Suppr超能文献

整合单细胞和批量RNA测序分析揭示绝经后骨质疏松症中的免疫相关生物标志物。

Integrated single-cell and bulk RNA sequencing analysis reveal immune-related biomarkers in postmenopausal osteoporosis.

作者信息

Fang Shenyun, Ni Haonan, Zhang Qianghua, Dai Jilin, He Shouyu, Min Jikang, Zhang Weili, Li Haidong

机构信息

Department of Orthopedic Surgery, First People's Hospital of Huzhou, The First affiliated Hospital of Huzhou University, Huzhou, 313000, China.

Huzhou Key Laboratory for Early Diagnosis and Treatment of Osteoarthritis, Huzhou, 313000, China.

出版信息

Heliyon. 2024 Sep 17;10(18):e38022. doi: 10.1016/j.heliyon.2024.e38022. eCollection 2024 Sep 30.

Abstract

BACKGROUND

Postmenopausal osteoporosis (PMOP) represents as a significant health concern, particularly as the population ages. Currently, there is a paucity of comprehensive descriptions regarding the immunoregulatory mechanisms and early diagnostic biomarkers associated with PMOP. This study aims to examine immune-related differentially expressed genes (IR-DEGs) in the peripheral blood mononuclear cells of PMOP patients to identify immunological patterns and diagnostic biomarkers.

METHODS

The GSE56815 dataset from the Gene Expression Omnibus (GEO) database was used as the training group, while the GSE2208 dataset served as the validation group. Initially, differential expression analysis was conducted after data integration to identify IR-DEGs in the peripheral blood mononuclear cells of PMOP. Subsequently, feature selection of these IR-DEGs was performed using RF, SVM-RFE, and LASSO regression models. Additionally, the expression of IR-DEGs in distinct bone marrow cell subtypes was analyzed using single-cell RNA sequencing (scRNA-seq) datasets, allowing the identification of cellular communication patterns within various cell subgroups. Finally, molecular subtypes and diagnostic models for PMOP were constructed based on these selected IR-DEGs. Furthermore, the expression levels of characteristic IR-DEGs were examined in rat osteoporosis (OP) models.

RESULTS

Using machine learning, six IR-DEGs (JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5) were identified. Subsequently, two molecular subtypes of PMOP (subtype 1 and subtype 2) were established, with subtype 1 exhibiting a higher proportion of M1 macrophage infiltration. Analysis of the scRNA-seq dataset revealed 11 distinct cell clusters. It was noted that JUN was significantly overexpressed in M1 macrophages, while HMOX1 showed a marked elevation in endothelial cells and M2 macrophages. Cell communication results suggested that the PMOP microenvironment features increased interactions among M2 macrophages, CD8 T cells, Tregs, and fibroblasts. The diagnostic model based on these six IR-DEGs demonstrated excellent diagnostic performance (AUC = 0.927). In the OP rat model, the expression of IL1R2 and TNFSF8 were significantly elevated.

CONCLUSION

JUN, HMOX1, CYSLTR2, TNFSF8, IL1R2, and SSTR5 may serve as promising molecular targets for diagnosing and subtyping patients with PMOP. These results offer novel perspectives on the early diagnosis of PMOP and the advancement of personalized immune-based therapies.

摘要

背景

绝经后骨质疏松症(PMOP)是一个重大的健康问题,尤其是随着人口老龄化。目前,关于与PMOP相关的免疫调节机制和早期诊断生物标志物的全面描述较少。本研究旨在检测PMOP患者外周血单个核细胞中免疫相关差异表达基因(IR-DEGs),以识别免疫模式和诊断生物标志物。

方法

将来自基因表达综合数据库(GEO)的GSE56815数据集用作训练组,GSE2208数据集用作验证组。首先,在数据整合后进行差异表达分析,以识别PMOP患者外周血单个核细胞中的IR-DEGs。随后,使用随机森林(RF)、支持向量机递归特征消除(SVM-RFE)和套索回归模型对这些IR-DEGs进行特征选择。此外,使用单细胞RNA测序(scRNA-seq)数据集分析IR-DEGs在不同骨髓细胞亚群中的表达,从而识别各种细胞亚群内的细胞通讯模式。最后,基于这些选定的IR-DEGs构建PMOP的分子亚型和诊断模型。此外,在大鼠骨质疏松症(OP)模型中检测特征性IR-DEGs的表达水平。

结果

通过机器学习,鉴定出6个IR-DEGs(JUN、血红素加氧酶1(HMOX1)、半胱氨酰白三烯受体2(CYSLTR2)、肿瘤坏死因子配体超家族成员8(TNFSF8)、白细胞介素1受体2(IL1R2)和促生长抑素受体5(SSTR5))。随后,建立了PMOP的两种分子亚型(亚型1和亚型2),亚型1表现出更高比例的M1巨噬细胞浸润。对scRNA-seq数据集的分析揭示了11个不同的细胞簇。值得注意的是,JUN在M1巨噬细胞中显著过表达,而HMOX1在内皮细胞和M2巨噬细胞中显著升高。细胞通讯结果表明,PMOP微环境的特征是M2巨噬细胞、CD8 T细胞、调节性T细胞(Tregs)和成纤维细胞之间的相互作用增加。基于这6个IR-DEGs的诊断模型表现出优异的诊断性能(曲线下面积(AUC)=0.927)。在OP大鼠模型中,IL1R2和TNFSF8的表达显著升高。

结论

JUN、HMOX1、CYSLTR2、TNFSF8、IL1R2和SSTR5可能是诊断PMOP患者并对其进行亚型分类的有前景的分子靶点。这些结果为PMOP的早期诊断和基于个性化免疫的治疗进展提供了新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/932f/11425179/fa56090fa177/gr1.jpg

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验