Gene signature based on glycolysis is closely related to immune infiltration of patients with osteoarthritis
Abstract Background:Osteoarthritis (OA) is a degenerative joint disease characterized by low-grade inflammation and high levels of clinical heterogeneity. Aberrant metabolism such as shifting from oxidative phosphorylation to glycolysis is a response to changes in the inflammatory micro-environment and may play a key role in cartilage degeneration and OA progression. Therefore, there is a pressing need to identify glycolysis regulators in the diagnosis of OA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis. Methods: We systematically studied glycolysis patterns mediated by 141 glycolysis regulators in 74 samples and discussed the characteristics of the immune microenvironment modified by glycolysis. The random forest was applied to screen candidate glycolysis regulators to predict the occurrence of OA. RT-qPCR was performed to validate these glycolysis regulators. Then two 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 differentially expressed genes (DEGs) of glycolysis were identified between OA and normal samples. STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the risk of OA using the random forest (RF) method. Two glycolysis patterns in OA were identified and glycolysis scoring system was constructed to show distinct individual immune characteristics. Glycolysis cluster A and higher glycolysis score was revealed to be related to an inflamed phenotype. Conclusions: Taken together, these results established a genetic signature for OA based on glycolysis, which has reference significance for the in-depth study of the metabolic mechanism of OA and the exploration of new clinical treatment strategies..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
ResearchSquare.com - (2022) vom: 08. Dez. Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chen, Ziyi [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.21203/rs.3.rs-2132594/v1 |
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| PPN (Katalog-ID): |
XRA037539876 |
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| 520 | |a Abstract Background:Osteoarthritis (OA) is a degenerative joint disease characterized by low-grade inflammation and high levels of clinical heterogeneity. Aberrant metabolism such as shifting from oxidative phosphorylation to glycolysis is a response to changes in the inflammatory micro-environment and may play a key role in cartilage degeneration and OA progression. Therefore, there is a pressing need to identify glycolysis regulators in the diagnosis of OA, determination of individualized risk, discovery of therapeutic targets, and improve understanding of pathogenesis. Methods: We systematically studied glycolysis patterns mediated by 141 glycolysis regulators in 74 samples and discussed the characteristics of the immune microenvironment modified by glycolysis. The random forest was applied to screen candidate glycolysis regulators to predict the occurrence of OA. RT-qPCR was performed to validate these glycolysis regulators. Then two 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 differentially expressed genes (DEGs) of glycolysis were identified between OA and normal samples. STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the risk of OA using the random forest (RF) method. Two glycolysis patterns in OA were identified and glycolysis scoring system was constructed to show distinct individual immune characteristics. Glycolysis cluster A and higher glycolysis score was revealed to be related to an inflamed phenotype. Conclusions: Taken together, these results established a genetic signature for OA based on glycolysis, which has reference significance for the in-depth study of the metabolic mechanism of OA and the exploration of new clinical treatment strategies. | ||
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