Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Background Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment. Methods From the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential feature genes of OA, and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. In addition, the expression levels of the feature genes were verified using real-time quantitative polymerase chain reaction (qRT-PCR). Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between feature genes and immune cells. Results The Limma package and WGCNA identified a total of 72 DEGs related to OA, of which 12 were up-regulated and 60 were down-regulated. Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). The ANN diagnostic model was constructed based on these four feature genes. The AUC of the training set was 0.942, and the AUC of the validation set was 0.850. In addition, the qRT-PCR validation results demonstrated a significant downregulation of BTG2, DUSP5, and GADD45 mRNA expression levels in OA samples compared to normal samples, while CALML4 mRNA expression level exhibited an upregulation. Immune cell infiltration analysis revealed B cells memory, T cells gamma delta, B cells naive, Plasma cells, T cells CD4 memory resting, and NK cells The abnormal infiltration of activated cells may be related to the progression of OA. Conclusions BTG2, CALML4, DUSP5, and GADD45B were identified as potential feature genes for OA, and an ANN diagnostic model with good diagnostic performance was developed, providing a new perspective for the early diagnosis and personalized treatment of OA. Supplementary Information The online version contains supplementary material available at 10.1186/s13018-024-05340-4.
Background Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment. Methods From the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential feature genes of OA, and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. In addition, the expression levels of the feature genes were verified using real-time quantitative polymerase chain reaction (qRT-PCR). Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between feature genes and immune cells. Results The Limma package and WGCNA identified a total of 72 DEGs related to OA, of which 12 were up-regulated and 60 were down-regulated. Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). The ANN diagnostic model was constructed based on these four feature genes. The AUC of the training set was 0.942, and the AUC of the validation set was 0.850. In addition, the qRT-PCR validation results demonstrated a significant downregulation of BTG2, DUSP5, and GADD45 mRNA expression levels in OA samples compared to normal samples, while CALML4 mRNA expression level exhibited an upregulation. Immune cell infiltration analysis revealed B cells memory, T cells gamma delta, B cells naive, Plasma cells, T cells CD4 memory resting, and NK cells The abnormal infiltration of activated cells may be related to the progression of OA. Conclusions BTG2, CALML4, DUSP5, and GADD45B were identified as potential feature genes for OA, and an ANN diagnostic model with good diagnostic performance was developed, providing a new perspective for the early diagnosis and personalized treatment of OA. Supplementary Information The online version contains supplementary material available at 10.1186/s13018-024-05340-4.
Background: Osteoarthritis (OA) is a common cause of disability among the elderly, profoundly affecting quality of life. This study aims to leverage bioinformatics and machine learning to develop an artificial neural network (ANN) model for diagnosing OA, providing new avenues for early diagnosis and treatment. Methods:From the Gene Expression Omnibus (GEO) database, we first obtained OA synovial tissue microarray datasets. Differentially expressed genes (DEGs) associated with OA were identified through utilization of the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, protein-protein interaction (PPI) network analysis and machine learning were employed to identify the most relevant potential signature genes of OA,and ANN diagnostic model and receiver operating characteristic (ROC) curve were constructed to evaluate the diagnostic performance of the model. Finally, immune cell infiltration analysis was performed using CIBERSORT algorithm to explore the correlation between signature genes and immune cells. Results: The Limma package and WGCNA identified a total of 72 DEGs related to OA,of which 12 were up-regulated and 60 were down-regulated. Then, the PPI network analysis identified 21 hub genes, and three machine learning algorithms finally screened four feature genes (BTG2, CALML4, DUSP5, and GADD45B). The ANN diagnostic model was constructed based on these four feature genes. The AUC of the training set was 0.942, and the AUC of the validation set was 0.850. Immune cell infiltration analysis revealed B cells memory, T cells gamma delta, B cells naive, Plasma cells, T cells CD4 memory resting, and NK cells The abnormal infiltration of activated cells may be related to the progression of OA. Conclusions: In this study, BTG2, CALML4, DUSP5, and GADD45B were identified as potential characteristic genes for OA, and an ANN diagnostic model with excellent diagnostic performance has been developed. Therefore, the diagnostic model established in this research can serve as a reliable reference for early OA diagnosis and provide a novel perspective on the pathogenesis of OA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.