Dataset Information

Identifying a possible new target for diagnosis and treatment of postmenopausal osteoporosis through bioinformatics and clinical sample analysis.

ABSTRACT:

Background

Postmenopausal osteoporosis, a common yet chronic systemic metabolic disease, has become a major public health problem due to life expectancy increasing around the world. The differentiation of mesenchymal stem cells (MSCs) into osteoblasts, and the differentiation of circulating monocyte cells into osteoclasts, play an important role in the balance of bone metabolism. However, when both undergo pathological changes, it can lead to abnormalities, resulting in osteoporosis. This study aims to explore a new biomarker for postmenopausal osteoporosis, thereby providing a new entry point for bioinformatic research into the clinical diagnosis and treatment of the disease.

Methods

Using the Gene Expression Omnibus (GEO) database, microarray analysis was conducted to identify differentially expressed genes in MSCs and monocytes in both postmenopausal osteoporosis patients and a healthy control group. The Database for Annotation, Visualization and Integrated Discovery (DAVID) database was used to analyze the function and enrichment of the selected genes, and a protein-protein interaction (PPI) network was constructed from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website and displayed in Cytoscape. To achieve the final results, module analysis of the PPI network was performed by using Molecular Complex Detection (MCODE).

Results

We identified 45 high-expression and 26 low-expression genes through the study, all of which underwent pathway enrichment analysis. This enrichment was observed in the cell cycle regulation, osteoclast differentiation, tumor necrosis factor (TNF) signaling pathway, and RNA transport. The top 10 hub genes of the PPI network were SF3B1, SRSF5, FUBP1, SRSF3, TIA1, KHSRP, LUC7L3, PNN, SRC, and ATRX. Comparing the MSCs and monocytes between the postmenopausal osteoporosis patients and the healthy control group, we noted that the expression of the above genes differed greatly.

Conclusions

Through bioinformatic analysis and clinical specimen validation, our study provides a new way for exploring the pathogenesis of postmenopausal osteoporosis. Most importantly, it suggests that the hub genes, SF3B1, SRSF5, FUBP1, KHSRP, and SRC, may become new diagnostic markers and therapeutic targets for diagnosing and treating postmenopausal osteoporosis in the future.

SUBMITTER: Liu T

PROVIDER: S-EPMC8350639 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:ObjectivePostmenopausal osteoporosis (PMOP) is a common systemic metabolic bone disorder that is owing to the reduced estrogen secretion and imbalance of bone absorption and bone formation in postmenopausal women. Ferroptosis has been identified as a novel modulatory mechanism of osteoporosis. Nevertheless, the particular modulatory mechanism between ferroptosis and PMOP is still unclear. The objective of the current investigation was to detect potential biomarkers connected to ferroptosis in PMOP and discover its probable mechanism through bioinformatics.MethodsWe downloaded PMOP-related microarray datasets from the database of Gene Expression Omnibus (GEO) and obtained the differentially expressed genes (DEGs). Utilizing bioinformatics analysis, the DEGs were intersected with the ferroptosis dataset to obtain ferroptosis-connected mRNAs. Enrichment analysis employing KOBAS 3.0 was conducted to comprehend the biological functions and enrichment pathways of the DEGs. The generation of the protein-protein interaction (PPI) network was conducted with the aim of identifying central genes. Lastly, the coexpression and competitive endogenous RNA (ceRNA) networks were built using Cytoscape. With the help of external datasets GSE56815 to verify the reliability of the hub genes by plotting ROC curves.ResultsWe identified 178 DE microRNAs (miRNAs), 138 DE circular RNAs (circRNAs), and 86 ferroptosis-related mRNAs. Enrichment analysis exhibited that mRNAs were primarily connected with the signaling pathways of PI3K/Akt, metabolism, mTOR, FoxO, HIF-1, AMPK, MAPK, ferroptosis, VEGF, and NOD-like receptors. Generation of the PPI network detected eight hub genes. The circRNA/miR-23b-3p/PTEN axis may relieve PMOP by inhibiting ferroptosis through targeting the pathway of PI3K/Akt signaling, which is a vital modulatory pathway for PMOP progression. Moreover, the ROC curves ultimately indicates that the four hub genes have greater diagnostic importance in PMOP samples in contrast to the normal group samples, which may be possible markers for PMOP diagnosis.ConclusionsBioinformatics analysis identified four hub genes, namely, PTEN, SIRT1, VEGFA, and KRAS, as potential biomarkers for PMOP diagnosis and management. Moreover, the circRNA/miR-23b-3p/PTEN axis may relieve PMOP by suppressing ferroptosis through targeting the pathway of PI3K/Akt signaling, providing a new avenue to explore the pathogenesis of PMOP.

