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Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis

BACKGROUND: Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring O...

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Autores principales: Meng, Jinzhi, Du, Huawei, Lv, Haiyuan, Lu, Jinfeng, Li, Jia, Yao, Jun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853447/
https://www.ncbi.nlm.nih.gov/pubmed/36685513
http://dx.doi.org/10.3389/fimmu.2022.1072526
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author Meng, Jinzhi
Du, Huawei
Lv, Haiyuan
Lu, Jinfeng
Li, Jia
Yao, Jun
author_facet Meng, Jinzhi
Du, Huawei
Lv, Haiyuan
Lu, Jinfeng
Li, Jia
Yao, Jun
author_sort Meng, Jinzhi
collection PubMed
description BACKGROUND: Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring OA-dysregulated autophagy and inhibiting chondrocyte apoptosis in vitro. MATERIAL AND METHODS: Two publicly available transcriptomic mRNA OA-related datasets (GSE10575 and GSE51588) were explored for biomarker identification by least absolute shrinkage and selection operator (LASSO) regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE). We applied the GSE32317 and GSE55457 cohorts to validate the markers’ efficacy for diagnosis. The connections of markers to chondrocyte autophagy and apoptosis in OA were also comprehensively explored in vitro using molecular biology approaches, including qRT-PCR and Western blot. RESULTS: We identified 286 differentially expressed genes (DEGs). These DEGs were enriched in the ECM-receptor interaction and PI3K/AKT signaling pathway. After external cohort validation and protein-protein interaction (PPI) network construction, PDK1 was finally identified as a diagnostic marker for OA. The pharmacological properties of BX795-downregulated PDK1 expression inhibited LPS-induced chondrocyte inflammation and apoptosis and rescued OA-dysregulated autophagy. Additionally, the phosphorylation of the mediators associated with the MAPK and PI3K/AKT pathways was significantly downregulated, indicating the regulatory function of PDK1 in apoptosis and autophagy via MAPK and PI3K/AKT-associated signaling pathways in chondrocytes. A significantly positive association between the PDK1 expression and Neutrophils, Eosinophils, Plasma cells, and activated CD4 memory T cells, as well as an evident negative correlation between T cells follicular helper and CD4 naive T cells, were detected in the immune cell infiltration analysis. CONCLUSIONS: PDK1 can be used as a diagnostic marker for OA. Inhibition of its expression can rescue OA-dysregulated autophagy and inhibit apoptosis by reducing the phosphorylation of PI3K/AKT and MAPK signaling pathways.
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spelling pubmed-98534472023-01-21 Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis Meng, Jinzhi Du, Huawei Lv, Haiyuan Lu, Jinfeng Li, Jia Yao, Jun Front Immunol Immunology BACKGROUND: Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring OA-dysregulated autophagy and inhibiting chondrocyte apoptosis in vitro. MATERIAL AND METHODS: Two publicly available transcriptomic mRNA OA-related datasets (GSE10575 and GSE51588) were explored for biomarker identification by least absolute shrinkage and selection operator (LASSO) regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE). We applied the GSE32317 and GSE55457 cohorts to validate the markers’ efficacy for diagnosis. The connections of markers to chondrocyte autophagy and apoptosis in OA were also comprehensively explored in vitro using molecular biology approaches, including qRT-PCR and Western blot. RESULTS: We identified 286 differentially expressed genes (DEGs). These DEGs were enriched in the ECM-receptor interaction and PI3K/AKT signaling pathway. After external cohort validation and protein-protein interaction (PPI) network construction, PDK1 was finally identified as a diagnostic marker for OA. The pharmacological properties of BX795-downregulated PDK1 expression inhibited LPS-induced chondrocyte inflammation and apoptosis and rescued OA-dysregulated autophagy. Additionally, the phosphorylation of the mediators associated with the MAPK and PI3K/AKT pathways was significantly downregulated, indicating the regulatory function of PDK1 in apoptosis and autophagy via MAPK and PI3K/AKT-associated signaling pathways in chondrocytes. A significantly positive association between the PDK1 expression and Neutrophils, Eosinophils, Plasma cells, and activated CD4 memory T cells, as well as an evident negative correlation between T cells follicular helper and CD4 naive T cells, were detected in the immune cell infiltration analysis. CONCLUSIONS: PDK1 can be used as a diagnostic marker for OA. Inhibition of its expression can rescue OA-dysregulated autophagy and inhibit apoptosis by reducing the phosphorylation of PI3K/AKT and MAPK signaling pathways. Frontiers Media S.A. 2023-01-06 /pmc/articles/PMC9853447/ /pubmed/36685513 http://dx.doi.org/10.3389/fimmu.2022.1072526 Text en Copyright © 2023 Meng, Du, Lv, Lu, Li and Yao https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Meng, Jinzhi
Du, Huawei
Lv, Haiyuan
Lu, Jinfeng
Li, Jia
Yao, Jun
Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis
title Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis
title_full Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis
title_fullStr Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis
title_full_unstemmed Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis
title_short Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis
title_sort identification of the osteoarthritis signature gene pdk1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853447/
https://www.ncbi.nlm.nih.gov/pubmed/36685513
http://dx.doi.org/10.3389/fimmu.2022.1072526
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