Open Access

ARTICLE

Screen for autophagy-related biomarkers in osteoarthritis based on bioinformatic analysis

CHAO LIU^*

Department of Huaxin Orthopedics, Hengyang Central Hospital, Hengyang, China

* Corresponding Author: CHAO LIU. Email:

BIOCELL 2024, 48(2), 339-351. https://doi.org/10.32604/biocell.2023.047044

Received 24 July 2023; Accepted 24 October 2023; Issue published 23 February 2024

Abstract

Introduction: Osteoarthritis (OA) is still an important health problem, and understanding its pathological mechanisms is essential for its diagnosis and treatment. There is evidence that autophagy may play a role in OA progression, but the exact mechanism remains unclear. Methods: In this study, we adopted a multi-prong approach to systematically identify the key autophagy-related genes (ARGs) associated with OA. Through weighted gene co-expression network analysis, we initially identified significant gene modules associated with OA. Subsequent differential gene analysis performed on normal and OA specimens. Further analysis later using the MCC algorithm highlighted hub ARGs. These genes were then incorporated into the prediction model of OA. In addition, the expression patterns of these DEGs were verified by in vitro experiments. Results: A total of 104 differentially expressed genes (DEGs) were identified by differential gene analysis, of which 102 were up-regulated and 2 were down-regulated. These differentially expressed genes were closely related to key signaling pathways, such as PI3K-Akt signaling pathway, IL-17 signaling pathway and osteoclast differentiation. Further MCC analysis highlighted 10 hub ARGs, among which ATF3, CYCS, FOXO3, KLF6, NFKBIA and SOCS3 were particularly significant, which were then included in the prediction model of OA, which showed robust prediction ability with an area under the curve of 0.783. In vitro experiments confirmed that the expression pattern of these DEGs was consistent with our prediction. Conclusion: In summary, our comprehensive analysis not only provides new insights into the molecular basis of OA, but also suggests potential biomarkers for its diagnosis.

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Keywords

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