Identification of core genes of osteoarthritis by bioinformatics

doi:10.12307/2025.117

Abstract

Abstract: BACKGROUND: At present, osteoarthritis has become a major disease affecting the quality of life of the elderly, and the therapeutic effect is poor, often focusing on preventing the disease process, and the pathogenesis of osteoarthritis is still not fully understood. Bioinformatics analysis was carried out to explore the main pathogenesis of osteoarthritis and related mechanisms of gene coding regulation.
OBJECTIVE: To screen core differential genes with a major role in osteoarthritis by gene expression profiling.
METHODS: Datasets were downloaded from the Gene Expression Omnibus (GEO): GSE114007, GSE117999, and GSE129147. Differential genes in the GSE114007 and GSE117999 data collections were screened using R software, performing differential genes to weighted gene co-expression network analysis. The module genes most relevant to osteoarthritis were selected to perform protein interaction analysis. Candidate core genes were selected using the cytocape software. The candidate core genes were subsequently subjected to least absolute shrinkage and selection operator regression and COX analysis to identify the core genes with a key role in osteoarthritis. The accuracy of the core genes was validated using an external dataset, GSE129147.
RESULTS AND CONCLUSION: (1) A total of 477 differential genes were identified, 265 differential genes associated with osteoarthritis were obtained by weighted gene co-expression network analysis, and 8 candidate core genes were identified. The least absolute shrinkage and selection operator regression analysis finally yielded a differential gene ASPM with core value that was externally validated. (2) It is concluded that abnormal gene ASPM expression screened by bioinformatics plays a key central role in osteoarthritis.

Key words: osteoarthritis, differentially expressed gene, core differentially expressed gene, bioinformatics, LASSO regression analysis

CLC Number:

Zhu Xuekun, Liu Heng, Feng Hui, Gao Yunlong, Wen Lei, Cai Xiaosong, Zhao Ben, Zhong Min. Identification of core genes of osteoarthritis by bioinformatics[J]. Chinese Journal of Tissue Engineering Research, 2025, 29(3): 637-644.

Figures/Tables 7

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