基于集成生物信息学分析、机器学习和分子对接技术鉴定代谢综合征相关类风湿关节炎的诊断基因和药物预测。

BACKGROUND: Interactions between the immune and metabolic systems may play a crucial role in the pathogenesis of metabolic syndrome-associated rheumatoid arthritis (MetS-RA). The purpose of this study was to discover candidate biomarkers for the diagnosis of RA patients who also had MetS. METHODS: Three RA datasets and one MetS dataset were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms including Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) were employed to identify hub genes in MetS-RA. Enrichment analysis was used to explore underlying common pathways between MetS and RA. Receiver operating characteristic curves were applied to assess the diagnostic performance of nomogram constructed based on hub genes. Protein-protein interaction, Connectivity Map (CMap) analyses, and molecular docking were utilized to predict the potential small molecule compounds for MetS-RA treatment. qRT-PCR was used to verify the expression of hub genes in fibroblast-like synoviocytes (FLS) of MetS-RA. The effects of small molecule compounds on the function of RA-FLS were evaluated by wound-healing assays and angiogenesis experiments. The CIBERSORT algorithm was used to explore immune cell infiltration in MetS and RA. RESULTS: MetS-RA key genes were mainly enriched in immune cell-related signaling pathways and immune-related processes. Two hub genes ( and ) were selected as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning and proved to have a high diagnostic value (area under the curve, , 0.92; , 0.90). qRT-PCR results showed that the expression of and in MetS-RA-FLS was significantly higher than that in non-MetS-RA-FLS (nMetS-RA-FLS). The combination of CMap analysis and molecular docking predicted camptothecin (CPT) as a potential drug for MetS-RA treatment. validation, CPT was observed to suppress the cell migration capacity and angiogenesis capacity of MetS-RA-FLS. Immune cell infiltration results revealed immune dysregulation in MetS and RA. CONCLUSION: Two hub genes were identified in MetS-RA, a nomogram for the diagnosis of RA and MetS was established based on them, and a potential therapeutic small molecule compound for MetS-RA was predicted, which offered a novel research perspective for future serum-based diagnosis and therapeutic intervention of MetS-RA.

背景：免疫和代谢系统之间的相互作用可能在代谢综合征相关类风湿关节炎（MetS-RA）的发病机制中起关键作用。本研究的目的是发现用于诊断同时患有 MetS 的 RA 患者的候选生物标志物。

方法：从基因表达综合数据库（GEO）中获得三个 RA 数据集和一个 MetS 数据集。采用差异表达分析、加权基因共表达网络分析（WGCNA）和机器学习算法（包括最小绝对收缩和选择算子（LASSO）回归和随机森林（RF）），以鉴定 MetS-RA 中的枢纽基因。富集分析用于探索 MetS 和 RA 之间潜在的共同途径。受试者工作特征曲线用于评估基于枢纽基因构建的列线图的诊断性能。蛋白质-蛋白质相互作用、连通性映射（CMap）分析和分子对接用于预测 MetS-RA 治疗的潜在小分子化合物。qRT-PCR 用于验证 MetS-RA 成纤维样滑膜细胞（FLS）中枢纽基因的表达。通过划痕愈合实验和血管生成实验评估小分子化合物对 RA-FLS 功能的影响。CIBERSORT 算法用于探索 MetS 和 RA 中的免疫细胞浸润。

结果：MetS-RA 的关键基因主要富集在免疫细胞相关信号通路和免疫相关过程中。通过机器学习选择两个枢纽基因（和）作为开发列线图的候选生物标志物，该列线图具有理想的诊断性能（曲线下面积，，0.92；，0.90）。qRT-PCR 结果表明，MetS-RA-FLS 中和的表达明显高于非 MetS-RA-FLS（nMetS-RA-FLS）。CMap 分析和分子对接的组合预测喜树碱（CPT）是治疗 MetS-RA 的潜在药物。验证实验表明，CPT 可抑制 MetS-RA-FLS 的细胞迁移能力和血管生成能力。免疫细胞浸润结果表明 MetS 和 RA 存在免疫失调。

结论：在 MetS-RA 中鉴定出两个枢纽基因，基于它们建立了 RA 和 MetS 的诊断列线图，并预测了潜在的治疗 MetS-RA 的小分子化合物，为未来基于血清的 MetS-RA 诊断和治疗干预提供了新的研究视角。