Identification of a potential signature to predict the risk of postmenopausal osteoporosis

Geng, Yannan; Shao, Rui; Xu, Tiantong; Zhang, Lilong

doi:10.1016/j.gene.2023.147942

Cited by 3 publications

(2 citation statements)

References 53 publications

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“… 14 Subsequently, the method proposed by Johnson et al was used to eliminate batch effects. 15 The limma package of R software (version 4.1.2) was used for differential analysis. 16 …”

Section: Methodsmentioning

confidence: 99%

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis

Gao,

Ma,

Shao

et al. 2024

JIR

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Background Progress in research on expression profiles in osteoarthritis (OA) has been limited to individual tissues within the joint, such as the synovium, cartilage, or meniscus. This study aimed to comprehensively analyze the common gene expression characteristics of various structures in OA and construct a diagnostic model. Methods Three datasets were selected: synovium, meniscus, and knee joint cartilage. Modular clustering and differential analysis of genes were used for further functional analyses and the construction of protein networks. Signature genes with the highest diagnostic potential were identified and verified using external gene datasets. The expression of these genes was validated in clinical samples by Real-time (RT)-qPCR and immunohistochemistry (IHC) staining. This study investigated the status of immune cells in OA by examining their infiltration. Results The merged OA dataset included 438 DEGs clustered into seven modules using WGCNA. The intersection of these DEGs with WGCNA modules identified 190 genes. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms, nine signature genes were identified ( CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 ), each demonstrating substantial diagnostic potential (areas under the curve from 0.701 to 0.925). Furthermore, dysregulation of various immune cells has also been observed. Conclusion CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 demonstrated significant diagnostic efficacy in OA and are involved in immune cell infiltration.

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“… 14 Subsequently, the method proposed by Johnson et al was used to eliminate batch effects. 15 The limma package of R software (version 4.1.2) was used for differential analysis. 16 …”

Section: Methodsmentioning

confidence: 99%

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis

Gao,

Ma,

Shao

et al. 2024

JIR

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“…Conversely, certain circRNAs have been associated with osteoclastogenesis and bone resorption, emphasizing their dual roles in bone metabolism [6,9,10]. Furthermore, the dysregulation of circRNAs has been associated with various bone disorders, including osteoporosis, osteoarthritis, osteosarcoma, and bone metastasis [9,[11][12][13][14][15][16][17][18][19][20][21]. Importantly, profiling studies have identified distinct circRNA expression patterns in diseased bone tissues compared to healthy controls, suggesting their potential utility as diagnostic and prognostic biomarkers [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Circular RNA in Bone Biology: A Comprehensive Review

Valenti,

Zerlotin,

Cominacini

et al. 2024

Cells

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Circular RNAs (circRNAs) have emerged as pivotal regulators of gene expression with diverse roles in various biological processes. In recent years, research into circRNAs’ involvement in bone biology has gained significant attention, unveiling their potential as novel regulators and biomarkers in bone-related disorders and diseases. CircRNAs, characterized by their closed-loop structure, exhibit stability and resistance to degradation, underscoring their functional significance. In bone tissue, circRNAs are involved in critical processes such as osteogenic differentiation, osteoclastogenesis, and bone remodeling through intricate molecular mechanisms including microRNA regulation. Dysregulated circRNAs are associated with various bone disorders, suggesting their potential as diagnostic and prognostic biomarkers. The therapeutic targeting of these circRNAs holds promise for addressing bone-related conditions, offering new perspectives for precision medicine. Thus, circRNAs constitute integral components of bone regulatory networks, impacting both physiological bone homeostasis and pathological conditions. This review provides a comprehensive overview of circRNAs in bone biology, emphasizing their regulatory mechanisms, functional implications, and therapeutic potential.

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Knockdown of the UL-16 binding protein 1 promotes osteoblast differentiation of human mesenchymal stem cells by activating the SMAD2/3 pathway

Lai,

Li,

Yang

et al. 2024

BMC Musculoskelet Disord

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Osteoporosis is caused by the imbalance of osteoblasts and osteoclasts. The regulatory mechanisms of differentially expressed genes (DEGs) in pathogenesis of osteoporosis are of significant and needed to be further investigated. GSE100609 dataset downloaded from Gene Expression Omnibus (GEO) database was used to identified DEGs in osteoporosis patients. KEGG analysis was conducted to demonstrate signaling pathways related to enriched genes. Osteoporosis patients and the human mesenchymal stem cells (hMSCs) were obtained for in vivo and in vitro resaerch. Lentivirus construction and viral infection was used to knockdown genes. mRNA expression and protein expression were detected via qRT-PCR and western blot assay separately. Alkaline phosphatase (ALP) activity detection, alizarin Red S (ARS) staining, and expression of bone morphogenetic protein 2 (BMP2), osteocalcin (OCN) and Osterix were evaluated to determine osteoblast differentiation capacity. UL-16 binding protein 1 (ULBP1) gene was upregulated in osteoporosis and downregulated in differentiated hMSCs. Knockdown of ULBP1 increased ALP activity, mineralization ability evaluated by ARS staining, expression of BMP2, OCN and Osterix in differentiated hMSCs. Furthermore, rescue experiment demonstrated that suppressed ULBP1 boosted osteoblast differentiation by activating TNF-β signaling pathway. Knockdown of ULBP1 gene could promoted osteoblast differentiation by activating TNF-β signaling pathway in differentiated hMSCs. ULBP1 may be a the Achilles’ heel of osteoporosis, and suppression of ULBP1 could be a promising treatment for osteoporosis.

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Identification of a potential signature to predict the risk of postmenopausal osteoporosis

Cited by 3 publications

References 53 publications

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis

Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis

Exploring the Role of Circular RNA in Bone Biology: A Comprehensive Review

Knockdown of the UL-16 binding protein 1 promotes osteoblast differentiation of human mesenchymal stem cells by activating the SMAD2/3 pathway

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