Bioinformatics analysis and identification to immune-related markers of osteoporosis.
- Author:
Jiao SUN
1
;
Shan CONG
1
;
Li LUO
2
Author Information
1. Department of Rheumatism and Immunity, the First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China.
2. Department of Rheumatism and Immunity, the First Affiliated Hospital of Xinjiang Medical University, Urumqi 830011, China.*Corresponding author, E-mail: luoli2102@yeah.net.
- Publication Type:Journal Article
- MeSH:
Humans;
Vascular Endothelial Growth Factor A/genetics*;
Biomarkers;
Osteoporosis/genetics*;
Computational Biology/methods*;
ErbB Receptors/genetics*;
Gene Expression Profiling/methods*
- From:
Chinese Journal of Cellular and Molecular Immunology
2023;39(12):1108-1113
- CountryChina
- Language:Chinese
-
Abstract:
Objective To identify immune-related dysregulation mechanisms and potential diagnostic predictive biomarkers in osteoporosis. Methods Gene expression data for both osteoporosis and control populations were retrieved from the GSE35958 and GSE56815 datasets. Immune-related differentially expressed genes (DEGs) were obtained by screening DEGs and were compared with the immunology database and analysis portal (ImmPort) database. Enrichment analysis of these immune-related DEGs was conducted using the Clusterprofiler software package. A protein-protein interaction network was built with the STRING database, which is a search tool for finding interacting genes/proteins, and the top 10 genes with the highest network connectivity were identified as candidate genes. Subsequently, the diagnostic predictive effect of candidate genes was evaluated using receiver operating characteristic (ROC) curves, logistic regression, and column plots. Finally, PCR and Western blot analysis were applied to detect the differential expression of these genes in bone marrow tissue of patients with osteoporosis. Results A total of 138 immune-related DEGs were obtained through intersection analysis. The results of the enrichment analysis indicated that these genes were involved in biological functions such as immune inflammation and signaling pathways including T cell receptors, mitogen activated protein kinase (MAPK), rat sarcoma virus oncogene homologs (Ras), osteoclast differentiation, and B cell receptors. In addition, among the candidate genes, upregulated vascular endothelial growth factor A (VEGFA) and epidermal growth factor receptor (EGFR) and downregulated AKT1, SRC, and JUN in osteoporosis showed the highest connectivity. Among them, VEGFA, EGFR, JUN, and AKT1 demonstrated the best diagnostic predictive value. Conclusion The screening of immune-related DEGs will enhance the understanding of osteoporosis and facilitate the development of immunotherapy targets.