Project description:BackgroundPostmenopausal osteoporosis (PMOP) is a common condition that leads to a loss of bone density and an increased risk of fractures in women. Recent evidence suggests that exosomal miRNAs are involved in regulating bone development and osteogenesis. However, exosomal miRNAs as biomarkers for PMOP diagnosis have not been systematically evaluated. In this study, we aim to identify PMOP-associated circulating exosomal miRNAs and evaluate their diagnostic performance.MethodsWe performed next-generation sequencing and bioinformatics analysis of plasma exosomal miRNAs from 12 PMOP patients and 12 non-osteoporosis controls to identify PMOP-associated exosomal miRNAs, and then validated them in an independent natural community cohort with 26 PMOP patients and 21 non-osteoporosis controls. Exosomes were isolated with the size exclusion chromatography method from the plasma of elder postmenopausal women. The plasma exosomal miRNA profiles were characterized in PMOP paired with controls with next-generation sequencing. Potential plasma exosomal miRNAs were validated by qRT-PCR in the validation cohort, and their performance in diagnosing PMOP was systematically evaluated with the receiver operating characteristic curve.ResultsTwenty-seven miRNAs were identified as differentially expressed in PMOP versus controls in sequencing data, of which six exosomal miRNAs (miR-196-5p, miR-224-5p, miR320d, miR-34a-5p, miR-9-5p, and miR-98-5p) were confirmed to be differentially expressed in PMOP patients by qRT-PCR in the validation cohort. The three miRNAs combination (miR-34a-5p + miR-9-5p + miR-98-5p) demonstrated the best diagnostic performance, with an AUC = 0.734. In addition, the number of pregnancies was found to be an independent risk factor that can improve the performance of exosomal miRNAs in diagnosing PMOP.ConclusionsThese results suggested that the plasma exosomal miRNAs had the potential to serve as noninvasive diagnostic biomarkers for PMOP.

Project description:Postmenopausal osteoporosis (PMOP), as well as its associated increased risk for fragility fracture, is one of the most disabling consequences of aging in women. This present study aimed to identify candidate genes that involve pathogenesis of PMOP and the therapeutic mechanism of Liuweidihuang (LWDH) pills on PMOP. We integrated microarray datasets of PMOP derived from the Gene Expression Omnibus (GEO) to screen differentially expressed genes (DEGs) between PMOP and normal controls as well as patients with PMOP and patients after treatment of LWDH pills. GO and KEGG enrichment analysis for DEGs were performed. The shared DEGs, associated with both the pathogenesis of PMOP and the therapeutic mechanism of LWDH, were further analyzed by protein-protein interaction (PPI) network. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the DEGs obtained by our integrated analysis. Compared with normal controls, 1732 DEGs in PMOP were obtained with p<0.05. According to the qRT-PCR results, expression of ATF2, FBXW7, RDX, and RBBP4 was consistent with that in our integrated analysis, generally. GO and KEGG enrichment analysis showed that those DEGs were significantly enriched in regulation of transcription, DNA-dependent, cytoplasm, protein binding, and MAPK signaling pathway. A total of 58 shared DEGs in PMOP versus normal control and in patients with PMOP versus patients after LWDH treatment were identified, which had opposite expression trend in these two comparisons. In the PPI network, CSNK2A1, ATF2, and FBXW7 were three hub proteins. Three genes including ATF2, FBXW7, and RDX were speculated to be therapeutic targets of LWDH for PMOP based on BATMAN-TCM database. We speculated that three genes of ATF2, FBXW7, and RDX may play crucial roles in both pathogenesis of PMOP and therapeutic mechanism of LWDH on PMOP. Our results may provide clues for the molecular pathogenesis of PMOP and offer new possibilities for treatment of PMOP.

Project description:ObjectivePostmenopausal osteoporosis (PMOP) is one of the most commonly occurring conditions worldwide and is characterized by estrogen deficiency as well as persistent calcium loss with age. The aim of our study was to identify significant ferroptosis-associated biomarkers for PMOP.Methods and materialsWe obtained our training dataset from the Gene Expression Omnibus (GEO) database using GSE56815 expression profiling data. Meanwhile, we extracted ferroptosis-associated genes for further analysis. Differentially expressed ferroptosis-associated genes (DEFAGs) between OP patients and normal controls were selected using the "limma" package. We established a ferroptosis-associated gene signature using training models, specifically, random forest (RF) and support vector machine (SVM) models. It was further validated in another dataset (GSE56814) which also showed a high AUC: 0.98, indicating high diagnostic value. Using consensus clustering, the OP patient subtypes were identified. A ferroptosis associated gene (FAG)-Scoring scheme was developed by PCA. The important candidate genes associated with OP were also compared between different ferrclusters and geneclusters.ResultsThere were significant DEFAGs acquired, of which five (HMOX1, HAMP, LPIN1, MAP3K5, FLT3) were selected for establishing a ferroptosis-associated gene signature. Analyzed from the ROC curve, our established RF model had a higher AUC value than the SVM model (RF model AUC:1.00). Considering these results, the established RF model was chosen to be the most appropriate training model. Later, based on the expression levels of the five DEFAGs, a clinical application nomogram was established. The OP patients were divided into two subtypes (ferrcluster A, B and genecluster A, B, respectively) according to the consensus clustering method based on DEFAGs and differentially expressed genes (DEGs). Ferrcluster B and genecluster B had higher ferroptosis score than ferrcluster A and genecluster A, respectively. The expression of COL1A1 gene was significantly higher in ferrcluster B and gencluster B compared with ferrcluster A and gencluster A, respectively, while there is no statistical difference in term of VDR gene, COL1A2 genes, and PTH gene expressions between ferrcluster A and B, together with gencluster A and B.ConclusionsOn the basis of five explanatory variables (HMOX1, HAMP, LPIN1, MAP3K5 and FLT3), we developed a diagnostic ferroptosis-associated gene signature and identified two differently categorized OP subtypes that may potentially be applied for the early diagnosis and individualized treatment of PMOP. The ER gene, VDR gene, IL-6 gene, COL1A1 and COL1A2 genes, and PTH gene are important candidate gene of OP, however, more studies are still anticipated to further elucidate the relationship between these genes and ferroptosis in OP.

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.