and developed a model with a ROC-AUC of 0.970 for distinguishing HIRI from non-HIRI samples.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38935114"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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