Identification of ZNF652 as a Diagnostic and Therapeutic Target in Osteoarthritis Using Machine Learning DOI Creative Commons
Yeping Chen,

Rongyuan Liang,

Xifan Zheng

et al.

Journal of Inflammation Research, Journal Year: 2024, Volume and Issue: Volume 17, P. 10141 - 10161

Published: Dec. 1, 2024

Purpose: Osteoarthritis (OA) is the most common degenerative joint disease. However, its etiology remains largely unknown. Zinc Finger Protein 652 (ZNF652) a transcription factor implicated in various biological processes. Nevertheless, role OA has not been elucidated. Methods: The search term "osteoarthritis" was utilized to procure transcriptome data relating patients and healthy people from Gene Expression Omnibus (GEO) database. Then screening process initiated identify differentially expressed genes (DEGs). DEGs were discerned using three distinct machine learning methods. accuracy of these diagnosing evaluated Receiver Operating Characteristic (ROC) Curve. A competitive endogenous RNA (ceRNA) visualization network established delve into potential regulatory targets. ZNF652 expression confirmed cartilage rats quantitative reverse polymerase chain reaction (qRT-PCR) Western blotting (WB) analyzed an independent t -test. Results: identified as DEG exhibited highest diagnostic value for according ROC analysis. GO KEGG enrichment analyses suggest that plays vital development through processes including nitric oxide anabolism, macrophage proliferation, immune response, PI3K/Akt MAPK signaling pathways. increased validated qRT-PCR (1.193 ± 0.005 vs 1.000 0.005, p < 0.001) WB (0.981 0.055 0.856 0.026, = 0.012) Conclusion: found be related pathogenesis can potentially serve therapeutic target OA. underlying mechanism pathways, cells their functions findings need clinical trials molecular requires further study. Keywords: osteoarthritis, zinc finger protein 652, algorithms, cell

Language: Английский

Identification of ZNF652 as a Diagnostic and Therapeutic Target in Osteoarthritis Using Machine Learning DOI Creative Commons
Yeping Chen,

Rongyuan Liang,

Xifan Zheng

et al.

Journal of Inflammation Research, Journal Year: 2024, Volume and Issue: Volume 17, P. 10141 - 10161

Published: Dec. 1, 2024

Purpose: Osteoarthritis (OA) is the most common degenerative joint disease. However, its etiology remains largely unknown. Zinc Finger Protein 652 (ZNF652) a transcription factor implicated in various biological processes. Nevertheless, role OA has not been elucidated. Methods: The search term "osteoarthritis" was utilized to procure transcriptome data relating patients and healthy people from Gene Expression Omnibus (GEO) database. Then screening process initiated identify differentially expressed genes (DEGs). DEGs were discerned using three distinct machine learning methods. accuracy of these diagnosing evaluated Receiver Operating Characteristic (ROC) Curve. A competitive endogenous RNA (ceRNA) visualization network established delve into potential regulatory targets. ZNF652 expression confirmed cartilage rats quantitative reverse polymerase chain reaction (qRT-PCR) Western blotting (WB) analyzed an independent t -test. Results: identified as DEG exhibited highest diagnostic value for according ROC analysis. GO KEGG enrichment analyses suggest that plays vital development through processes including nitric oxide anabolism, macrophage proliferation, immune response, PI3K/Akt MAPK signaling pathways. increased validated qRT-PCR (1.193 ± 0.005 vs 1.000 0.005, p < 0.001) WB (0.981 0.055 0.856 0.026, = 0.012) Conclusion: found be related pathogenesis can potentially serve therapeutic target OA. underlying mechanism pathways, cells their functions findings need clinical trials molecular requires further study. Keywords: osteoarthritis, zinc finger protein 652, algorithms, cell

Language: Английский

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