基于多机器学习算法的类风湿关节炎新型生物标志物的鉴定和验证：硅基和体内研究。

BACKGROUND: Rheumatoid arthritis (RA) remains one of the most prevalent chronic joint diseases. However, due to the heterogeneity among RA patients, there are still no robust diagnostic and therapeutic biomarkers for the diagnosis and treatment of RA. METHODS: We retrieved RA-related and pan-cancer information datasets from the Gene Expression Omnibus and The Cancer Genome Atlas databases, respectively. Six gene expression profiles and corresponding clinical information of GSE12021, GSE29746, GSE55235, GSE55457, GSE77298, and GSE89408 were adopted to perform differential expression gene analysis, enrichment, and immune component difference analyses of RA. Four machine learning algorithms, including LASSO, RF, XGBoost, and SVM, were used to identify RA-related biomarkers. Unsupervised cluster analysis was also used to decipher the heterogeneity of RA. A four-signature-based nomogram was constructed and verified to specifically diagnose RA and osteoarthritis (OA) from normal tissues. Consequently, RA-HFLS cell was utilized to investigate the biological role of in RA. In addition, comparisons of diagnostic efficacy and biological roles among and other classic biomarkers of RA were also performed. RESULTS: Immune and stromal components were highly enriched in RA. Chemokine- and Th cell-related signatures were significantly activated in RA tissues. Four promising and novel biomarkers, including , , , and , were identified and verified, which could be treated as novel treatment and diagnostic targets for RA. Nomograms based on the four signatures might aid in distinguishing and diagnosing RA, which reached a satisfactory performance in both training (AUC = 0.894) and testing (AUC = 0.843) cohorts. Two distinct subtypes of RA patients were identified, which further verified that these four signatures might be involved in the immune infiltration process. Furthermore, knockdown of could significantly suppress the proliferation and invasion ability of RA cell line and thus could be treated as a novel therapeutic target. CRTAM owned a great diagnostic performance for RA than previous biomarkers including , , , , , , and . Mechanically, CRTAM could also be involved in the progression through immune dysfunction, fatty acid metabolism, and genomic instability across several cancer subtypes. CONCLUSION: , , , and were highly expressed in RA tissues and might function as pivotal diagnostic and treatment targets by deteriorating the immune dysfunction state. In addition, might fuel cancer progression through immune signals, especially among RA patients.

背景：类风湿关节炎（RA）仍然是最常见的慢性关节疾病之一。然而，由于 RA 患者存在异质性，目前仍没有用于 RA 诊断和治疗的稳健诊断和治疗生物标志物。

方法：我们分别从基因表达综合数据库和癌症基因组图谱数据库中检索了与 RA 相关的信息和泛癌信息数据集。采用 GSE12021、GSE29746、GSE55235、GSE55457、GSE77298 和 GSE89408 中的六个基因表达谱和相应的临床信息，进行 RA 的差异表达基因分析、富集分析和免疫成分差异分析。使用 LASSO、RF、XGBoost 和 SVM 四种机器学习算法来识别 RA 相关的生物标志物。还进行了无监督聚类分析以破译 RA 的异质性。构建并验证了基于四个特征的列线图，以特异性地从正常组织中诊断 RA 和骨关节炎（OA）。随后，利用 RA-HFLS 细胞来研究在 RA 中的生物学作用。此外，还比较了和其他 RA 经典生物标志物之间的诊断效能和生物学作用。

结果：在 RA 中高度富集了免疫和基质成分。趋化因子和 Th 细胞相关的特征在 RA 组织中明显激活。鉴定并验证了四个有前途的新型生物标志物，包括、、、和，它们可以作为 RA 的新型治疗和诊断靶点。基于四个特征的列线图可能有助于区分和诊断 RA，在训练（AUC=0.894）和测试（AUC=0.843）队列中都取得了令人满意的性能。确定了两种不同的 RA 患者亚型，这进一步验证了这四个特征可能参与了免疫浸润过程。此外，CRTAM 的敲低可以显著抑制 RA 细胞系的增殖和侵袭能力，因此可以作为一种新的治疗靶点。CRTAM 对 RA 的诊断性能优于包括、、、、、、和在内的以前的生物标志物。在机制上，CRTAM 还可以通过多种癌症亚型的免疫功能障碍、脂肪酸代谢和基因组不稳定性参与癌症的进展。

结论：、、、在 RA 组织中高表达，可能通过恶化免疫功能障碍状态，作为关键的诊断和治疗靶点。此外，CRTAM 可能通过免疫信号，尤其是在 RA 患者中，促进癌症的进展。