Bioinformatics analysis and prediction of Alzheimer's disease and alcohol dependence based on Ferroptosis-related genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37520121", "kind": "science-record", "revision": 4, "content_hash": "sha256:b39104b57d2504fa50079de329bd4d747c0a6faa72c59af1c45e0d1ab8ffb647", "created_at": "2026-11-01T19:02:17Z", "updated_at": "2026-11-01T22:09:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis and prediction of Alzheimer's disease and alcohol dependence based on Ferroptosis-related genes.", "doi": "10.3389/fnagi.2023.1201142", "pmid": "37520121", "authors": ["Mei Tian", "Jing Shen", "Zhiqiang Qi", "Yu Feng", "Peidi Fang"], "journal": "Frontiers in aging neuroscience", "year": 2023, "abstract": "Background: Alzheimer's disease (AD) is a neurodegenerative disease whose origins have not been universally accepted. Numerous studies have demonstrated the relationship between AD and alcohol dependence; however, few studies have combined the origins of AD, alcohol dependence, and programmed cell death (PCD) to analyze the mechanistic relationship between the development of this pair of diseases. We demonstrated in previous studies the relationship between psychiatric disorders and PCD, and in the same concerning neurodegeneration-related AD, we found an interesting link with the Ferroptosis pathway. In the present study, we explored the bioinformatic interactions between AD, alcohol dependence, and Ferroptosis and tried to elucidate and predict the development of AD from this aspect.\nMethods: We selected the Alzheimer's disease dataset GSE118553 and alcohol dependence dataset GSE44456 from the Gene Expression Omnibus (GEO) database. Ferroptosis-related genes were gathered through Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and relevant literature, resulting in a total of 88 related genes. For the AD and alcohol dependence datasets, we conducted Limma analysis to identify differentially expressed genes (DEGs) and performed functional enrichment analysis on the intersection set. Furthermore, we used ferroptosis-related genes and the DEGs to perform machine learning crossover analysis, employing Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify candidate immune-related central genes. This analysis was also used to construct protein-protein interaction networks (PPI) and artificial neural networks (ANN), as well as to plot receiver operating characteristic (ROC) curves for diagnosing AD and alcohol dependence. We analyzed immune cell infiltration to explore the role of immune cell dysregulation in AD. Subsequently, we conducted consensus clustering analysis of AD using three relevant candidate gene models and examined the immune microenvironment and functional pathways between different subgroups. Finally, we generated a network of gene-gene interactions and miRNA-gene interactions using Networkanalyst.\nResults: The crossover of AD and alcohol dependence DEG contains 278 genes, and functional enrichment analysis showed that both AD and alcohol dependence were strongly correlated with Ferroptosis, and then crossed them with Ferroptosis-related genes to obtain seven genes. Three candidate genes were finally identified by machine learning to build a diagnostic prediction model. After validation by ANN and PPI analysis, ROC curves were plotted to assess the diagnostic value of AD and alcohol dependence. The results showed a high diagnostic value of the predictive model. In the immune infiltration analysis, functional metabolism and immune microenvironment of AD patients were significantly associated with Ferroptosis. Finally, analysis of target genes and miRNA-gene interaction networks showed that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4.\nConclusion: We obtained a diagnostic prediction model with good effect by comprehensive analysis, and validation of ROC in AD and alcohol dependence data sets showed good diagnostic, predictive value for both AD (AUC 0. 75, CI 0.91-0.60), and alcohol dependence (AUC 0.81, CI 0.95-0.68). In the consensus clustering grouping, we identified variability in the metabolic and immune microenvironment between subgroups as a likely cause of the different prognosis, which was all related to Ferroptosis function. Finally, we discovered that hsa-mir-34a-5p and has-mir-106b-5p could simultaneously regulate the expression of both CYBB and ACSL4.", "claims": [{"id": "claim-method", "statement": "LASSO regression was applied for candidate gene identification.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37520121", "locator": "sections[15] \"3.3. Identification of candidate genes linked with Ferroptosis in Alzheimer’s disease combined with alcohol dependence by machine learning and PPI network construction\" paragraph 2", "quote": "LASSO regression was applied for candidate gene identification."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37520121", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO regression and random forest analyses of the GSE118553 and GSE44456 source datasets, cross-screened with ferroptosis-related genes, identified CYBB, STEAP3, and ACSL4 as the three candidate genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37520121", "locator": "sections[15] \"3.3. Identification of candidate genes linked with Ferroptosis in Alzheimer’s disease combined with alcohol dependence by machine learning and PPI network construction\" paragraph 2", "quote": "LASSO regression was applied for candidate gene identification."}, {"asset": "source:pmid:37520121", "locator": "sections[15] \"3.3. Identification of candidate genes linked with Ferroptosis in Alzheimer’s disease combined with alcohol dependence by machine learning and PPI network construction\" paragraph 2", "quote": "We also used RF regression for the identification of candidate genes, from which four potential candidate biomarkers were determined in the AD dataset (Figure 4F), and six potential candidate biomarkers were determined in the alcohol dependence dataset (Figure 4G)."}, {"asset": "source:pmid:37520121", "locator": "sections[15] \"3.3. Identification of candidate genes linked with Ferroptosis in Alzheimer’s disease combined with alcohol dependence by machine learning and PPI network construction\" paragraph 2", "quote": "The genes identified by these two-machine learning (ML) algorithms were then cross-analyzed, which resulted in three candidate genes (CYBB, STEAP3, and ACSL4) (Figure 4H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LASSO regression and random forest analyses of the GSE118553 and GSE44456 source datasets, cross-screened with ferroptosis-related genes, identified CYBB, STEAP3, and ACSL4 as the three candidate genes.", "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:37520121"], "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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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE118553", "GSE44456"], "resolved_accessions": ["GSE118553", "GSE44456"], "statements": ["The original contributions presented in this study are included in this article/Supplementary material, further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE118553, GSE44456 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:37520121", "data:geo:GSE118553"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE118553"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:48:18Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12661, "completion_tokens": 1301, "cached_tokens": 128}}, "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:37520121", "type": "paper", "label": "pmid:37520121"}, {"id": "source:pmid:37520121", "type": "manuscript", "label": "PMID 37520121 full text"}, {"id": "data:geo:GSE118553", "type": "dataset", "label": "GEO GSE118553"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37520121", "type": "paper", "label": "pmid:37520121"}, {"id": "source:pmid:37520121", "type": "manuscript", "label": "PMID 37520121 full text"}, {"id": "data:geo:GSE118553", "type": "dataset", "label": "GEO GSE118553"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37520121", "type": "extraction", "used": ["source:pmid:37520121"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE118553", "type": "accession_resolution", "used": ["data:geo:GSE118553"], "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:37520121", "parent_hash": "sha256:821a62fbc678e63b55c666e107eadd0724037f67ca0fac7d02626ea58169f2fa", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE118553 licence not read", "asset:data:geo:GSE118553 not downloaded", "asset:data:geo:GSE44456 licence not read", "asset:data:geo:GSE44456 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.7, "evidence": ["Limma"], "order_basis": "analysis_only", "id": "assay:1"}]}