ArticleBone & joint research2017
Identification of potential pathogenic genes associated with osteoporosis.
Article in Bone & joint research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed, 52 citations in OpenAlex.
- HIRA promotes osteogenic differentiation of BMSCs and ameliorates osteoporosis by mediating M2 polarization of macrophages through the YAP1/β-catenin pathway.Molecular and cellular biochemistry · 2026Article
- Exploring the role of mitochondrial dysfunction-related genes in osteoporosis using weighted gene co-expression network analysis and machine learning.European journal of medical research · 2025Article
- LGR4 (GPR48): The Emerging Inter-Bridge in Osteoimmunology.Biomedicines · 2025Review
- Comprehensive bioinformatics analysis reveals novel potential biomarkers associated with aging and mitochondria in osteoporosis.Scientific reports · 2025Article
- Exploring the association between circadian rhythms and osteoporosis: new diagnostic and therapeutic targets identified via machine learning.Frontiers in molecular biosciences · 2025Article
- Exploring the various functions of PHD finger protein 20: beyond the unknown.Toxicological research · 2025Review
- SIRT1 and ZNF350 as novel biomarkers for osteoporosis: a bioinformatics analysis and experimental validation.Molecular biology reports · 2024Article
- Identification of potential cell death-related biomarkers for diagnosis and treatment of osteoporosis.BMC musculoskeletal disorders · 2024Article
- Knockdown of the UL-16 binding protein 1 promotes osteoblast differentiation of human mesenchymal stem cells by activating the SMAD2/3 pathway.BMC musculoskeletal disorders · 2024Article
- RPL35A promotes the progression of cholangiocarcinoma by mediating HSPA8 ubiquitination.Biology direct · 2024Article
- Unveiling the unexplored novel signatures for osteoporosis via a detailed bioinformatics and molecular experiments based approach.American journal of translational research · 2024Article
- Alzheimer's Disease and Impaired Bone Microarchitecture, Regeneration and Potential Genetic Links.Life (Basel, Switzerland) · 2023Review
- Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms.Frontiers in molecular biosciences · 2023Article
- Up-regulated and hypomethylated genes are causative factors and diagnostic markers of osteoporosis.American journal of translational research · 2023Article
- Digits in a dish: AnFrontiers in cell and developmental biology · 2023Article
- Identification of combined biomarkers for predicting the risk of osteoporosis using machine learning.Aging · 2022Article
- Identification and validation of novel gene markers of osteoporosis by weighted co expression analysis.Annals of translational medicine · 2022Article
- Identification of key biomarkers in steroid-induced osteonecrosis of the femoral head and their correlation with immune infiltration by bioinformatics analysis.BMC musculoskeletal disorders · 2022Article
- Identification of osteoporosis based on gene biomarkers using support vector machine.Open medicine (Warsaw, Poland) · 2022Article
- Immune Cell Infiltration Characteristics of Pigmented Villous Nodular Synovitis and Prediction of Potential Diagnostic Markers Based on Bioinformatics.BioMed research international · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesOsteoporosis is a chronic disease. The aim of this study was to identify key genes in osteoporosis.
methodsMicroarray data sets GSE56815 and GSE56814, comprising 67 osteoporosis blood samples and 62 control blood samples, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in osteoporosis using Limma package (3.2.1) and Meta-MA packages. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to identify biological functions. Furthermore, the transcriptional regulatory network was established between the top 20 DEGs and transcriptional factors using the UCSC ENCODE Genome Browser. Receiver operating characteristic (ROC) analysis was applied to investigate the diagnostic value of several DEGs.
resultsA total of 1320 DEGs were obtained, of which 855 were up-regulated and 465 were down-regulated. These differentially expressed genes were enriched in Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, mainly associated with gene expression and osteoclast differentiation. In the transcriptional regulatory network, there were 6038 interactions pairs involving 88 transcriptional factors. In addition, the quantitative reverse transcriptase-polymerase chain reaction result validated the expression of several genes (VPS35, FCGR2A, TBCA, HIRA, TYROBP, and JUND). Finally, ROC analyses showed that VPS35, HIRA, PHF20 and NFKB2 had a significant diagnostic value for osteoporosis.
conclusionGenes such as VPS35, FCGR2A, TBCA, HIRA, TYROBP, JUND, PHF20, NFKB2, RPL35A and BICD2 may be considered to be potential pathogenic genes of osteoporosis and may be useful for further study of the mechanisms underlying osteoporosis.
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