Kun Zhang, Hailong Li, Xinhong Chen, Ping Tang, Meng Wang, Chunting Yang, Rong Su, Xiaqin Gao, Fan Zhang, Juan Han
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引用次数: 0
Abstract
Sarcopenia and osteoporosis share pathophysiological links, but their co-occurrence mechanisms remain unclear. This study aimed to identify molecular mediators of their co-development using bioinformatics. Datasets for sarcopenia (GSE56815) and osteoporosis (GSE9103) were retrieved from GEO. Differentially expressed genes (DEGs) were analysed via edgeR and limma. Gene ontology (GO), Kyoto encyclopaedia of genes and genomes (KEGG) and weighted gene co-expression network analysis (WGCNA) identified shared pathways and hub genes. Protein–protein interaction (PPI) networks were constructed using STRING and Cytoscape. We validated hub genes in independent datasets (GSE13850, GSE8479) and assessed via ROC curves. Immune infiltration, single-cell analysis and drug prediction were performed. We identified 134 common DEGs (30 upregulated, 104 downregulated). WGCNA and PPI analysis revealed 14 hub genes (APOE, CDK2, PGK1, HRAS, RUNX2 etc.), all with ROC-AUC > 0.6. PGK1 was consistently downregulated in both diseases and linked to 21 miRNAs and six transcription factors (HSF1, TP53, JUN etc.). Single-cell analysis localised PGK1 predominantly in skeletal muscle fibroblasts. DrugBank identified lamivudine as a potential PGK1-targeting therapeutic. PGK1 emerged as a central downregulated gene in sarcopenia and osteoporosis, enriched in fibroblasts and modulated by lamivudine. These findings highlight PGK1 as a shared diagnostic and therapeutic target, offering insights into musculoskeletal crosstalk.
期刊介绍:
IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells.
The scope includes the following topics:
Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.