Analysis and Validation of Hub Genes in Blood Monocytes of Postmenopausal Osteoporosis Patients

Studies on the Role of MAP4K2, SPI1, and CTSD in Osteoporosis

Osteoporosis (OP) is a prevalent skeletal disorder characterized by an imbalance between bone resorption and bone formation, resulting in a significant global burden. Previous research utilizing bioinformatics analysis has identified MAP4K2, SPI1, and CTSD as hub genes associated with OP. In this current investigation, we have successfully established a differential expression system of MAP4K2, SPI1, and CTSD in rat bone marrow mesenchymal stem cells (BMSCs) through transfection techniques. Additionally, the CCK-8 assay was employed to assess cell proliferation, while the alkaline phosphatase (ALP) activity assay and ALP staining assay were utilized to evaluate osteogenic differentiation. Alizarin red staining was employed to detect mineralization of BMSCs. Furthermore, the expression of relevant genes and molecules associated with the MAPK signaling pathway, autophagy, and apoptosis in the sera of rat BMSCs were examined using quantitative real-time polymerase chain reaction (qRT-PCR). The purpose of this study was to preliminarily investigate whether MAP4K2, SPI1, and CTSD have an effect on the osteogenic capacity of rat BMSCs and whether these genes, when differentially expressed, affect the expression of related genes in the MAPK, autophagy, and apoptosis signaling pathways and thus the osteogenic function of BMSCs. In summary, the findings of this study indicate that MAP4K2 and CTSD exert significant influence on the proliferation, osteogenic differentiation, and mineralization processes of rat BMSCs cells. Furthermore, these proteins may contribute to the development of OP through their involvement in the regulation of autophagy and apoptosis. Conversely, our investigation did not reveal any discernible impact of SPI1 on OP-related phenotypes. Consequently, this research serves as a fundamental basis for further exploration of potential therapeutic targets for the treatment of OP.

The Nexus Between Traditional Chinese Medicine and Immunoporosis: Implications in the Treatment and Management of Osteoporosis

Osteoporosis (OP) is a globally prevalent bone disease characterized by reduced bone mass and heightened fracture risk, posing a significant health and economic challenge to aging societies worldwide. Osteoimmunology-an emerging field of study-investigates the intricate relationship between the skeletal and the immune systems, providing insights into the immune system's impact on bone health and disease progression. Recent research has demonstrated the essential roles played by various immune cells (T cells, B cells, macrophages, dendritic cells, mast cells, granulocytes, and innate lymphoid cells) in regulating bone metabolism, homeostasis, formation, and remodeling through interactions with osteoclasts (OC) and osteoblasts (OB). These findings underscore that osteoimmunology provides an essential theoretical framework for understanding the pathogenesis of various skeletal disorders, including OP. Traditional Chinese medicine (TCM) and its active ingredients have significant clinical value in OP treatment. Unfortunately, despite their striking multieffect pathways in the pharmacological field, current research has not yet summarized them in a comprehensive and detailed manner with respect to their interventional roles in immune bone diseases, especially OP. Consequently, this review addresses recent studies on the mechanisms by which immune cells and their communication molecules contribute to OP development. Additionally, it explores the potential therapeutic benefits of TCM and its active components in treating OP from the perspective of osteoimmunology. The objective is to provide a comprehensive framework that enhances the understanding of the therapeutic mechanisms of TCM in treating immune-related bone diseases and to facilitate the development of novel therapeutic strategies.

A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults

BACKGROUND

Osteoporosis has become a significant public health concern that necessitates the application of appropriate techniques to calculate disease risk. Traditional methods, such as logistic regression,have been widely used to identify risk factors and predict disease probability. However,with the advent of advanced statistics techniques,machine learning models offer promising alternatives for improving prediction accuracy. What's more, studies that use risk factors and prediction models for osteoporosis in high-risk groups for cardiovascular diseases are scarce. We aimed to explore the risk factors and disease probability of osteoporosis by comparing logistic regression with four machine learning models. By doing so,we seek to provide insights into the most effective methods for osteoporosis risk assessment and contribute to the development of tailored prevention strategies at high risk of cardiovascular disease among old adults.

