SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer's disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38871989", "kind": "science-record", "revision": 4, "content_hash": "sha256:5700b9539261dd62ee0d056fa750952876e6694ae1d5205403fc5682396818b0", "created_at": "2026-11-01T20:28:55Z", "updated_at": "2026-11-01T22:54:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "SCG5 and MITF may be novel markers of copper metabolism immunorelevance in Alzheimer's disease.", "doi": "10.1038/s41598-024-64599-z", "pmid": "38871989", "authors": ["Xianbo Zhuang", "Yitong Xia", "Yingli Liu", "Tingting Guo", "Zhangyong Xia", "Zheng Wang", "Guifeng Zhang"], "journal": "Scientific reports", "year": 2024, "abstract": "The slow-developing neurological disorder Alzheimer's disease (AD) has no recognized etiology. A bioinformatics investigation verified copper metabolism indicators for AD development. GEO contributed AD-related datasets GSE1297 and GSE5281. Differential expression analysis and WGCNA confirmed biomarker candidate genes. Each immune cell type in AD and control samples was scored using single sample gene set enrichment analysis. Receiver Operating Characteristic (ROC) analysis, short Time-series Expression Miner (STEM) grouping, and expression analysis between control and AD samples discovered copper metabolism indicators that impacted AD progression. We test clinical samples and cellular function to ensure study correctness. Biomarker-targeting miRNAs and lncRNAs were predicted by starBase. Trust website anticipated biomarker-targeting transcription factors. In the end, Cytoscape constructed the TF/miRNA-mRNA and lncRNA-miRNA networks. The DGIdb database predicted biomarker-targeted drugs. We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38. A TF/miRNA-mRNA regulation network was then established with two miRNAs (hsa-miR-34a-5p and 34c-5p), six TFs (NFKB1, RELA, MYC, HIF1A, JUN, and SP1), and four biomarkers. The DGIdb database contained 171 drugs targeting ten copper metabolism-relevant biomarkers (BRCA1, MITF, NFKBIA, CD38, CCK2, HPRT1, SPHK1, LDHA, SCG5, and SYT1). Copper metabolism biomarkers CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38 alter AD progression, laying the groundwork for disease pathophysiology and novel AD diagnostic and treatment.", "claims": [{"id": "claim-method", "statement": "Relying on the threshold value as p-value ≤ 0.05 and |log2FoldChange (FC)|≥ 0.5, we first determined the DEGs between AD and control samples through ‘limma’ package (version 3.48.3)16,17. The DE-CMRGs in AD were acquired by overlapping the DEGs and CMRGs. A co-expression network was created using the R package ‘WGCNA’ (1.70–3)18. The pROC package28 calculated each gene’s ROC curve AUC. Data from different groups was compared using the Wilcoxon or student’s t-test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38871989", "locator": "sections[13] \"Differential expression analysis\" paragraph 2", "quote": "Relying on the threshold value as p-value ≤ 0.05 and |log2FoldChange (FC)|≥ 0.5, we first determined the DEGs between AD and control samples through ‘limma’ package (version 3.48.3)16,17."}, {"asset": "source:pmid:38871989", "locator": "sections[13] \"Differential expression analysis\" paragraph 2", "quote": "The DE-CMRGs in AD were acquired by overlapping the DEGs and CMRGs."}, {"asset": "source:pmid:38871989", "locator": "sections[14] \"Weighted gene co-expression network analysis (WGCNA)\" paragraph 2", "quote": "A co-expression network was created using the R package ‘WGCNA’ (1.70–3)18."}, {"asset": "source:pmid:38871989", "locator": "sections[23] \"Statistical analysis\" paragraph 2", "quote": "The pROC package28 calculated each gene’s ROC curve AUC."}, {"asset": "source:pmid:38871989", "locator": "sections[23] \"Statistical analysis\" paragraph 2", "quote": "Data from different groups was compared using the Wilcoxon or student’s t-test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets utilized in this investigation are accessible through online repositories. The repository/repositories’ names and accession number(s) are available at the following link: https://www.ncbi.nlm.nih.gov/, GSE1297 and GSE5281.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38871989", "locator": "sections[32] \"notes\" paragraph 1", "quote": "The datasets utilized in this investigation are accessible through online repositories. The repository/repositories’ names and accession number(s) are available at the following link: https://www.ncbi.nlm.nih.gov/, GSE1297 and GSE5281."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 14 differentially expressed copper metabolism-related genes associated with AD progression: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38871989", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We identified 57 differentially expressed copper metabolism-related genes (DE-CMRGs). Next, fourteen copper metabolism indicators impacting AD progression were identified: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study identified 14 differentially expressed copper metabolism-related genes associated with AD progression: CCK, ATP6V1E1, SYT1, LDHA, PAM, HPRT1, SCG5, ATP6V1D, GOT1, NFKBIA, SPHK1, MITF, BRCA1, and CD38.", "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:38871989"], "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:GSE1297"], "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:group_comparison_wilcoxon", "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:GSE1297"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38871989", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-024-64599-z", "sha256": "sha256:05508a27ecc906390ade95e8fb8b8643d179491555f1fc0bb45828ceb5fffa9e", "bytes": 35820, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-024-64599-z", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11176367", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE1297", "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=GSE1297", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE1297", "version": null, "note": "The datasets utilized in this investigation are accessible through online repositories. 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(B) THE CCK8 assay showed that the OD value at 450 nm was", "negative control group (the oe-NC). (B) THE CCK8 assay showed that the OD value at 450 nm in"], "order_basis": "paper_text_order:2456", "id": "assay:1"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.85, "evidence": ["roup. Materials and methods Cell culture and transfection The human HT22 cells were acquired from iCel"], "order_basis": "paper_text_order:3415", "id": "assay:2"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Relying on the threshold value as p-value ≤ 0.05 and |log2FoldChange (FC)|≥ 0.5, we first determined the DEGs between AD and control samples through ‘limma’ package (version 3.48.3)16,17. The DE-CMRGs in AD were acquired by overlapping the DEGs and CMRGs. A co-expression network was created using the R package ‘WGCNA’ (1.70–3)18. The pROC package28 calculated each gene’s ROC curve AUC. Data from different groups was compared using the Wilcoxon or student’s t-test."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}