2022, Vol. 26 ›› Issue (23): 3706-3713
Bioinformatics analysis of gene expression profile of peripheral blood lymphocytes in patients with osteoarthritis
Yang Wei1, Yuan Puwei2, Du Longlong2, Li Xuefeng2, Gao Qimeng2, Han Qingmin3
1The Third Clinical Medical College of Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong Province, China; 2Shaanxi University of Chinese Medicine, Xianyang 712046, Shaanxi Province, China; 3the Third Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, Guangdong Province, China
Abstract: BACKGROUND: At present, there are no sensitive markers for monitoring the occurrence or progression of osteoarthritis. The detection of changes in peripheral blood gene expression profiles during the active period of osteoarthritis is helpful to investigate the accurate diagnosis and treatment targets in the blood and explain the pathogenesis.
OBJECTIVE: To analyze the differences in the gene expression profiles of peripheral blood lymphocytes between osteoarthritis patients and healthy people by bioinformatics methods, and to explore the diagnosis and treatment targets of osteoarthritis from the molecular level in the blood, so as to provide new ideas for the study of osteoarthritis.
METHODS: We searched osteoarthritis blood-related chip data from Gene Expression Omnibus and ArrayExpress databases, and downloaded the GSE63359 data set. The screening samples included 46 osteoarthritis patients’ blood and 26 healthy people’s blood, including 32 female patients and 19 healthy women. The R language limma package was used to screen the differentially expressed genes between male/female osteoarthritis patients and male/female healthy people. The ggplot2 package was used to draw volcano plots, and the ComplexHeatmap package was used to draw heat maps. The threshold was set to
P < 0.05 & |log2FC|>0.5 to obtain differentially expressed genes, and then a Venn diagram was made to obtain eight differentially expressed genes: MAP2K7, CREBZF, CLK4, TRIM37, IL18RAP, LRRN3, BLNK, and MS4A1. DAVID was used to analyze the gene ontology and Kyoto encyclopedia of genes and genomes pathways of differentially expressed genes, and the R language ggplot2 package was used to draw bubble plots. STRING and Cytoscape software were used to constructed protein-protein interaction network. Mcode and centiscape plug-in were used for module analysis, and the Cytohubba was used to screen out key genes.
RESULTS AND CONCLUSION: A total of 115 differentially expressed genes were screened out, including 16 up-regulated genes and 99 down-regulated genes. The gene ontology enrichment analysis of all differentially expressed genes mainly focused on “lymphocyte-mediated immunity,” “humoral immune response,” “antigen receptor-mediated signaling pathway,” “B cell receptor signaling pathway,” “immunoglobulin-mediated immune response,” “positive regulation of phagocytosis,” and other biological functions. Kyoto encyclopedia of genes and genomes was mainly enriched in five pathways related to osteoarthritis: hematopoietic cell lineage, Th1 and Th2 cell differentiation, Th17 cell differentiation, osteoclast differentiation and TNF signaling pathway. Protein-protein interaction network and related plug-ins were used to screen out 10 key genes, including 8 core genes, that were highly related to osteoarthritis: tumor necrosis factor, CD19, transferrin receptor, pairing box 5, mitogen-activated protein kinase 7, CD24, CD20 and B cell connection, which were highly related to osteoarthritis inflammation and cell apoptosis. The bioinformatics analysis indicates that the differences in peripheral blood lymphocytes cells gene expression between osteoarthritis patients and healthy people are concentrated in cell apoptosis and inflammation, and thus blood expression profile becomes an effective breakthrough for monitoring osteoarthritis target markers and studying its potential molecular mechanisms.
Key words: osteoarthritis, peripheral blood lymphocytes, gene expression profile, B cell linker, transmembrane 4 domain A1, inflammation, mitogen-activated protein kinase kinase 7, apoptosis, bioinformatics