Bioinformatics identification and validation of genes related to fatty acid metabolism in rheumatoid arthritis

doi:10.12307/2024.544

Abstract

Abstract: BACKGROUND: Research has shown that fatty acid metabolism genes are closely related to the development of rheumatoid arthritis. Therefore, exploring the progression of rheumatoid arthritis based on fatty acid metabolism genes is of clinical significance.
OBJECTIVE: To investigate whether fatty acid metabolism genes can serve as reliable biomarkers for predicting the progression of rheumatoid arthritis.
METHODS: Gene data related to synovial tissue were downloaded from the Gene Expression Comprehensive Database (GEO). STRING was used to construct the protein-protein interaction network analysis. Cytoscape was utilized for biological annotation (gene ontology) and signaling pathway enrichment analysis (Kyoto Encyclopedia of Genes and Genomes). Fatty acid metabolism related genes were screened from the molecular feature database (MSigDB). Least absolute shrinkage and selection operator and support vector machine recursive feature elimination feature were used to screen for potential biomarkers. Immune cell infiltration levels in normal individuals and rheumatoid arthritis patients were assessed using the CIBERSORT algorithm. Finally, the expression levels of fatty acid metabolism related genes were verified using the receiver operating characteristic curve in GSE77298.
RESULTS AND CONCLUSION: 361 differentially expressed genes in rheumatoid arthritis were identified, of which 13 overlapped with the reported fatty acid metabolism related genes. Based on machine learning algorithms, five genes were selected, and the receiver operating characteristic curve showed that five genes (PCK1, PDK1, PTGS2, PLA2G2D, and DPEP2) could predict the development of rheumatoid arthritis. The CIBERSORT algorithm results showed that five genes were associated with activated mast cells, neutrophils, resting mast cells, and memory resting CD4+ T cells. The receiver operating characteristic curve showed that PLA2G2D and PCK1 have high diagnostic value. To conclude, the expression characteristics of fatty acid metabolism related genes can serve as potential biomarkers for predicting clinical outcomes, which can further improve the accuracy of prediction in RA patients.

Key words: rheumatoid arthritis, fatty acid metabolism related genes, differentially expressed gene, biomarker, bioinformatics analysis

CLC Number:

Lu Xiaoling, Liu Bin, Xu Bin. Bioinformatics identification and validation of genes related to fatty acid metabolism in rheumatoid arthritis[J]. Chinese Journal of Tissue Engineering Research, 2024, 28(32): 5116-5121.

Figures/Tables 6

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