Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38495196", "kind": "science-record", "revision": 4, "content_hash": "sha256:9dcce939f66112190ea54c146c05df40c16c4997b3e46e0e7d1a8d3246e11dce", "created_at": "2026-11-01T19:20:26Z", "updated_at": "2026-11-01T22:53:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis.", "doi": "10.1016/j.heliyon.2024.e27379", "pmid": "38495196", "authors": ["Tianfeng Wang", "Xiaowei Fang", "Ximei Sheng", "Meng Li", "Yulin Mei", "Qing Mei", "Aijun Pan"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Cuproptosis is a copper-dependent cell death that is connected to the development and immune response of multiple diseases. However, the function of cuproptosis in the immune characteristics of sepsis remains unclear.\nMethod: We obtained two sepsis datasets (GSE9960 and GSE134347) from the GEO database and classified the raw data with R packages. Cuproptosis-related genes were manually curated, and differentially expressed cuproptosis-related genes (DECuGs) were identified. Afterwards, we applied enrichment analysis and identified key DECuGs by performing machine learning techniques. Then, the immune cell infiltrations and correlation between DECuGs and immunocyte features were created by the CIBERSORT algorithm. Subsequently, unsupervised hierarchical clustering analysis was performed based on key DECuGs. We then constructed a ceRNA network based on key DECuGs by using multi-step computational strategies and predicted potential drugs in the DrugBank database. Finally, the role of these key genes in immune cells was validated at the single-cell RNA level between septic patients and healthy controls.\nResults: Overall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis samples. Afterwards, we obtained six key DECuGs by performing machine learning. Then, the LIPT1-T-cell CD4 memory resting was the most positively correlated DECuG-immunocyte pair. Subsequently, two different subclusters were identified by six DECuGs. Bioinformatics analysis revealed that there were different immune characteristics between the two subclusters. Moreover, we identified the key lncRNA OIP5-AS1 within the ceRNA network and obtained 4 drugs that may represent novel drugs for sepsis. Finally, these key DECuGs were statistically significantly dysregulated in another validation set and showed a major distribution in monocytes, T cells, B cells, NK cells and platelets at the single-cell RNA level.\nConclusion: These findings suggest that cuproptosis might promote the progression of sepsis by affecting the immune system and metabolic dysfunction, which provides a new direction for understanding potential pathogenic processes and therapeutic targets in sepsis.", "claims": [{"id": "claim-method", "statement": "The differentially expressed genes (DEGs) between sepsis and healthy controls in the combined dataset were identified by using the “limma” R package, and the thresholds of |Fold Change (FC)|>1.2 and P < 0.05 were chosen as statistically significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38495196", "locator": "sections[4] \"Identification of dysregulated genes and cuproptosis-related genes in sepsis\" paragraph 2", "quote": "The differentially expressed genes (DEGs) between sepsis and healthy controls in the combined dataset were identified by using the “limma” R package, and the thresholds of |Fold Change (FC)|>1.2 and P < 0.05 were chosen as statistically significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to analyse in this study could be found in the GEO database. The original contributions presented in the study are included in the article and supplementary material, further reasonable request can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38495196", "locator": "sections[31] \"Data availability statement\" paragraph 2", "quote": "The data used to analyse in this study could be found in the GEO database. The original contributions presented in the study are included in the article and supplementary material, further reasonable request can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Overall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38495196", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Overall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis samples."}, {"asset": "source:pmid:38495196", "locator": "sections[17] \"Identification of the cuproptosis-related diagnostic genes by machine learning\" paragraph 2", "quote": "Finally, the 6 DECuGs (ANKRD9, DLD, LIPT1, MTF1, PDHB, UBE2D4) as diagnostic biomarkers for sepsis were identified by the intersection of the two machine learning algorithms (Fig. 5C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Overall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis 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:38495196"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE134347"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE134347"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38495196", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e27379", "sha256": "sha256:257faa736e836c876d1c7ae0517dd1707bd12abc4f370999ad2b4ac001d567ca", "bytes": 61242, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e27379", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10943398", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE134347", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134347", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE134347", "version": null, "note": "The data used to analyse in this study could be found in the GEO database. 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