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Wu YK, Liu CD, Liu C, Wu J, Xie ZG. Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis. Front Immunol 2024; 15:1387311. [PMID: 38711508 PMCID: PMC11070572 DOI: 10.3389/fimmu.2024.1387311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 04/08/2024] [Indexed: 05/08/2024] Open
Abstract
Background Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. Methods We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Results Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. Conclusion RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.
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Affiliation(s)
- Ying-Kai Wu
- Department of Orthopaedic, The Second Affiliated Hospital of Soochow University, Jiangsu, China
- Department of Orthopaedics, Ningyang County First People’s Hospital, Tai an, China
| | - Cai-De Liu
- Department of General Practice, Affiliated Hospital of Weifang Medical University, Wei Fang, China
| | - Chao Liu
- Gynecology and Obstetrics, Ningyang County Maternal and Child Health Hospital, Tai an, China
| | - Jun Wu
- Medical Cosmetology and Plastic Surgery Center, LinYi People’s Hospital, Lin Yi, China
| | - Zong-Gang Xie
- Department of Orthopaedic, The Second Affiliated Hospital of Soochow University, Jiangsu, China
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Chen B, Lin C, Jin X, Zhang X, Yang K, Wang J, Zhang F, Zhang Y, Ji Y, Meng Z. Construction of a diagnostic model for osteoarthritis based on transcriptomic immune-related genes. Heliyon 2024; 10:e23636. [PMID: 38187306 PMCID: PMC10770511 DOI: 10.1016/j.heliyon.2023.e23636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 12/08/2023] [Accepted: 12/08/2023] [Indexed: 01/09/2024] Open
Abstract
Background Osteoarthritis (OA) is a leading cause of disability globally, affecting over 500 million individuals worldwide. However, accurate and early diagnosis of OA is challenging to achieve. Immune-related genes play an essential role in OA development. Therefore, the objective of this study was to develop a diagnostic model for OA based on immune-related genes identified in synovial membrane. Methods The gene expression profile of OA were downloaded based on four datasets. The significantly differentially expressed genes (DEGs) between OA and control groups were selected. The differential immune cells were analyzed, followed by immune-related DEGs screening. WGCNA was used to screen module genes and these genes were further selected through optimization algorithm. Then, nomogram model was constructed. Chemical drug small molecule related to OA was predicted. Finally, expression levels of several key genes were validated by qRT-PCR through construction of OA rat models. Results The total 656 DEGs were obtained. Eight immune cells were significantly differential between two groups, and 317 immune-related DEGs were obtained. WGCNA identified three modules. The genes in modules were significantly involved in 15 pathways, involving in 65 genes. Then 12 DEGs were screened as the final optimal combination of DEGs, such as CEBPB, CXCL1, JUND, GABARAPL2 and PDGFC. The Nomogram model was also constructed. Furthermore, the chemical small molecules, such as acetaminophen, aspirin, and caffeine were predicted. The expression levels of CEBPB, CXCL1, GABARAPL2 and PDGFC were validated in OA rat models. Conclusion A diagnostic model based on twelve immune related genes was constructed. These model genes, such as CEBPB, CXCL1, GABARAPL2, and PDGFC, may serve as diagnostic biomarkers and immunotherapeutic targets.
