Hub genes and diagnostic model associated with mitochondrial function in Alzheimer's disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41308013", "kind": "science-record", "revision": 4, "content_hash": "sha256:e95f4d82156794c7c56989672754613014dd9c040c26582a0f191660c3734729", "created_at": "2026-11-01T20:31:20Z", "updated_at": "2026-11-01T22:57:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Hub genes and diagnostic model associated with mitochondrial function in Alzheimer's disease.", "doi": "10.1002/ame2.70104", "pmid": "41308013", "authors": ["Xuchao Zhu", "Ling Zhang", "Chuan Qin"], "journal": "Animal models and experimental medicine", "year": 2025, "abstract": "BACKGROUND: Alzheimer's disease (AD) represents the most prevalent neurodegenerative disorder, with mitochondrial dysfunction being observed in both AD patients and mouse models. Nonetheless, further investigation is required to elucidate the pathogenic genes associated with AD and to develop early diagnostic methodologies centered on mitochondrial function.\nMETHODS: In this study, the dataset GSE132903 was retrieved from the GEO database, encompassing both non-demented (ND) control and AD samples. Through the combination of differential expression gene analysis, weighted gene co-expression network analysis, and intersection with mitochondrial database gene sets, four hub genes associated with AD were identified. These four hub genes were subsequently validated in APP/PS1 and 5xFAD mouse models using molecular biology techniques.\nRESULTS: The hub genes identified through bioinformatics analysis include SYNJ2BP, VDAC1, NUBPL, and COX19. Within the GSE132903 dataset, the expression levels of SYNJ2BP, NUBPL, and COX19 were significantly elevated in the AD group compared to the non-demented (ND) group, whereas VDAC1 expression was reduced in the AD group relative to the ND group. Furthermore, in the hippocampus of APP/PS1 and 5xFAD mouse models, the expression patterns of SYNJ2BP and NUBPL were consistent with the bioinformatics analysis results.\nCONCLUSION: Hub genes identified here through bioinformatics and molecular biology may help early diagnosis of AD patients and may also help build new AD models to explore its pathogenesis.", "claims": [{"id": "claim-method", "statement": "The package “limma” of R software was applied for differential analysis between AD and ND groups. Specifically, expression profile datasets were utilized, and multiple linear regression was performed with the lmFit function, followed by the application of the eBayes function to adjust standard errors to a common value using empirical Bayesian methods.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41308013", "locator": "sections[4] \"Differential expression analysis\" paragraph 2", "quote": "The package “limma” of R software was applied for differential analysis between AD and ND groups. Specifically, expression profile datasets were utilized, and multiple linear regression was performed with the lmFit function, followed by the application of the eBayes function to adjust standard errors to a common value using empirical Bayesian methods."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The source of dataset GSE132903 is from GEO public database, which contains gene information for the middle temporal gyrus, annotated by the GPL10558 platform, comprising 97 samples from AD and 98 samples from normal control (ND).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41308013", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "The source of dataset GSE132903 is from GEO public database, which contains gene information for the middle temporal gyrus, annotated by the GPL10558 platform, comprising 97 samples from AD and 98 samples from normal control (ND)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Through the combination of differential expression gene analysis, weighted gene co‐expression network analysis, and intersection with mitochondrial database gene sets, four hub genes associated with AD were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41308013", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Through the combination of differential expression gene analysis, weighted gene co‐expression network analysis, and intersection with mitochondrial database gene sets, four hub genes associated with AD were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Through the combination of differential expression gene analysis, weighted gene co‐expression network analysis, and intersection with mitochondrial database gene sets, four hub genes associated with AD were identified.", "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:41308013"], "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:GSE132903"], "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:GSE132903"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41308013", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/ame2.70104", "sha256": 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Specifically, expression profile datasets were utilized, and multiple linear regression was performed with the lmFit function, followed by the application of the eBayes function to adjust standard errors to a common value using empirical Bayesian methods."}}, {"id": "method-2", "family": "regression_modelling", "specific": "ols", "label": "Regression modelling", "dsl_operations": ["ols_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.8, "band": "high", "quote": "linear regression"}}, {"id": "method-3", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "hierarchical clustering"}}], "targets": [], "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": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE132903", "GSE63060"], "resolved_accessions": ["GSE132903", "GSE63060"], "statements": ["The source of dataset GSE132903 is from GEO public database, which contains gene information for the middle temporal gyrus, annotated by the GPL10558 platform, comprising 97 samples from AD and 98 samples from normal control (ND)."], "basis": "The paper names GSE132903, GSE63060 in GEO and the accession resolves in the repository. 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Nonetheless, further investigation i", "subsequently validated in APP/PS1 and 5xFAD mouse models using molecular biology techniques. R", "ore, in the hippocampus of APP/PS1 and 5xFAD mouse models, the expression patterns of SYNJ2BP a", "cience, Chinese Academy of Medical Sciences. Mice were housed in a barrier environment with tempera"], "order_basis": "paper_text_order:214", "id": "assay:1"}, {"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": 1.0, "evidence": ["The package “limma” of R software was applied for differential analysis between AD and ND groups. Specifically, expression profile datasets were utilized, and multiple linear regression was performed with the lmFit function, followed by the application of the eBayes function to adjust standard errors to a common value using empirical Bayesian methods."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}