Dataset Information

Identification of the transcriptome signatures and immune-inflammatory responses in postmenopausal osteoporosis.

ABSTRACT: Postmenopausal osteoporosis is the most common type of osteoporosis in women. To date, little is known about their transcriptome signatures, although biomarkers from peripheral blood mononuclear cells are attractive for postmenopausal osteoporosis diagnoses. Here, we performed bulk RNA sequencing of 206 samples (124 postmenopausal osteoporosis and 82 normal samples) and described the clinical phenotypic characteristics of postmenopausal women. We then highlighted the gene set enrichment analyses between the extreme T-score group and the heathy control group, revealing that some immune-inflammatory responses were enhanced in postmenopausal osteoporosis, with representative pathways including the mitogen-activated protein kinase (NES = 1.6, FDR <0.11) pathway and B_CELL_RECEPTOR (NES = 1.69, FDR <0.15) pathway. Finally, we developed a combined risk prediction model based on lasso-logistic regression to predict postmenopausal osteoporosis, which combined eleven genes (PTGS2, CXCL16, NECAP1, RPS23, SSR3, CD74, IL4R, BTBD2, PIGS, LILRA2, MAP3K11) and three pieces of clinical information (age, procollagen I N-terminal propeptide, β isomer of C-terminal telopeptide of type I) and provided the best prediction ability (AUC = 0.97). Taken together, this study filled a gap in the large-scale transcriptome signature profiles and revealed the close relationship between immune-inflammatory responses and postmenopausal osteoporosis, providing a unique perspective for understanding the occurrence and development of postmenopausal osteoporosis.

SUBMITTER: Gao P

PROVIDER: S-EPMC10770509 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Identification of the transcriptome signatures and immune-inflammatory responses in postmenopausal osteoporosis.

Gao Pan P Pan Xiaoguang X Wang Shang S Guo Sijia S Dong Zhanying Z Wang Zhefeng Z Liang Xue X Chen Yan Y Fang Fang F Yang Ling L Huang Jinrong J Zhang Chenxi C Li Conghui C Luo Yonglun Y Peng Songlin S Xu Fengping F

Heliyon 20231212 1

Postmenopausal osteoporosis is the most common type of osteoporosis in women. To date, little is known about their transcriptome signatures, although biomarkers from peripheral blood mononuclear cells are attractive for postmenopausal osteoporosis diagnoses. Here, we performed bulk RNA sequencing of 206 samples (124 postmenopausal osteoporosis and 82 normal samples) and described the clinical phenotypic characteristics of postmenopausal women. We then highlighted the gene set enrichment analyses ...[more]

PMID: 38187229

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Project description:Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarray analyses of the Gene Expression Omnibus (GEO) database, and the optimal diagnostic gene biomarkers for PMOP were identified with LASSO and Boruta algorithms. Classification models, including support vector machine (SVM), decision tree and random forests models, were established to test the diagnostic value of identified gene biomarkers for PMOP. Functional annotations and protein‑protein interaction (PPI) network constructions were also conducted. Integrated microarray analyses (GSE56815, GSE13850 and GSE7429) of the GEO database were employed, and 1,320 DEGs were identified between PMOP and normal controls. An 11‑gene combination was also identified as an optimal biomarker for PMOP by feature selection and classification methods using SVM, decision tree and random forest models. This combination was comprised of the following genes: Dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein‑coupled receptor 116 (GPR116), BCL2 interacting killer, adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), GPR87, cylicin 2, EF‑hand calcium binding domain 1 and DEAH‑box helicase 35. RBBP4 (degree=12) was revealed to be the hub gene of this PMOP‑specific PPI network. Among these 11 genes, three genes (OSTF1, ADRB1 and NEO1) were speculated to serve roles in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may be involved in PMOP by regulating the nuclear factor‑κB signaling pathway. Furthermore, DHTKD1 and RBBP4 may be involved in PMOP by regulating mitochondrial dysfunction and interacting with ESR1, respectively. In conclusion, the findings of the current study provided an insight for exploring the mechanism and developing novel biomarkers for PMOP. Further studies are required to test the diagnostic value for PMOP prior to use in a clinical setting.

Project description:BackgroundPostmenopausal osteoporosis is a systemic metabolic disorder typified by an imbalance in bone turnover, where bone resorption supersedes bone formation. This imbalance primarily arises from a decline in bone mass induced by estrogen deficiency, and an elevated risk of fractures resulting from degradation of bone microstructure. Despite recognizing these changes, the precise causative factors and potential molecular pathways remain elusive. In this study, we aimed to identify differentially expressed genes (DEGs), associated pathways, and the role of immune infiltration in osteoporosis, leveraging an integrated bioinformatics approach to shed light on potential underlying molecular mechanisms.MethodsWe retrieved the expression profiles of GSE230665 from the Gene Expression Omnibus database, comprising 15 femur samples, including 12 postmenopausal osteoporosis samples and 3 normal controls. From the aggregated microarray datasets, we derived differentially expressed genes (DEGs) for further bioinformatics analysis. We used WGCNA, analyzed DEGs, PPI, and conducted GO analysis to identify pivotal genes. We then used the CIBERSORT method to explore the degree of immune cell infiltration within femur specimens affected by postmenopausal osteoporosis. To probe into the relationship between pivotal genes and infiltrating immune cells, we conducted correlation analysis.ResultsWe identified a total of 12,204 DEGs. Among these, 12,157 were up-regulated, and 47 were down-regulated. GO and KEGG pathway analyses indicated that these DEGs predominantly targeted cellular protein localization activity and associated signaling pathways. The protein-protein interaction network highlighted four central hub-genes: RPL31, RPL34, EEF1G, and BPTF. Principal component analysis indicated a positive correlation between the expression of these genes and resting NK cells (as per CIBERSORT). In contrast, the expression of RPL31, RPL34, and EEF1G showed a negative correlation with T cells (gamma delta per CIBERSORT).ConclusionsImmune infiltration plays a role in the development of osteoporosis.

Dataset Information

Identification of the transcriptome signatures and immune-inflammatory responses in postmenopausal osteoporosis.

Publications

Identification of the transcriptome signatures and immune-inflammatory responses in postmenopausal osteoporosis.

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