Feng Lijuan, Bai Kaiyong, He Limeng, Wang Hao, Zhang Wei
Department of Nuclear Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
Department of Nuclear Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Front Immunol. 2025 May 22;16:1585895. doi: 10.3389/fimmu.2025.1585895. eCollection 2025.
Rheumatoid arthritis (RA) is an autoimmune inflammatory disease. The mechanism by which telomeres are involved in the development of RA remains unclear. This study aimed to investigate the relationship between RA and telomeres.
In this study, we identified differentially expressed genes (DEGs) between RA and control samples by analyzing transcriptome data from a public database. Candidate genes were determined through the intersection of DEGs and telomere-related genes. Biomarkers were subsequently identified using machine learning algorithms, receiver operating characteristic analysis, and expression level comparisons between RA and control samples. Additionally, a nomogram model was employed to predict the diagnostic ability of biomarkers for RA. Subsequently, the potential mechanisms of these biomarkers in RA were further explored using gene set enrichment analysis (GSEA), subcellular localization, chromosome localization, immune infiltration, functional analysis, molecular regulatory networks, drug prediction, and molecular docking. Furthermore, the expression of biomarkers between RA and control samples was validated through experiments.
ABCC4, S100A8, VAMP2, PIM2, and ISG20 were identified as biomarkers. These biomarkers demonstrated excellent diagnostic ability for RA through a nomogram. Most of the biomarkers were found to be enriched in processes related to allograft rejection and the cell cycle. Subcellular and chromosomal localization analyses indicated that ABCC4 is localized to the plasma membrane, ISG20 to the mitochondria, PIM2 and S100A8 to the cytoplasm, and VAMP2 to the nucleus. Additionally, nine differential immune cells were identified between RA and control samples, with a strong correlation observed between the biomarkers and activated CD4 memory T cells. S100A8, PIM2, and VAMP2 exhibited high similarity to other biomarkers. Furthermore, three transcription factors (TFs), 121 microRNAs (miRNAs), and six long non-coding RNAs (lncRNAs) were identified as targeted biomarkers. Five drugs-methotrexate, adefovir, furosemide, azathioprine, and cefmetazole-were also identified as targeted biomarkers. Notably, ABCC4 interacted with all five drugs and exhibited the strongest binding energy with methotrexate. The results of the experiments were consistent with those obtained from the bioinformatics analysis.
This study identified five biomarkers-ABCC4, S100A8, VAMP2, PIM2, and ISG20-and offered new insights into potential therapeutic strategies for RA.
类风湿关节炎（RA）是一种自身免疫性炎症性疾病。端粒参与RA发病的机制尚不清楚。本研究旨在探讨RA与端粒之间的关系。
在本研究中，我们通过分析公共数据库中的转录组数据，确定了RA样本与对照样本之间的差异表达基因（DEG）。通过DEG与端粒相关基因的交集确定候选基因。随后，使用机器学习算法、受试者工作特征分析以及RA样本与对照样本之间的表达水平比较来鉴定生物标志物。此外，采用列线图模型预测生物标志物对RA的诊断能力。随后，使用基因集富集分析（GSEA）、亚细胞定位、染色体定位、免疫浸润、功能分析、分子调控网络、药物预测和分子对接等方法进一步探索这些生物标志物在RA中的潜在机制。此外，通过实验验证了RA样本与对照样本之间生物标志物的表达。
ABCC4、S100A8、VAMP2、PIM2和ISG20被鉴定为生物标志物。这些生物标志物通过列线图显示出对RA优异的诊断能力。发现大多数生物标志物在与同种异体移植排斥和细胞周期相关的过程中富集。亚细胞和染色体定位分析表明，ABCC4定位于质膜，ISG20定位于线粒体，PIM2和S100A8定位于细胞质，VAMP2定位于细胞核。此外，在RA样本与对照样本之间鉴定出9种差异免疫细胞，生物标志物与活化的CD4记忆T细胞之间存在强相关性。S100A8、PIM2和VAMP2与其他生物标志物表现出高度相似性。此外，还鉴定出3种转录因子（TF）、121种微小RNA（miRNA）和6种长链非编码RNA（lncRNA）作为靶向生物标志物。还确定了5种药物——甲氨蝶呤、阿德福韦、呋塞米、硫唑嘌呤和头孢美唑——作为靶向生物标志物。值得注意的是，ABCC4与所有5种药物相互作用，并且与甲氨蝶呤表现出最强的结合能。实验结果与生物信息学分析结果一致。
本研究鉴定出5种生物标志物——ABCC4、S100A8、VAMP2、PIM2和ISG20——并为RA的潜在治疗策略提供了新的见解。