METHODS

We carried out a cross-sectional investigation of a high-risk group in cardiovascular patients. A logistic regression model and four common machine learning methods,DT,RF,SVM,and XGBoost were implemented to create a prediction model using information from 211 participants who met the inclusion requirements. Metrics for calibration and discrimination were used to compare the models.

RESULTS

In total,211 patients were enrolled. The AUCs were 0.751 for the logistic regression model,0.72 for the SVM model,0.70 for the random forest model,0.697 for the model XGBoost,and 0.69 for the decision tree model. The logistic regression model outperforms other models for machine learning. According to the logistic regression model,there were nine predictors,including age,sex,glucose,TG (triglyceride),fracture history,stroke history,and CNV (copy number variation) nssv659422, and low-sodium salt. A well-calibrated result of 0.199 on the Brier scale. The findings of the internal validation demonstrated the high degree of repeatability of the prediction model employed in this study.

CONCLUSIONS

In this study, we discovered that when predicting osteoporosis,a number of machine learning techniques fell short of logistic regression. In a specific population, we have innovatively developed a risk prediction model for osteoporosis events that integrates genetic and environmental factors, is an effective tool for assessing osteoporosis risk and can serve as the basis for specialized intervention approaches.

The link between osteoporosis and frozen shoulder: exploring the therapeutic effect of TAK715 on reversing fibrosis and protecting against osteoporosis via the p38 MAPK signaling pathway

BACKGROUND

The global incidence of frozen shoulder (FS) (2% ~ 5%) and osteoporosis (OP) is high (9.1%-12.1%). Clinically, postmenopausal women are particularly at risk for both diseases. The main objective of this current research is to investigate the pathogenesis mechanism of FS and explore the connection between FS and OP.

METHODS

We obtained FS and OP datasets from GEO and identified crosstalk genes. Following KEGG and GO enrich analysis, the p38 MAPK signaling pathway was focused and the specific p38α inhibitor TAK715 was screened out. We conducted flow cytometry, western blot, and PCR analyses to assess the treatment effect of TAK715 on FS synovium fibroblasts at different concentrations. Additionally, we employed SD rats to validate the treatment effects of TAK715 in vivo.

RESULTS

TAK715 was useful in reversing fibrosis at the concentration of 1 μM, 5 μM and 10 μM. The unbalanced apoptosis process in frozen shoulder cell and the activation of osteoclast were inhibited at the concentration of 5 μM by TAK715. Then we successfully established a FS and OP rat model, with the FS with OP rat displaying less range of motion (ROM) and thicker shoulder capsule. In FS rat that was treated with TAK715, the frozen shoulder side was corrected in ROM and bone loss.

CONCLUSIONS

The frozen shoulder with osteoporosis may exhibit more severe symptoms, and TAK715 is effective in protecting fibrosis and osteoporosis both in vitro and vivo. The therapy to correct FS and OP simultaneously by TAK715 provides novel approach in FS treatment and study.

Identification and analysis of biomarkers associated with oxidative stress and ferroptosis in recurrent miscarriage

Recurrent miscarriage (RM) has a huge impact on women. Both oxidative stress and ferroptosis play an important role in the pathogenesis of RM. Hence, it was vital to screen the ferroptosis oxidation-related biomarkers for the diagnosis and treatment of RM. We introduced transcript data to screen out differentially expressed genes (DEGs) in RM. Ferroptosis oxidation-related differentially expressed genes were obtained by overlapping DEGs and oxidative stress related genes with correlations >0.9 with ferroptosis-related genes. Least Absolute Shrinkage and Selectionator operator regression and support vector machine based recursive feature elimination algorithm were implemented to screen feature genes. The biomarkers associated with ferroptosis oxidation were screened via receiver operating characteristic curve analysis. We finally analyzed the competing endogenous RNAs regulatory network and potential drugs of biomarkers. We identified 1047 DEGs in RM. Then, 9 ferroptosis oxidation-related differentially expressed genes were obtained via venn diagram. Subsequently, 8 feature genes (PTPN6, GJA1, HMOX1, CPT1A, CREB3L1, SNCA, EPAS1, and TGM2) were identified via machine learning. Moreover, 4 biomarkers associated with ferroptosis oxidation, including PTPN6, GJA1, CPT1A, and CREB3L1, were screened via receiver operating characteristic curve analysis. We constructed the '227 long noncoding RNAs-4 mRNAs-36 microRNAs' network, in which hsa-miR-635 was associated with CREB3L1 and PTPN6. There were 11 drugs with therapeutic potential on 3 biomarkers associated with ferroptosis oxidation. We also observed higher expression of CPT1A and CREB3L1 in RM group compared to the healthy control group by quantitative real-time reverse transcription polymerase chain reaction. Overall, we obtained 4 biomarkers (PTPN6, GJA1, CPT1A, and CREB3L1) associated with ferroptosis and oxidative stress, which laid a theoretical foundation for the diagnosis and treatment of RM.

