ArticleJournal of orthopaedic surgery and research2023
Bioinformatics and machine learning were used to validate glutamine metabolism-related genes and immunotherapy in osteoporosis patients.
Article in Journal of orthopaedic surgery and research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Enhancing the effectiveness of immunotherapy in rheumatoid arthritis by delaying immunosenescence triggered by fibroblast-like synoviocytes.Journal of orthopaedic surgery and research · 2025Review
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
- Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis.BMC musculoskeletal disorders · 2024Article
- Article
- A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration.BMC genomics · 2024Article
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
Abstract
backgroundOsteoporosis (OP), often referred to as the "silent disease of the twenty-first century," poses a significant public health concern due to its severity, chronic nature, and progressive course, predominantly affecting postmenopausal women and elderly individuals. The pathogenesis and progression of this disease have been associated with dysregulation in tumor metabolic pathways. Notably, the metabolic utilization of glutamine has emerged as a critical player in cancer biology. While metabolic reprogramming has been extensively studied in various malignancies and linked to clinical outcomes, its comprehensive investigation within the context of OP remains lacking.
methodsThis study aimed to identify and validate potential glutamine metabolism genes (GlnMgs) associated with OP through comprehensive bioinformatics analysis. The identification of GlnMgs was achieved by integrating the weighted gene co-expression network analysis and a set of 28 candidate GlnMgs. Subsequently, the putative biological functions and pathways associated with GlnMgs were elucidated using gene set variation analysis. The LASSO method was employed to identify key hub genes, and the diagnostic efficacy of five selected GlnMgs in OP detection was assessed. Additionally, the relationship between hub GlnMgs and clinical characteristics was investigated. Finally, the expression levels of the five GlnMgs were validated using independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).
resultsFive GlnMgs, namely IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N, were identified in this study. To gain insights into their biological functions, particular emphasis was placed on synaptic transmission GABAergic, inward rectifier potassium channel activity, and the cytoplasmic side of the lysosomal membrane. Furthermore, the diagnostic potential of these five GlnMgs in distinguishing individuals with OP yielded promising results, indicating their efficacy as discriminative markers for OP.
conclusionsThis study discovered five GlnMgs that are linked to OP. They shed light on potential new biomarkers for OP and tracking its progression.
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