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Affiliation(s)
- Bo Chen
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Chun Lin
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Xing Jin
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Xibin Zhang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Kang Yang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Jianjian Wang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Feng Zhang
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
| | - Yuxin Zhang
- Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai 200011, China
- Department of Rehabilitation Medicine, Huangpu Branch, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
| | - Yingying Ji
- The affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, 214151, China
| | - Zhaoxiang Meng
- Rehabilitation Medicine Department, Northern Jiangsu People's Hospital, Yangzhou, Jiangsu, 225001, China
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Chen Z, Hua Y. Gene signature based on glycolysis is closely related to immune infiltration of patients with osteoarthritis. Cytokine 2023; 171:156377. [PMID: 37769593 DOI: 10.1016/j.cyto.2023.156377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 08/14/2023] [Accepted: 09/20/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND Osteoarthritis (OA) is a degenerative arthritis with high levels of clinical heterogeneity. Aberrant metabolism such as shifting from oxidative phosphorylation to glycolysis is a response to changes in the inflammatory microenvironment of OA. Therefore, there is a pressing need to identify novel glycolysis regulators during OA progression. METHODS We systematically studied glycolysis patterns mediated by 141 glycolysis regulators in 74 human synovial samples and discussed the characteristics of the immune microenvironment modified by glycolysis. The random forest (RF) method was applied to screen candidate hub glycolysis regulators in OA. RT-qPCR was performed to validate these key regulators. Then distinct glycolysis patterns were identified, and systematic correlation between these glycolysis patterns and immune cell infiltration was analyzed. The glycolysis score was constructed to quantify glycolysis patterns together with immune infiltration of individual OA patient. RESULTS 56 glycolysis-related differentially expressed genes (DEGs) were identified between OA and non-OA samples. STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the probability of OA. Two glycolysis patterns in OA were identified. Glycolysis cluster A with higher glycolysis score was related to an inflamed phenotype. CONCLUSIONS Taken together, our results established a glycolysis-based genetic signature for OA, guided in-depth studies on the metabolic mechanism of OA, and facilitated to explore new clinical treatment strategies.
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Affiliation(s)
- Ziyi Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, No. 12, Wulumuqi Zhong Road, Shanghai 200040, China
| | - Yinghui Hua
- Department of Sports Medicine, Huashan Hospital, Fudan University, No. 12, Wulumuqi Zhong Road, Shanghai 200040, China.
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Chen Z, Hua Y. Identification of m7G-related hub biomarkers and m7G regulator expression pattern in immune landscape during the progression of osteoarthritis. Cytokine 2023; 170:156313. [PMID: 37549488 DOI: 10.1016/j.cyto.2023.156313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 07/10/2023] [Accepted: 07/30/2023] [Indexed: 08/09/2023]
Abstract
BACKGROUND Accumulating evidence has shown that aberrant N7-methylguanosine (m7G) RNA methylation played an important role in the occurrence and development of cancer. However, knowledge of m7G modifications in inflammatory diseases is limited. Osteoarthritis (OA) is the most common arthritic disease with poor prognosis. Our research aimed to identify m7G-related hub biomarkers and investigate m7G regulator expression pattern in immune landscape of OA patients. METHODS Gene expression profiles and their clinical information were obtained from the Gene Expression Omnibus (GEO) database, and differential analysis of 14 m7G-related regulators between elective OA and normal samples was performed. M7G-related hub genes for OA were mined based on single-sample gene set enrichment analysis (ssGSEA) and the random forest (RF) algorithm, and qRT-PCR was performed to confirm the abnormal expression of hub genes. Enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory analysis based on m7G hub genes were performed. Then we predicted several candidate drugs related to m7G hub genes using DSigDB database. Moreover, we comprehensively evaluated m7G methylation patterns in OA samples and systematically correlated these modification patterns with the characteristics of immune cell infiltration. The m7G score was generated to quantify m7G methylation patterns for individual OA patients by the application of principal component analysis (PCA) algorithm. RESULTS We constructed an OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1). Two m7G methylation patterns in OA were discovered to show distinct biological characteristics, and an m7G score were generated. M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. CONCLUSIONS Our study was the first to comprehensively investigate the m7G methylation dysregulations in immune landscape during the progression of OA. These 4 m7G gene-related signatures can be used as novel OA biomarkers to predict the occurrence of OA. Evaluating the m7G methylation patterns of OA individuals will contribute to enhancing our cognition of immune infiltration characterization and guiding more effective immunotherapy strategies.
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Affiliation(s)
- Ziyi Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yinghui Hua
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China.
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