Association between daily consumption of spicy food and bone mineral density in middle-aged and older adults: a cross-sectional study

Background: Many studies have reported the effects of spicy food on human health, but no studies have been conducted on the impact of long-term spicy food consumption on bone mineral density (BMD). This study aimed to investigate the impact of daily consumption of spicy food on BMD in the population aged 50 years and older. Methods: This cross-sectional study was conducted from 2020 to 2022 in Jiangxi Province, China. This study investigated the differences in BMD between non-consumers and daily spicy food consumers in adults aged 50-85 years. A multiple linear regression model was used to investigate the association between spicy food consumption and BMD of the total lumbar spine (LS), femoral neck (FN), and total hip, as well as biochemical markers of bone metabolism (BMBM) levels. Results: The results showed that daily consumption of spicy food was negatively associated with total LS BMD (β = -0.013, P = 0.015). Subgroup analyses showed this negative association was more pronounced among smokers and drinkers compared to non-smokers (β: -0.006 vs. -0.042; P for interaction <0.05) and non-drinkers (β: -0.004 vs. -0.037; P for interaction <0.05). In addition, according to the daily frequency of spicy food consumption, the daily spicy food consumers were categorized into one meal per day, two meals per day, and three meals per day groups. Further analysis revealed that the negative association between spicy food and total LS BMD was progressively stronger as the frequency of daily consumption of spicy food increased (P for trend <0.05). For BMBM, daily consumption of spicy food was positively associated with serum PINP levels and negatively associated with serum Ca and serum Mg levels. Conclusions: Our study suggested that daily consumption of spicy food was associated with lower LS BMD in middle-aged and older Chinese adults, and this association was more pronounced in the smoking and drinking populations. The adverse effects of spicy food on LS BMD become progressively stronger with increasing frequency of daily consumption of spicy food. In addition, daily consumption of spicy food was associated with higher PINP levels and lower serum Ca and Mg levels.

YTHDC1 inhibits osteoclast differentiation to alleviate osteoporosis by enhancing PTPN6 messenger RNA stability in an m6A-hUR-dependent manner

YTHDC1 has been confirmed to mediate osteoporosis (OP) progression by regulating osteogenic differentiation. However, whether YTHDC1 mediates osteoclast differentiation and its molecular mechanism remains unclear. Quantitative real-time polymerase chain reaction and Western blot analysis were performed to detect the levels of YTHDC1, PTPN6, NFATc1, TRAP, RUNX2, alkaline phosphatase, and HUR. YTHDC1 knockout mice was constructed by CRISPR/Cas9 system, and the OP mice model was established by ovariectomy. Hematoxylin and eosin staining and micro-computed tomography were used to evaluate bone formation and bone mass. Mouse primary bone marrow macrophage cells were isolated and induced into osteoclasts. TRAP-positive cells were detected using TRAP staining. MeRIP-qPCR, RIP-qPCR assay, RNA affinity isolation assay, and co-immunoprecipitation assay were used to confirm the interactions among YTHDC1, PTPN6, and HUR. YTHDC1 expression was reduced and positively correlated with lumbar bone mineral density in OP patients. In the ovariectomy model of YTHDC1 knockout mice, bone formation was reduced, bone histomorphology was changed, and osteoclastic-related factor (NFATc1 and TRAP) levels were enhanced. Overexpression YTHDC1 inhibited osteoclast differentiation. YTHDC1 increased PTPN6 messenger RNA stability in an m6A-dependent manner. Moreover, YTHDC1 interacted with HUR to positively regulate PTPN6 expression. PTPN6 knockdown promoted osteoclast differentiation, and this effect was reversed by overexpressing HUR or YTHDC1. YTHDC1 was involved in regulating OP progression through inhibiting osteoclast differentiation by enhancing PTPN6 messenger RNA stability in an m6A-HUR-dependent manner.

