生物信息学结合机器学习和单细胞测序分析以识别类风湿性关节炎和缺血性心力衰竭的共同机制及生物标志物。

Bioinformatics combining machine learning and single-cell sequencing analysis to identify common mechanisms and biomarkers of rheumatoid arthritis and ischemic heart failure.

作者信息

Sun Ziyi, Lin Jianguo, Sun Xiaoning, Yun Zhangjun, Zhang Xiaoxiao, Xu Siyu, Duan Jinlong, Yao Kuiwu

机构信息

Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No.5, Beixiangge, Xicheng District, Beijing, 100053, People's Republic of China.

Graduate School, Beijing University of Chinese Medicine, No.11 Beisanhuan East Road, Chaoyang District, Beijing, 100029, People's Republic of China.

出版信息

Heliyon. 2025 Jan 9;11(2):e41641. doi: 10.1016/j.heliyon.2025.e41641. eCollection 2025 Jan 30.

Abstract

Patients with rheumatoid arthritis (RA) have an increased risk of ischemic heart failure (IHF), but the shared mechanisms are unclear. This study analyzed RNA sequencing data from five RA and IHF datasets to identify common biological mechanisms and significant biomarkers. One hundred and seventy-seven common differentially expressed genes (CDEGs) were identified, with enrichment analysis highlighting pathways related to sarcomere organization, ventricular myocardial tissue morphogenesis, chondrocyte differentiation, prolactin signaling, hematopoietic cell lineage, and protein methyltransferases. Five hub genes (CD2, CD3D, CCL5, IL7R, and SPATA18) were identified through protein-protein interaction (PPI) network analysis and machine learning. Co-expression and immune cell infiltration analyses underscored the importance of the inflammatory immune response, with hub genes showing significant correlations with plasma cells, activated CD4 T memory cells, monocytes, and T regulatory cells. Single-cell RNA sequencing (scRNA-seq) confirmed hub gene expression primarily in T cells, activated T cells, monocytes, and NK cells. The findings underscore the critical roles of sarcomere organization, prolactin signaling, protein methyltransferase activity, and immune responses in the progression of IHF in RA patients. These insights not only identify valuable biomarkers and therapeutic targets but also offer promising directions for early diagnosis, personalized treatments, and preventive strategies for IHF in the context of RA. Moreover, the results highlight opportunities for repurposing existing drugs and developing new therapeutic interventions, which could reduce the risk of IHF in RA patients and improve their overall prognosis.

摘要

类风湿性关节炎(RA)患者发生缺血性心力衰竭(IHF)的风险增加,但其共同机制尚不清楚。本研究分析了来自五个RA和IHF数据集的RNA测序数据,以确定常见的生物学机制和重要的生物标志物。共鉴定出177个共同差异表达基因(CDEG),富集分析突出了与肌节组织、心室心肌组织形态发生、软骨细胞分化、催乳素信号传导、造血细胞谱系和蛋白质甲基转移酶相关的途径。通过蛋白质-蛋白质相互作用(PPI)网络分析和机器学习确定了五个枢纽基因(CD2、CD3D、CCL5、IL7R和SPATA18)。共表达和免疫细胞浸润分析强调了炎症免疫反应的重要性,枢纽基因与浆细胞、活化的CD4 T记忆细胞、单核细胞和调节性T细胞显示出显著相关性。单细胞RNA测序(scRNA-seq)证实枢纽基因主要在T细胞、活化T细胞、单核细胞和NK细胞中表达。这些发现强调了肌节组织、催乳素信号传导、蛋白质甲基转移酶活性和免疫反应在RA患者IHF进展中的关键作用。这些见解不仅确定了有价值的生物标志物和治疗靶点,还为RA背景下IHF的早期诊断、个性化治疗和预防策略提供了有前景的方向。此外,结果突出了现有药物重新利用和开发新治疗干预措施的机会,这可以降低RA患者发生IHF的风险并改善其总体预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/872a/11783397/6f7a897cf43e/gr1.jpg

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