Machine learning combined with bioinformatics to identify and validate key genes for cellular senescence in osteoarthritis

doi:10.12307/2024.344

Abstract

Abstract: BACKGROUND: Cellular senescence is closely related to the development and progression of osteoarthritis, but the specific targets and regulatory mechanisms are not yet clear.
OBJECTIVE: To mine key genes in cellular senescence-mediated osteoarthritis by integrating bioinformatics and machine learning approaches and validate them via experiments to explore the role of cellular senescence in osteoarthritis.
METHODS: The osteoarthritis gene expression profiles obtained from the GEO database were intersected with cellular senescence-related genes obtained from the CellAge database and the expression of the intersected genes was extracted for differential analysis, followed by GO and KEGG analysis of the differential genes. The key osteoarthritis cellular senescence genes were then screened by protein-protein interaction network analysis and machine learning, and in vitro cellular experiments were performed. Finally, the expression of the key genes was detected by qPCR.
RESULTS AND CONCLUSION: A total of 31 osteoarthritis cell senescence differential genes were identified. GO analysis showed that these genes were mainly involved in the biological processes, such as regulation of leukocyte differentiation, monocyte differentiation, regulation of T cell differentiation and exerted roles in DNA transcription factor binding, histone deacetylase binding, chromatin DNA binding, and chemokine binding. KEGG analysis showed that osteoarthritis cell senescence differential genes were mainly activated in the JAK/STAT signaling pathway, PI3K/Akt signaling pathway and FoxO signaling pathway. MYC, a key gene for osteoarthritis cellular senescence, was identified by protein-protein interaction network topology analysis and machine learning methods. The results of the in vitro cellular assay showed that the mRNA expression of MYC was significantly lower in the experimental group (osteoarthritis group) than the control group (normal group) (P < 0.05). To conclude, MYC can be a key gene in the senescence of osteoarthritic cells and may be a new target in the prevention and treatment of osteoarthritis by mediating immune response, inflammatory response and transcriptional regulation.

Key words: osteoarthritis, cellular senescence, key gene, cell experiment, machine learning, bioinformatics

CLC Number:

Yuan Changshen, Liao Shuning, Li Zhe, Wu Siping, Chen Lewei, Liu Jinyi, Li Yanhong, Duan Kan. Machine learning combined with bioinformatics to identify and validate key genes for cellular senescence in osteoarthritis[J]. Chinese Journal of Tissue Engineering Research, 2024, 28(20): 3196-3202.

Figures/Tables 11

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