Enhancing postmenopausal osteoporosis: a study of KLF2 transcription factor secretion and PI3K-Akt signaling pathway activation by PIK3CA in bone marrow mesenchymal stem cells

Introduction

Mesenchymal stem cells can develop into osteoblasts, making them a promising cell-based osteoporosis treatment. Despite their therapeutic potential, their molecular processes are little known. Bioinformatics and experimental analysis were used to determine the molecular processes of bone marrow mesenchymal stem cell (BMSC) therapy for postmenopausal osteoporosis (PMO).

Material and methods

We used weighted gene co-expression network analysis (WGCNA) to isolate core gene sets from two GEO microarray datasets (GSE7158 and GSE56815). GeneCards found PMO-related genes. GO, KEGG, Lasso regression, and ROC curve analysis refined our candidate genes. Using the GSE105145 dataset, we evaluated KLF2 expression in BMSCs and examined the link between KLF2 and PIK3CA using Pearson correlation analysis. We created a protein-protein interaction network of essential genes involved in osteoblast differentiation and validated the functional roles of KLF2 and PIK3CA in BMSC osteoblast differentiation in vitro.

Results

We created 6 co-expression modules from 10 419 differentially expressed genes (DEGs). PIK3CA, the key gene in the PI3K-Akt pathway, was among 197 PMO-associated DEGs. KLF2 also induced PIK3CA transcription in PMO. BMSCs also expressed elevated KLF2. BMSC osteoblast differentiation involved the PI3K-Akt pathway. In vitro, KLF2 increased PIK3CA transcription and activated the PI3K-Akt pathway to differentiate BMSCs into osteoblasts.

Conclusions

BMSCs release KLF2, which stimulates the PIK3CA-dependent PI3K-Akt pathway to treat PMO. Our findings illuminates the involvement of KLF2 and the PI3K-Akt pathway in BMSC osteoblast development, which may lead to better PMO treatments.

Epigenome-wide association study identifies DNA methylation loci associated with handgrip strength in Chinese monozygotic twins

Background: The decline in muscle strength and function with aging is well recognized, but remains poorly characterized at the molecular level. Here, we report the epigenetic relationship between genome-wide DNA methylation and handgrip strength (HGS) among Chinese monozygotic (MZ) twins. Methods: DNA methylation (DNAm) profiling was conducted in whole blood samples through Reduced Representation Bisulfite Sequencing method. Generalized estimating equation was applied to regress the DNAm of each CpG with HGS. The Genomic Regions Enrichment of Annotations Tool was used to perform enrichment analysis. Differentially methylated regions (DMRs) were detected using comb-p. Causal inference was performed using Inference about Causation through Examination of Familial Confounding method. Finally, we validated candidate CpGs in community residents. Results: We identified 25 CpGs reaching genome-wide significance level. These CpGs located in 9 genes, especially FBLN1, RXRA, and ABHD14B. Many enriched terms highlighted calcium channels, neuromuscular junctions, and skeletal muscle organ development. We identified 21 DMRs of HGS, with several DMRs within FBLN1, SLC30A8, CST3, and SOCS3. Causal inference indicated that the DNAm of 16 top CpGs within FBLN1, RXRA, ABHD14B, MFSD6, and TYW1B might influence HGS, while HGS influenced DNAm at two CpGs within FBLN1 and RXRA. In validation analysis, methylation levels of six CpGs mapped to FLBN1 and one CpG mapped to ABHD14B were negatively associated with HGS weakness in community population. Conclusion: Our study identified multiple DNAm variants potentially related to HGS, especially CpGs within FBLN1 and ABHD14B. These findings provide new clues to the epigenetic modification underlying muscle strength decline.

