Decoding Potential Cuproptosis-Related Genes in Sarcopenia: A Multi-Omics Network Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41463417", "kind": "science-record", "revision": 4, "content_hash": "sha256:38279fa23d6192371c44b099d0cd01bd626e4aa03c88757f21edfc3dc8ec2d9c", "created_at": "2026-11-01T20:31:26Z", "updated_at": "2026-11-01T22:57:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Decoding Potential Cuproptosis-Related Genes in Sarcopenia: A Multi-Omics Network Analysis.", "doi": "10.3390/biology14121642", "pmid": "41463417", "authors": ["Hongyu Yan", "Long Shi", "Yang Li", "Zhiwen Zhang"], "journal": "Biology", "year": 2025, "abstract": "Sarcopenia is a common age-related skeletal muscle disorder that lacks diagnostic and therapeutic options. Emerging evidence suggests that cuproptosis, a copper-dependent form of regulated cell death, contributes to muscle atrophy, yet the underlying associations remain poorly understood. To address this gap, we integrated two GEO datasets (GSE1428 and GSE25941) for differential expression analysis and applied weighted gene co-expression network analysis (WGCNA) to identify disease-related modules. Cuproptosis-related genes (CRGs) from GeneCards database were intersected with DEGs and WGCNA gene modules to obtain sarcopenia-associated cuproptosis DEGs (SAR-CUP DEGs). Functional enrichment was performed using GO, KEGG, GSEA and GSVA. Hub genes were further identified through three machine learning algorithms (LASSO, RF, and SVM). Regulatory networks were constructed via NetworkAnalyst and GeneMANIA database. A diagnostic model was also developed and later validated in an independent dataset (GSE136344). Experimental validation was performed in a D-galactose-induced sarcopenia cell model. We identified 367 DEGs and 7 co-expression modules, among which 14 SAR-CUP DEGs were mainly enriched in mitochondrial energy metabolism pathways. Machine learning methods highlighted SLC25A12 and PABPC4 as hub genes. Regulatory network analysis revealed key modulators, such as FOXC1, miR-16-5p, GOT2, and GOT1. Diagnostic performance analysis demonstrated strong predictive value for SLC25A12 (AUC = 0.879) and PABPC4 (AUC = 0.858), and RT-qPCR confirmed their downregulation in the sarcopenia cell model (p < 0.01). In conclusion, SLC25A12 and PABPC4 are promising biomarkers linking copper metabolism dysregulation with sarcopenia, offering potential targets for diagnosis and therapy.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) in sarcopenia were identified using the “limma” package [31] (version 3.52.2), by comparing transcriptional profiles between the control and sarcopenia groups in the training set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41463417", "locator": "sections[4] \"2.2. 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Identification of the DEGs and Functional Analysis of the Training Set\" paragraph 2", "quote": "A total of 367 DEGs were identified, comprising 83 up-regulated and 284 down-regulated genes (Figure 3A)."}, {"asset": "source:pmid:41463417", "locator": "sections[23] \"3.7. Construction of the Diagnostic Model\" paragraph 3", "quote": "When SLC25A12 and PABPC4 were integrated into a combined model, the ROC curve achieved a higher AUC of 0.881 (95% CI = 0.786–0.976), reflecting enhanced diagnostic performance (Figure 8G)."}, {"asset": "source:pmid:41463417", "locator": "sections[24] \"3.8. 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