Hub genes, a diagnostic model, and immune infiltration based on ferroptosis-linked genes in schizophrenia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38390236", "kind": "science-record", "revision": 3, "content_hash": "sha256:e831706a33cb3e7edd143b35e4030c6b6ae83a51f20fc60ba218b0d41c637f53", "created_at": "2026-11-01T19:20:20Z", "updated_at": "2026-11-01T21:32:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Hub genes, a diagnostic model, and immune infiltration based on ferroptosis-linked genes in schizophrenia.", "doi": "10.1016/j.ibneur.2024.01.007", "pmid": "38390236", "authors": ["Kun Lian", "Yongmei Li", "Wei Yang", "Jing Ye", "Hongbing Liu", "Tianlan Wang", "Guangya Yang", "Yuqi Cheng", "Xiufeng Xu"], "journal": "IBRO neuroscience reports", "year": 2024, "abstract": "Background: Schizophrenia (SCZ) is a prevalent and serious mental disorder, and the exact pathophysiology of this condition is not fully understood. In previous studies, it has been proven that ferroprotein levels are high in SCZ. It has also been shown that this inflammatory response may modify fibromodulin. Accumulating evidence indicates a strong link between metabolism and ferroptosis. Therefore, the present study aims to identify ferroptosis-linked hub genes to further investigate the role that ferroptosis plays in the development of SCZ.\nMaterial and methods: From the GEO database, four microarray data sets on SCZ (GSE53987, GSE38481, GSE18312, and GSE38484) and ferroptosis-linked genes were extracted. Using the prefrontal cortex expression matrix of SCZ patients and healthy individuals as the control group from GSE53987, weighted gene co-expression network analysis (WGCNA) was performed to discover SCZ-linked module genes. From the feed, genes associated with ferroptosis were retrieved. The intersection of the module and ferroptosis-linked genes was done to obtain the hub genes. Then, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and Gene Set Enrichment Analysis (GSEA) were conducted. The SCZ diagnostic model was established using logistic regression, and the GSE38481, GSE18312, and GSE38484 data sets were used to validate the model. Finally, hub genes linked to immune infiltration were examined.\nResults: A total of 13 SCZ module genes and 7 hub genes linked to ferroptosis were obtained: DECR1, GJA1, EFN2L2, PSAT1, SLC7A11, SOX2, and YAP1. The GO/KEGG/GSEA study indicated that these hub genes were predominantly enriched in mitochondria and lipid metabolism, oxidative stress, immunological inflammation, ferroptosis, Hippo signaling pathway, AMP-activated protein kinase pathway, and other associated biological processes. The diagnostic model created using these hub genes was further confirmed using the data sets of three blood samples from patients with SCZ. The immune infiltration data showed that immune cell dysfunction enhanced ferroptosis and triggered SCZ.\nConclusion: In this study, seven critical genes that are strongly associated with ferroptosis in patients with SCZ were discovered, a valid clinical diagnostic model was built, and a novel therapeutic target for the treatment of SCZ was identified by the investigation of immune infiltration.", "claims": [{"id": "claim-method", "statement": "Using logistic regression to analyse the GSE53987 dataset, a multigene prediction model was constructed (Fig. 7A).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38390236", "locator": "sections[17] \"Construction of the diagnostic model\" paragraph 2", "quote": "Using logistic regression to analyse the GSE53987 dataset, a multigene prediction model was constructed (Fig. 7A)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the finding is available in the GEO dataset (GSE53987, GSE38481, GSE18312, and GSE38484) and FerrDb (zhounan.org).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38390236", "locator": "sections[28] \"Data Availability\" paragraph 2", "quote": "The data used to support the finding is available in the GEO dataset (GSE53987, GSE38481, GSE18312, and GSE38484) and FerrDb (zhounan.org)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A seven-gene diagnostic model showed high diagnostic performance, with an average AUC of 0.789.", "kind": "quantitative", "target": {"value": 0.789, "unit": "auc", "tolerance": null, "source": "The findings demonstrated that the diagnostic performance of the predictive model that was constructed using these seven genes was high, with an average AUC of 0.789 (Fig. 7B)."}, "evidence": [{"asset": "source:pmid:38390236", "locator": "sections[17] \"Construction of the diagnostic model\" paragraph 2", "quote": "The findings demonstrated that the diagnostic performance of the predictive model that was constructed using these seven genes was high, with an average AUC of 0.789 (Fig. 7B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A seven-gene diagnostic model showed high diagnostic performance, with an average AUC of 0.789.", "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:38390236"], "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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No tolerance is published because the paper states none."}], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["glm_logit_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: logistic."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["glm_logit_fit"], "assets": ["source:pmid:38390236", "data:geo:GSE18312"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE18312"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:09:41Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13407, "completion_tokens": 1997, "cached_tokens": 264}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38390236", "type": "paper", "label": "pmid:38390236"}, {"id": "source:pmid:38390236", "type": "manuscript", "label": "PMID 38390236 full text"}, {"id": "data:geo:GSE18312", "type": "dataset", "label": "GEO GSE18312"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38390236", "type": "paper", "label": "pmid:38390236"}, {"id": "source:pmid:38390236", "type": "manuscript", "label": "PMID 38390236 full text"}, {"id": "data:geo:GSE18312", "type": "dataset", "label": "GEO GSE18312"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38390236", "type": "extraction", "used": ["source:pmid:38390236"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE18312", "type": "accession_resolution", "used": ["data:geo:GSE18312"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38390236", "parent_hash": "sha256:9012894f0d117ba22412b3c322ea4bb8bce5a41ab5207d837138ce805052715b", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE18312 licence not read", "asset:data:geo:GSE18312 not downloaded", "asset:data:geo:GSE38481 licence not read", "asset:data:geo:GSE38481 not downloaded", "asset:data:geo:GSE38484 licence not read", "asset:data:geo:GSE38484 not downloaded", "asset:data:geo:GSE53987 licence not read", "asset:data:geo:GSE53987 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ial and methods: From the GEO database, four microarray data sets on SCZ (GSE53987, GSE38481, GSE183"], "order_basis": "paper_text_order:662", "id": "assay:1"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Using logistic regression to analyse the GSE53987 dataset, a multigene prediction model was constructed (Fig. 7A)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}