Kaempferol Improves Exercise Performance by Regulating Glucose Uptake, Mitochondrial Biogenesis, and Protein Synthesis via PI3K/AKT and MAPK Signaling Pathways

Kaempferol is a natural flavonoid with reported bioactivities found in many fruits, vegetables, and medicinal herbs. However, its effects on exercise performance and muscle metabolism remain inconclusive. The present study investigated kaempferol's effects on improving exercise performance and potential mechanisms in vivo and in vitro. The grip strength, exhaustive running time, and distance of mice were increased in the high-dose kaempferol group (p < 0.01). Also, kaempferol reduced fatigue-related biochemical markers and increased the activities of superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px) related to antioxidant capacity. Kaempferol also increased the glycogen and adenosine triphosphate (ATP) content in the liver and skeletal muscle, as well as glucose in the blood. In vitro, kaempferol promoted glucose uptake, protein synthesis, and mitochondrial function and decreased oxidative stress in both 2D and 3D C2C12 myotube cultures. Moreover, kaempferol activated the PI3K/AKT and MAPK signaling pathways in the C2C12 cells. It also upregulated the key targets of glucose uptake, mitochondrial function, and protein synthesis. These findings suggest that kaempferol improves exercise performance and alleviates physical fatigue by increasing glucose uptake, mitochondrial biogenesis, and protein synthesis and by decreasing ROS. Kaempferol's molecular mechanism may be related to the regulation of the PI3K/AKT and MAPK signaling pathways.

Predicting Diagnostic Biomarkers Associated with Pyroptosis in Neuropathic Pain Based on Machine Learning and Experimental Validation

Purpose

Previous studies have shown that pyroptosis plays a vital role in the progress of neuropathic pain (NP), but the molecular mechanisms have not been fully elucidated. The aim of this study was to identify crucial pyroptosis-related genes (PRGs) in NP.

Methods

We identified pyroptosis-related differentially expressed genes (PRDEGs) in NP by machine learning analysis of the GSE24982 and GSE60670 datasets. Furthermore, these PRDEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and Friends analysis, respectively. Meanwhile, receiver operator characteristic (ROC) analysis was performed to assess the diagnostic value of PRDEGs in NP. Finally, we performed immune infiltration analysis of key PRDEGs using CIBERSORTR R package.

Results

We found that 5 PRDEGs by least absolute shrinkage and selection operator (LASSO) regression and random forest and verified by RT-qPCR. GO, KEGG and GSEA revealed that these PRDEGs were mainly enriched in regulation of neuron death, IL-4 signaling, IL-23 pathway, and NF-κB pathway. ROC analysis revealed that most of the PRDEGs performed well in diagnosing NP. We also revealed transcription factors, miRNA regulatory networks and drug interaction networks of PRDEGs. For immune infiltration analysis, PRDEGs were mainly correlated with dendritic cells, monocytes and follicular T helper cells, suggested that it might be involved in the regulation of neuroimmune-related signaling.

Conclusion

A total of five PRDEGs were can be employed as NP biomarkers, particularly Tlr4, Il1b and Casp8, and provide additional evidence for a vital role of pyroptosis in NP.

Identifying potential ferroptosis key genes for diagnosis and treatment of postmenopausal osteoporosis through competitive endogenous RNA network analysis

Objective

Postmenopausal 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.

Methods

We 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.

Results

We 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.

Conclusions

Bioinformatics 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.

Region-specific sympatho-adrenergic regulation of specialized vasculature in bone homeostasis and regeneration

Type H vessels couple angiogenesis with osteogenesis, while sympathetic cues regulate vascular and skeletal function. The crosstalk between sympathetic nerves and type H vessels in bone remains unclear. Here, we first identify close spatial connections between sympathetic nerves and type H vessels in bone, particularly in metaphysis. Sympathoexcitation, mimicked by isoproterenol (ISO) injection, reduces type H vessels and bone mass. Conversely, beta-2-adrenergic receptor (ADRB2) deficiency maintains type H vessels and bone mass in the physiological condition. In vitro experiments reveal indirect sympathetic modulation of angiogenesis via paracrine effects of mesenchymal stem cells (MSCs), which alter the transcription of multiple angiogenic genes in endothelial cells (ECs). Furthermore, Notch signaling in ECs underlies sympathoexcitation-regulated type H vessel formation, impacting osteogenesis and bone mass. Finally, propranolol (PRO) inhibits beta-adrenergic activity and protects type H vessels and bone mass against estrogen deficiency. These findings unravel the specialized neurovascular coupling in bone homeostasis and regeneration.

Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis

BACKGROUND

Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge.

METHODS

PMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by "limma" packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes' function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers.

RESULTS

A total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC).

CONCLUSIONS

ROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target.

PCDH7 as the key gene related to the co-occurrence of sarcopenia and osteoporosis

Sarcopenia and osteoporosis, two degenerative diseases in older patients, have become severe health problems in aging societies. Muscles and bones, the most important components of the motor system, are derived from mesodermal and ectodermal mesenchymal stem cells. The adjacent anatomical relationship between them provides the basic conditions for mechanical and chemical signals, which may contribute to the co-occurrence of sarcopenia and osteoporosis. Identifying the potential common crosstalk genes between them may provide new insights for preventing and treating their development. In this study, DEG analysis, WGCNA, and machine learning algorithms were used to identify the key crosstalk genes of sarcopenia and osteoporosis; this was then validated using independent datasets and clinical samples. Finally, four crosstalk genes (ARHGEF10, PCDH7, CST6, and ROBO3) were identified, and mRNA expression and protein levels of PCDH7 in clinical samples from patients with sarcopenia, with osteoporosis, and with both sarcopenia and osteoporosis were found to be significantly higher than those from patients without sarcopenia or osteoporosis. PCDH7 seems to be a key gene related to the development of both sarcopenia and osteoporosis.

Bioinformatic identification of hub genes Myd88 and Ccl3 and TWS-119 as a potential agent for the treatment of massive cerebral infarction

Background

Massive cerebral infarction (MCI) causes severe neurological deficits, coma and can even result in death. Here, we identified hub genes and pathways after MCI by analyzing microarray data from a murine model of ischemic stroke and identified potential therapeutic agents for the treatment of MCI.

Methods

Microarray expression profiling was performed using the GSE28731 and GSE32529 datasets from the Gene Expression Omnibus (GEO) database. Data from a sham group (n = 6 mice) and a middle cerebral artery occlusion (MCAO) group (n = 7 mice) were extracted to identify common differentially expressed genes (DEGs). After identifying gene interactions, we generated a protein-protein interaction (PPI) network with Cytoscape software. Then, the MCODE plug-in in Cytoscape was used to determine key sub-modules according to MCODE scores. Enrichment analyses were then conducted on DEGs in the key sub-modules to evaluate their biological functions. Furthermore, hub genes were identified by generating the intersections of several algorithms in the cytohubba plug-in; these genes were then verified in other datasets. Finally, we used Connectivity MAP (CMap) to identify potential agents for MCI therapy.

Results

A total of 215 common DEGs were identified and a PPI network was generated with 154 nodes and 947 edges. The most significant key sub-module had 24 nodes and 221 edges. Gene ontology (GO) analysis showed that the DEGs in this sub-module showed enrichment in inflammatory response, extracellular space and cytokine activity in terms of biological process, cellular component and molecular function, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that TNF signaling was the most enriched pathway. Myd88 and Ccl3 were identified as hub genes and TWS-119 was identified as the most potential therapeutic agent by CMap.

Conclusions

Bioinformatic analysis identified two hub genes (Myd88 and Ccl3) for ischemic injury. Further analysis identified TWS-119 as the best potential candidate for MCI therapy and that this target may be associated with TLR/MyD88 signaling.

Association between mixed aldehydes and bone mineral density based on four statistical models

Aldehydes as an environmental pollutant may lead to oxidative stress, which is an important mechanism in the development of osteoporosis. This suggests a possible link between aldehyde exposure and osteoporosis. Considering the mixed nature of aldehyde exposure and the interactions between different aldehydes, we explored for the first time the associations between mixed six aldehydes (benzaldehyde, butyraldehyde, heptanal, hexanal, isovaleraldehyde, and propionaldehyde) and BMD in three populations (men, premenopausal women, and postmenopausal women) by applying four statistical models: quantile g-computation (qgcomp) model, Bayesian kernel machine regression (BKMR) model, generalized linear regression model (GLM), and generalized additive model (GAM), based on the National Health and Nutrition Examination Survey (NHANES) 2013-2014. We found that mixed aldehydes could significantly reduce BMD in men, with hexanaldehyde and propanaldehyde having the greatest negative qgcomp model and BKMR model weights, also confirmed by GLM. The associations between isopentanaldehyde and propanaldehyde and femoral BMD in men were non-linear and had threshold effects as derived from the BKMR model and GAM. The associations turned positive when the concentrations of isopentanaldehyde and propanaldehyde exceeded their respective inflection points. To conclude, our study might provide new ideas for the prevention and treatment of osteoporosis, and hexanaldehyde and propanaldehyde should be more regulated to prevent osteoporosis.

