机器学习CIBERSORT算法下类风湿关节炎相关间质性肺病滑膜组织的免疫渗透和Sonic Hedgehog表达机制研究

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Qunqun Lu, Yizhen Jiang, Xiaofeng Cang, Jiaojiao Pan, Xiaowen Shen, Ruoyu Tang, Zhe Zhou, Yiwen Zhu
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引用次数: 0

摘要

类风湿性关节炎相关间质性肺病(RA-ILD)是类风湿性关节炎患者常见的并发症之一,影响患者的生活质量。CIBERSORT算法被广泛用于确定病变组织中免疫细胞(IC)的比例,而音速刺猬(Shh)信号通路作为一种必要的调节因子,在RA-ILD的病理学中也备受关注。这项工作旨在基于CIBERSORT算法探讨RA-ILD免疫浸润和滑膜组织(ST)Shh表达的机制。利用R语言对RA-ILD的差异基因进行通路富集分析。利用基于机器学习的 CIBERSORT 算法分析了 RA-ILD 肺组织中 22 种 IC 的含量和比例。同时,免疫印迹法检测和分析了RA-ILD组和Ctrl组(无ILD的RA患者)ST样本中Shh、Smoothened(Smo)和骨形态发生蛋白(BMPs)的表达。与 RA-ILD 相关的蛋白质网络中的中心靶基因包括:BSG、CCL2、CTLA4、FGFBP1、GLI1、HHIP、HLA-DRB1、IFNAR1、IL17A、IL23A、IL-6、INPP4A、LILRB1、MUC5B、PADI4、PPM1A、PTCH1、PTPN22、RSPO4、Shh、SMo、BMPs 和 BMPs、RSPO4、Shh、SMO、STAT4、SUFU、TAOK2、TIMP2 和 TWSG1,它们参与多种通路,如 B 细胞调控、Shh 通路的转录因子和 ST 免疫耐受相关通路。在使用 CIBERSORT 算法对 RA-ILD 进行免疫学分析时,发现 HLA(r = - 0.26)、PTPN22(r = - 0.36)、STAT4(r = - 0.18)、IL-6(r = - 0.17)、CTLA4(r = - 0.27)和 PADI4(r = - 0.21)均与 CD4+T 细胞呈负相关(P<0.05)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Study of the Immune Infiltration and Sonic Hedgehog Expression Mechanism in Synovial Tissue of Rheumatoid Arthritis-Related Interstitial Lung Disease under Machine Learning CIBERSORT Algorithm.

Study of the Immune Infiltration and Sonic Hedgehog Expression Mechanism in Synovial Tissue of Rheumatoid Arthritis-Related Interstitial Lung Disease under Machine Learning CIBERSORT Algorithm.

Rheumatoid arthritis-related interstitial lung disease (RA-ILD) is one of the common complications in patients with RA, which affects their quality of life. The CIBERSORT algorithm is widely employed to determine the proportion of immune cells (ICs) in diseased tissues, while the Sonic Hedgehog (Shh) signaling pathway, as an imperative regulatory factor, has also attracted attention in the pathology of RA-ILD. This work was to explore the mechanisms of RA-ILD immune infiltration and synovial tissue (ST) Shh expression based on the CIBERSORT algorithm. The differential genes of RA-ILD were subjected to pathway enrichment analysis using R language. The content and proportion of 22 types of ICs in RA-ILD lung tissues were analyzed using machine learning-based CIBERSORT algorithm. Meanwhile, immunoblotting was employed to detect and analyze the expression of Shh, Smoothened (Smo), and bone morphogenetic proteins (BMPs) proteins in ST samples from RA-ILD and Ctrl groups (RA patients without ILD). The hub target genes in the protein network associated with RA-ILD include BSG, CCL2, CTLA4, FGFBP1, GLI1, HHIP, HLA-DRB1, IFNAR1, IL17A, IL23A, IL-6, INPP4A, LILRB1, MUC5B, PADI4, PPM1A, PTCH1, PTPN22, RSPO4, Shh, SMO, STAT4, SUFU, TAOK2, TIMP2, and TWSG1, which are involved in multiple pathways, such as B cell regulation, transcription factors of the Shh pathway, and ST immune tolerance-related pathways. In the immunological analysis of RA-ILD using the CIBERSORT algorithm, HLA (r = - 0.26), PTPN22 (r = - 0.36), STAT4 (r = - 0.18), IL-6 (r = - 0.17), CTLA4 (r = - 0.27), and PADI4 (r = - 0.21) were all found to exhibit negative correlations with CD4+T cells (P < 0.05). Monocytes were found to be more abundant in RA-ILD patients' serum versus the Ctrl group. Shh, Smo, and BMP expressions were drastically lower in the RA-ILD group versus Ctrl group (P < 0.05). Significant immune cell infiltration was observed in the lung tissues of RA-ILD patients. Further analysis utilizing the CIBERSORT algorithm revealed alterations in the proportions of different IC subtypes, indicating their association with disease severity and prognosis. Moreover, there was a significant decrease in the expression levels of Shh, Smo, and BMP. These findings underscore the importance of immune cells in the pathophysiology of RA-ILD and suggest a potential involvement of the Shh signaling pathway in the pathogenesis of RA-ILD.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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