2023, Vol. 27 ›› Issue (11): 1669-1676

Bioinformatics analysis of cuproptosis genes in immune infiltration of osteoarthritis

Wang Weiwei1, Ou Zhixue2, Zhou Yi1, Li Tong1   

Abstract: BACKGROUND: Immune infiltration plays an important role in the progression of osteoarthritis, while cuproptosis is a novel modality of programmed cell death recently discovered. There are no studies on the mechanisms by which cuproptosis genes regulate immune infiltration in osteoarthritis.
OBJECTIVE: To integrate cuproptosis genes and GEO database-related chips, analyze the correlation between cuproptosis genes and immune infiltration, build a risk model, and perform gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis as well as miRNA and Chinese medicine prediction so as to provide some theoretical support for future exploration on the mechanism underlying cuproptosis in osteoarthritis immune infiltration.
METHODS: Eligible osteoarthritis-related microarrays were retrieved through the GEO database and normalized. Immune infiltration was extracted and quantified based on the processed gene expression matrix, and the correlation between immune infiltrating cells and their functions was analyzed, as well as their differences between osteoarthritis group and control group. Integrated cuproptosis gene expression matrix and standardized chip gene expression matrix were used to screen out the cuproptosis genes related to osteoarthritis. A risk model was then constructed to analyze the risk probability of cuproptosis genes related to osteoarthritis. In addition, upstream miRNAs were predicted by FunRich software. Finally, gene ontology, KEGG enrichment analysis and traditional Chinese medicine prediction of osteoarthritis-related cuproptosis genes were carried out through the Enrichr website and Coremine Medical database.
RESULTS AND CONCLUSION: Immune infiltration correlation results showed that there were the strongest positive correlation between dendritic cells and mast cells (r=0.87) and the strongest negative correlation between mast cells and tumor-infiltrating lymphocytes (r=-0.81). Differential immune infiltration results showed that in the osteoarthritis group, dendritic cells, immature dendritic cells, macrophages, mast cells, neutrophils, antigen presentation co-suppression, and chemokine C-C-Motif receptors were significantly increased (P < 0.05), while co-stimulation of B cells, Th2 cells, and T cells significantly decreased (P < 0.05). A total of 10 osteoarthritis-related cuproptosis genes were screened, namely SLC31A1, PDHB, PDHA1, LIPT1 , LIAS, DLD, FDX1, DLST, DLAT, DBT. The risk model results showed that PDHB may be a risk factor for osteoarthritis. Enrichment analysis results showed that osteoarthritis-related cuproptosis genes were mainly enriched in citric acid cycle, pyruvate metabolism, glycolysis/gluconeogenesis signaling pathways. A total of 27 herbal medicines and 1 herbal active ingredient, including rhizoma curcumae (0.004 75), scutellaria baicalensis (0.049), and rhodioloside (0.000 237), were predicted through the Coremine Medical database, of which rhodioloside was a potential herbal active ingredient for PDHB, the independent risk factor of osteoarthritis. A total of 29 upstream miRNAs of osteoarthritis-related cuproptosis genes, including has-miR-7a-5p, has-miR-7e-5p, and has-miR-96-5p, were predicted by FunRich software. To conclude, osteoarthritis-related cuproptosis genes are mainly involved in citric acid cycle, pyruvate metabolism, glycolysis/gluconeogenesis signaling pathways to regulate immune cells and functions, including dendritic cells, macrophages, B cells, antigen presentation co-suppression in patients with osteoarthritis. In this process, 29 miRNAs, including has-miR-7a-5p, has-miR-7e-5p, and has-miR-96-5p, may also play an important role. In addition, Chinese herbs and herbal active ingredients, such as sheep liver, rhizoma curcumae, and rhodioloside, may be potential sources of molecular drugs for osteoarthritis.
Key words: cuproptosis, osteoarthritis, immune infiltration, bioinformatics, GEO database, single-sample gene set enrichment analysis, PDHB, miRNA