Alternative therapies in health and medicine · 2024
Association of Different Types of Osteoarthritis with Different Immune-Related Subtypes and Hub Genes.
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Jiang Weigang, Li Tianyang, Tang Zhenzhou, Li Sucheng, Lu Minhua
Аннотация
BACKGROUND: Osteoarthritis (OA) is a diverse disorder that most frequently affects elderly people and makes them disabled. Many investigations have shown that the etiology of OA depends on cartilage wear, but immunology also plays a significant role. Thus, the goal of this study was to define the immune-related etiology of OA.
METHODS: Data from the "Gene Expression Omnibus (GEO)" database were used to find differentially expressed genes (DEGs), and the "Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm" was employed to calculate the quantity of distinct immune cells. We analyzed the results to identify patient subgroups and compare major active pathways.
RESULTS: The macrophage cell population accounts for the greatest percentage of infiltrating immune cells in OA. One hundred and twenty-two common intersection genes were identified, with the network analysis of protein-protein interactions revealing ten hub genes related to OA, including CXCL8, JUN, ATF3, DUSP1, PTGS2, IL6, MMP9, FOS, NFKBIA, and MYC. The random forest model showed that memory-activated CD4 T cells are strongly correlated with other immune cell types, while neutrophils have the weakest correlation with other immune cell types. Violin plots showed that OA patients had a significantly larger quantity of plasma cells and resting mast cells, with a significantly smaller quantity of resting memory CD4 T cells and activated mast cells than healthy controls.
CONCLUSIONS: Two immune-related subgroups of OA were identified by semi-supervised clustering analysis of microarray data, and core genes were also determined by network analysis. A group of the immune infiltrating cells was selected by random forest analysis suggesting they are related to the pathogenesis of OA.
METHODS: Data from the "Gene Expression Omnibus (GEO)" database were used to find differentially expressed genes (DEGs), and the "Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm" was employed to calculate the quantity of distinct immune cells. We analyzed the results to identify patient subgroups and compare major active pathways.
RESULTS: The macrophage cell population accounts for the greatest percentage of infiltrating immune cells in OA. One hundred and twenty-two common intersection genes were identified, with the network analysis of protein-protein interactions revealing ten hub genes related to OA, including CXCL8, JUN, ATF3, DUSP1, PTGS2, IL6, MMP9, FOS, NFKBIA, and MYC. The random forest model showed that memory-activated CD4 T cells are strongly correlated with other immune cell types, while neutrophils have the weakest correlation with other immune cell types. Violin plots showed that OA patients had a significantly larger quantity of plasma cells and resting mast cells, with a significantly smaller quantity of resting memory CD4 T cells and activated mast cells than healthy controls.
CONCLUSIONS: Two immune-related subgroups of OA were identified by semi-supervised clustering analysis of microarray data, and core genes were also determined by network analysis. A group of the immune infiltrating cells was selected by random forest analysis suggesting they are related to the pathogenesis of OA.