Department of Orthopedics, The Affiliated Hospital of Qingdao University, Qingdao 266003, Shandong Province, China.
Department of Nephrology, The First Affiliated Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Aging (Albany NY). 2023 May 10;15(9):3807-3825. doi: 10.18632/aging.204714.
Rheumatoid arthritis (RA) causes irreversible joint damage, but the pathogenesis is unknown. Therefore, it is crucial to identify diagnostic biomarkers of RA metabolism-related genes (MRGs). This study obtained transcriptome data from healthy individuals (HC) and RA patients from the GEO database. Weighted gene correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and random forest (RF) algorithms were adopted to identify the diagnostic feature biomarker for RA. In addition, biomarkers were verified by qRT-PCR and Western blot analysis. We established a mouse model of collagen-induced arthritis (CIA), which was confirmed by HE staining and bone structure micro-CT analysis, and then further verified the biomarkers by immunofluorescence. NMR analysis was used to analyze and identify possible metabolites. The correlation of diagnostic feature biomarkers and immune cells was performed using the Spearman-rank correlation algorithm. In this study, a total of 434 DE-MRGs were identified. GO and KEGG enrichment analysis indicated that the DE-MRGs were significantly enriched in small molecules, catabolic process, purine metabolism, carbon metabolism, and inositol phosphate metabolism. AKR1C3, MCEE, POLE4, and PFKM were identified through WGCNA, LASSO, and RF algorithms. The nomogram result should have a significant diagnostic capacity of four biomarkers in RA. Immune infiltration landscape analysis revealed a significant difference in immune cells between HC and RA groups. Our findings suggest that AKR1C3, MCEE, POLE4, and PFKM were identified as potential diagnostic feature biomarkers associated with RA's immune cell infiltrations, providing a new perspective for future research and clinical management of RA.
类风湿关节炎（RA）可导致不可逆的关节损伤，但发病机制尚不清楚。因此，确定与 RA 代谢相关基因（MRGs）相关的诊断生物标志物至关重要。本研究从 GEO 数据库中获取了健康个体（HC）和 RA 患者的转录组数据。采用加权基因相关网络分析（WGCNA）、最小绝对收缩和选择算子（LASSO）和随机森林（RF）算法来识别 RA 的诊断特征生物标志物。此外，通过 qRT-PCR 和 Western blot 分析验证了生物标志物。我们建立了胶原诱导关节炎（CIA）的小鼠模型，通过 HE 染色和骨结构 micro-CT 分析进行了验证，然后通过免疫荧光进一步验证了生物标志物。NMR 分析用于分析和鉴定可能的代谢物。使用 Spearman-rank 相关算法分析和鉴定诊断特征生物标志物和免疫细胞之间的相关性。在这项研究中，共鉴定出 434 个 DE-MRGs。GO 和 KEGG 富集分析表明，DE-MRGs 在小分子、分解代谢过程、嘌呤代谢、碳代谢和肌醇磷酸盐代谢中显著富集。通过 WGCNA、LASSO 和 RF 算法鉴定出 AKR1C3、MCEE、POLE4 和 PFKM。列线图结果应具有四个生物标志物在 RA 中显著的诊断能力。免疫浸润景观分析表明，HC 和 RA 组之间的免疫细胞存在显著差异。我们的研究结果表明，AKR1C3、MCEE、POLE4 和 PFKM 被鉴定为与 RA 免疫细胞浸润相关的潜在诊断特征生物标志物，为 RA 的未来研究和临床管理提供了新的视角。