Identification of autophagy-related genes in neuropathic pain through bioinformatic analysis

BACKGROUND

Neuropathic pain (NP) is one of the most common types of chronic pain and significantly compromises the quality of life. Autophagy is an intracellular catabolic process that is required to maintain cellular homeostasis in response to various stresses. The role of autophagy-related genes in the diagnosis and treatment of neuropathic pain remains unclear.

METHODS

We identified autophagy-related differentially expressed genes (ARDEGs) and differentially expressed miRNAs (DE-miRNAs) in neuropathic pain by bioinformatics analysis of the GSE145226 and GSE145199 datasets. These ARDEGs and their co-expressed genes were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and friends analysis. Meanwhile, we constructed TFs-ARDEGs, miRNA-ARDEGs regulatory network through ChIPBase database and the HTFtarget database, multiMir R package. Finally, we performed immune infiltration analysis of ARDEGs by Single Sample Gene Set Enrichment Analysis (ssGSEA).

RESULTS

We identified 2 potential autophagy-related differentially expressed genes (Sirt2 and ST7) that may be closely associated with the pathogenesis of neuropathic pain. GO, KEGG and GSEA analysis revealed that these two ARDEGs were mainly enriched in pyridine nucleotide metabolic process, nicotinamide nucleotide metabolic process, Nicotinate and nicotinamide metabolism, NF-κB pathway, KRAS signaling, P53 pathway. In the TFs-ARDEGs and miRNA-ARDEGs regulatory network, miR-140-5p and Cebpb were predicted to be as crucial regulators in the progression of NP. For the ssGSEA results, Sirt2 was positively correlated with Eosinophil and Effector memory CD8⁺ T cell infiltration, which suggested that it may be involved in the regulation of neuroimmune-related signaling.

CONCLUSION

Two autophagy-related differentially expressed genes, especially Sirt2, may be potential biomarkers for NP, providing more evidence about the crucial role of autophagy in neuropathic pain.

Shared mechanisms and crosstalk of COVID-19 and osteoporosis via vitamin D

Recently accumulated evidence implicates a close association of vitamin D (VitD) insufficiency to the incidence and clinical manifestations of the COVID-19 caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2). Populations with insufficient VitD including patients with osteoporosis are more susceptible to SARS-COV-2 infection and patients with COVID-19 worsened or developed osteoporosis. It is currently unknown, however, whether osteoporosis and COVID-19 are linked by VitD insufficiency. In this study, 42 common targets for VitD on both COVID-19 and osteoporosis were identified among a total of 243 VitD targets. Further bioinformatic analysis revealed 8 core targets (EGFR, AR, ESR1, MAPK8, MDM2, EZH2, ERBB2 and MAPT) in the VitD-COVID-19-osteoporosis network. These targets are involved in the ErbB and MAPK signaling pathways critical for lung fibrosis, bone structural integrity, and cytokines through a crosstalk between COVID-19 and osteoporosis via the VitD-mediated conventional immune and osteoimmune mechanisms. Molecular docking confirmed that VitD binds tightly to the predicted targets. These findings support that VitD may target common signaling pathways in the integrated network of lung fibrosis and bone structural integrity as well as the immune systems. Therefore, VitD may serve as a preventive and therapeutic agent for both COVID-19 and osteoporosis.

Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis

Backgrounds

As a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.

Methods

Data were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.

Results

A total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.

Conclusions

We identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.

Screening of Sepsis Biomarkers Based on Bioinformatics Data Analysis

Methods

Gene expression profiles of GSE13904, GSE26378, GSE26440, GSE65682, and GSE69528 were obtained from the National Center for Biotechnology Information (NCBI). The differentially expressed genes (DEGs) were searched using limma software package. Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs and screen hub genes.

