Machine learning-based identification of cuproptosis-related markers and immune infiltration in severe community-acquired pneumonia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37279744", "kind": "science-record", "revision": 4, "content_hash": "sha256:4ed700e913a598488e86e7fb0e833c7df912d0b5352b735855145e2c31800582", "created_at": "2026-11-01T19:01:59Z", "updated_at": "2026-11-01T22:52:03Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Machine learning-based identification of cuproptosis-related markers and immune infiltration in severe community-acquired pneumonia.", "doi": "10.1111/crj.13633", "pmid": "37279744", "authors": ["Shuyang Chen", "Zheng Zhou", "Yajun Wang", "Shujing Chen", "Jinjun Jiang"], "journal": "The clinical respiratory journal", "year": 2023, "abstract": "BACKGROUND: Severe community-acquired pneumonia (SCAP) is one of the world's most common diseases and a major etiology of acute respiratory distress syndrome (ARDS). Cuproptosis is a novel form of regulated cell death that can occur in various diseases.\nMETHODS: Our study explored the degree of immune cell infiltration during the onset of severe CAP and identified potential biomarkers related to cuproptosis. Gene expression matrix was obtained from GEO database indexed GSE196399. Three machine learning algorithms were applied: The least absolute shrinkage and selection operator (LASSO), the random forest, and the support vector machine-recursive feature elimination (SVM-RFE). Immune cell infiltration was quantified by single-sample gene set enrichment analysis (ssGSEA) scoring. Nomogram was constructed to verify the applicability of using cuproptosis-related genes to predict the onset of severe CAP and its deterioration toward ARDS.\nRESULTS: Nine cuproptosis-related genes were differentially expressed between the severe CAP group and the control group: ATP7B, DBT, DLAT, DLD, FDX1, GCSH, LIAS, LIPT1, and SLC31A1. All 13 cuproptosis-related genes were involved in immune cell infiltration. A three-gene diagnostic model was constructed to predict the onset of severe CAP: GCSH, DLD, and LIPT1.\nCONCLUSION: Our study confirmed the involvement of the newly discovered cuproptosis-related genes in the progression of SCAP.", "claims": [{"id": "claim-method", "statement": "In order to select CRGs to signify the onset of SCAP, methods including LASSO‐COX regression operation, random forest, and logistic regression were applied.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37279744", "locator": "sections[11] \"Identification of characteristic genes\" paragraph 2", "quote": "In order to select CRGs to signify the onset of SCAP, methods including LASSO‐COX regression operation, random forest, and logistic regression were applied."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available in Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/. These data were derived from the following resources available in the public domain: GSE196399, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE196399.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37279744", "locator": "sections[22] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The data that support the findings of this study are available in Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/. These data were derived from the following resources available in the public domain: GSE196399, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE196399."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "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:37279744"], "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:GSE196399"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE196399"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37279744", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1111/crj.13633", "sha256": "sha256:b20318653a145a185697257f28e0672178ee78a3f96837a7a0270b828da1c742", "bytes": 30099, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1111/crj.13633", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10363779", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE196399", "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=GSE196399", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE196399", "version": null, "note": "The data that support the findings of this study are available in Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/. 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