Decoding Potential Cuproptosis-Related Genes in Sarcopenia: A Multi-Omics Network Analysis
1
College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan 430061, China
2
Hubei Shizhen Laboratory, Wuhan 430061, China
3
Affiliated Hospital of Hubei University of Chinese Medicine, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan 430061, China
4
Hubei Key Laboratory of Theory and Application Research of Liver and Kidney in Traditional Chinese Medicine, Wuhan 430061, China
*
Author to whom correspondence should be addressed.
Biology 2025, 14(12), 1642; https://doi.org/10.3390/biology14121642
Submission received: 7 October 2025
/
Revised: 14 November 2025
/
Accepted: 17 November 2025
/
Published: 21 November 2025
(This article belongs to the Special Issue New Insights into Skeletal Muscle Metabolism in Pathological and Physiological Conditions)
Simple Summary
Sarcopenia is an age-related muscle wasting condition that currently lacks specific diagnostic biomarkers and effective treatments. This study investigated a new idea: whether a specific type of cell death linked to copper, called cuproptosis, plays a role in causing this condition. Our goal was to find key genes that connect copper metabolism to sarcopenia. By analyzing public genetic data and using advanced bioinformatics methods, we identified two genes, SLC25A12 and PABPC4, as central players. These genes appear to be involved in the mitochondrial energy production problems seen in the disease. We confirmed that both genes are significantly down-regulated in sarcopenia muscle cells. A diagnostic model based on these genes showed high accuracy in identifying the disease. These findings provide new understanding of sarcopenia and open the door for strategies that might involve monitoring copper levels and developing new drugs or nutritional plans to help prevent and treat sarcopenia in the elderly.
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.
Keywords:
sarcopenia; cuproptosis; SLC25A12; PABPC4; machine learning