Results

A total of 108 DEGs were identified in the study, of which 67 were upregulated and 41 were downregulated. 15 superlative diagnostic biomarkers (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) for sepsis were identified by bioinformatics analysis.

Conclusion

15 hub genes (CCL5, CCR7, CD2, CD27, CD274, CD3D, GNLY, GZMA, GZMH, GZMK, IL2RB, IL7R, ITK, KLRB1, and PRF1) have been elucidated in this study, and these biomarkers may be helpful in the diagnosis and therapy of patients with sepsis.

Impact of Alendronate Sodium plus Elcatonin on Postoperative Bone Pain in Patients with Osteoporotic Fractures

Objective

This research aims to investigate and analyze the impact of alendronate sodium (ALN) plus elcatonin (EC) in treating postoperative bone pain (BP) in patients with osteoporotic fractures (OPFs).

Methods

One hundred and thirty-eight cases of OPFs admitted between July 2018 and July 2021 were selected, of which 68 cases receiving ALN were set as the control group and 70 cases receiving ALN plus EC were set as the research group. Intercomparisons were performed in terms of BP, curative effect, complication rate, and serum bone metabolism indexes such as bone Gla protein (BGP), parathyroid hormone (PTH), and bone alkaline phosphatase (BALP).

Results

Better postoperative BP relief, higher overall response rate, and lower complication rate were identified in the research group versus the control group. On the other hand, the research group presented with increased BGP and BALP after treatment, higher than those in the control group, while the posttreament PTH decreased obviously and was lower versus the control group.

Conclusions

For OPF patients, ALN plus EC contributes to significantly reduced postoperative BP, improved clinical efficacy, higher treatment safety, and better bone metabolism, which has high clinical application value.

RRM2 regulates osteogenesis of mouse embryo fibroblasts via the Wnt/β‑catenin signaling pathway

Osteoporosis is a widespread bone metabolic disease characterized by reduced bone mass and bone microstructure deterioration. Ribonucleotide reductase M2 (RRM2) is a key enzyme in DNA synthesis and repair. The present study investigated the effect of RRM2 on osteogenesis of mouse embryo fibroblasts (MEFs) and its molecular mechanism. Bioinformatics analysis revealed that RRM2 expression was increased during osteogenesis of MEFs triggered by bone morphogenetic protein 9. Subsequently, MEFs were used as a mesenchymal stem cell model and osteogenic inducing medium was used to induce osteogenic differentiation. RRM2 protein expression was measured by western blotting during osteogenic differentiation induction of MEFs. RRM2 levels in MEFs were upregulated and downregulated by RRM2-overexpressing recombinant adenovirus and small interfering RNA-RRM2, respectively. Bone formation markers (RUNX family transcription factor 2, osterix, distal-less homeobox 5, collagen type I α1 chain, osteopontin and osteocalcin) were detected by reverse transcription-quantitative (RT-q) PCR and alkaline phosphatase (ALP) and Alizarin Red S staining were examined. The protein expression levels of β-catenin and the ratio of phosphorylated (p-)GSK-3β to GSK-3β were detected by western blotting and the RNA expression of downstream related target genes (β-catenin, axis inhibition protein 2 (AXIN2), transcription factor 7 like 2, lymphoid enhancer binding factor 1, c-MYC and Cyclin D1) in the Wnt/β-catenin signaling pathway was measured by RT-qPCR. RRM2 protein expression increased as the osteogenic differentiation induction period was extended. RRM2 overexpression increased osteogenic marker RNA expression, ALP activity, bone mineralization, the protein expression levels of β-catenin, the ratio of p-GSK-3β to GSK-3β and the RNA expression of downstream related target genes in the Wnt/β-catenin signaling pathway, whereas RRM2 knockdown had the opposite effect. The findings of the present study revealed that RRM2 overexpression enhanced osteogenic differentiation, while RRM2 knockdown reduced osteogenic differentiation. RRM2 may regulate osteogenic differentiation of MEFs via the canonical Wnt/β-catenin signaling pathway, providing a possible therapeutic target for osteoporosis.

Identification of ferroptosis-associated biomarkers for the potential diagnosis and treatment of postmenopausal osteoporosis

OBJECTIVE

Postmenopausal 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 MATERIALS

We 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.

RESULTS

There 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.

CONCLUSIONS

On 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.