ArticleBioMed research international2022
Screening Potential Diagnostic Biomarkers for Age-Related Sarcopenia in the Elderly Population by WGCNA and LASSO.
Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 7 citations in OpenAlex.
- Integrated transcriptomic and metabolomic analyses reveal heterosis for meat quality of Neijiang pigs.Frontiers in veterinary science · 2024Article
- Zebrafish as an Emerging Model for Sarcopenia: Considerations, Current Insights, and Future Directions.International journal of molecular sciences · 2023Review
- PGK1: A Common Biomarker and Therapeutic Target Linking Sarcopenia and Osteoporosis Through Fibroblast-Mediated Pathways.IET systems biologyArticle
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Sarcopenia is a common chronic disease characterized by age-related decline in skeletal muscle mass and function, and the lack of diagnostic biomarkers makes community-based screening problematic. Methods: Three gene expression profiles related with sarcopenia were downloaded and merged by searching the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and eigengenes of a module in the merged dataset were identified by differential expression analysis and weighted gene coexpression network analysis (WGCNA), and common genes (CGs) were defined as the intersection of DEGs and eigengenes of a module. CGs were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) analysis was performed to screen the CGs for identifying the diagnostic biomarkers of sarcopenia. Based on the diagnostic biomarkers, we established a novel nomogram model of sarcopenia. At last, we validated the diagnostic biomarkers and evaluated the diagnostic performance of the nomogram model by the area under curve (AUC) value. Results: We screened out 107 DEGs and 788 eigengenes in the turquoise module, and 72 genes were selected as CGs of sarcopenia by intersection. GO analysis showed that CGs were mainly involved in metal ion detoxification and mitochondrial structure, and KEGG analysis revealed that CGs were mainly enriched in the mineral absorption, glucagon signaling pathway, FoxO signaling pathway, insulin signaling pathway, AMPK signaling pathway, and estrogen signaling pathway. Then, six diagnostic biomarkers (ARHGAP36, FAM171A1, GPCPD1, MT1X, ZNF415, and RXRG) were identified by LASSO analysis. Finally, the validation AUC values indicated that the six diagnostic biomarkers had high diagnostic accuracy for sarcopenia. Conclusion: We identified six diagnostic biomarkers with high diagnostic performance, providing new insights into the incidence and progression of sarcopenia in future research.
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