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Updated: May 5, 2026
Application of Atomic Force Microscopy to Detect Early Osteoarthritis
Published on: May 24, 2020
Bioinformatics and machine learning approaches to explore the biomarkers in fatty acid degradation linked to
Jian Li1, Jinpeng Wei1, Hua Wu1,2
1Department of Orthopedics, Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China.
Background:
Fatty acid degradation (FAD) plays a crucial role in maintaining cellular energy homeostasis, with its dysfunction serves as an important pathological basis for the progression of various diseases. However, the specific regulatory mechanisms of this process in osteoarthritis (OA) remain to be further elucidated. This study aims to identify potential FAD-associated biomarkers and to investigate the role and potential mechanisms of FAD in OA.
Methods:
OA-related datasets and FAD-associated genes were retrieved from publicly accessible databases. Multiple bioinformatics methods were employed to reveal the potential connections among the aforementioned genes. Screening FAD-associated differentially expressed genes highly correlated with OA (hub OA-FADEGs) using machine learning methods. Single-sample gene set enrichment analysis (ssGSEA) was employed to characterize immune cell infiltration in OA and to explore their correlations with FADEGs. Additionally, scatter plots were used to evaluate the diagnostic efficacy of hub OA-FADEGs. Finally, enrichment analysis of hub OA-FADEGs and their corresponding therapeutic drugs was performed using the Drug Signatures Database (DSigDB).
Results:
Machine learning algorithms were applied to screen for hub OA-FADEGs, identifying APOD, COL1A1, SULF1, CHI3L1, PENK, and ADM as genes that are significantly upregulated or downregulated in OA samples. These results were subsequently verified by qRT-PCR. Furthermore, the aforementioned genes all exhibit strong diagnostic efficacy for OA. Ultimately, we identified 28 therapeutic drugs that may target hub OA-FADEGs using DSigDB.
Conclusion:
Based on comprehensive bioinformatics analysis, this study proposes that 6 key hub OA-FADEGs, including APOD, COL1A1, SULF1, CHI3L1, PENK, and ADM, could serve as potential diagnostic biomarkers for OA and highlights their regulatory roles in disease progression. These findings provide novel insights into the metabolic pathogenesis underlying OA.
