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

被引:0
|
作者
Fang, Shenyun [1 ,3 ]
Ni, Haonan [1 ]
Zhang, Qianghua [1 ,3 ]
Dai, Jilin [1 ,3 ]
He, Shouyu [1 ,3 ]
Min, Jikang [1 ,3 ]
Zhang, Weili [2 ]
Li, Haidong [1 ]
机构
[1] Huzhou Univ, Peoples Hosp Huzhou 1, Affiliated Hosp 1, Dept Orthoped Surg, Huzhou 313000, Peoples R China
[2] Huzhou Univ, Peoples Hosp Huzhou 1, Affiliated Hosp 1, Dept Ophthalmol, Huzhou 313000, Peoples R China
[3] Huzhou Key Lab Early Diag & Treatment Osteoarthrit, Huzhou 313000, Peoples R China
关键词
Postmenopausal osteoporosis; Immune; Diagnosis; Molecular subtype; Biomarkers; BONE; PREVALENCE; IDENTIFICATION; DENSITY; TOOL; JUN;
D O I
10.1016/j.heliyon.2024.e38022
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:20
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