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

Comprehensive Analysis of the Mechanism of Anoikis in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC), ranking as the second-leading cause of global mortality among malignancies, poses a substantial burden on public health worldwide. Anoikis, a type of programmed cell death, serves as a barrier against the dissemination of cancer cells to distant organs, thereby constraining the progression of cancer. Nevertheless, the mechanism of genes related to anoikis in HCC is yet to be elucidated.

Methods

This paper's data (TCGA-HCC) were retrieved from the database of the Cancer Genome Atlas (TCGA). Differential gene expression with prognostic implications for anoikis was identified by performing both the univariate Cox and differential expression analyses. Through unsupervised cluster analysis, we clustered the samples according to these DEGs. By employing the least absolute shrinkage and selection operator Cox regression analysis (CRA), a clinical predictive gene signature was generated from the DEGs. The Cell-Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to determine the proportions of immune cell types. The external validation data (GSE76427) were procured from Gene Expression Omnibus (GEO) to verify the performance of the clinical prognosis gene signature. Western blotting and immunohistochemistry (IHC) analysis confirmed the expression of risk genes.

Results

In total, 23 prognostic DEGs were identified. Based on these 23 DEGs, the samples were categorized into four distinct subgroups (clusters 1, 2, 3, and 4). In addition, a clinical predictive gene signature was constructed utilizing ETV4, PBK, and SLC2A1. The gene signature efficiently distinguished individuals into two risk groups, specifically low and high, demonstrating markedly higher survival rates in the former group. Significant correlations were observed between the expression of these risk genes and a variety of immune cells. Moreover, the outcomes from the validation cohort analysis aligned consistently with those obtained from the training cohort analysis. The results of Western blotting and IHC showed that ETV4, PBK, and SLC2A1 were upregulated in HCC samples.

Conclusion

The outcomes of this paper underscore the effectiveness of the clinical prognostic gene signature, established utilizing anoikis-related genes, in accurately stratifying patients. This signature holds promise in advancing the development of personalized therapy for HCC.

Multi-omics Analysis to Identify Key Immune Genes for Osteoporosis based on Machine Learning and Single-cell Analysis

OBJECTIVE

Osteoporosis is a severe bone disease with a complex pathogenesis involving various immune processes. With the in-depth understanding of bone immune mechanisms, discovering new therapeutic targets is crucial for the prevention and treatment of osteoporosis. This study aims to explore novel bone immune markers related to osteoporosis based on single-cell and transcriptome data, utilizing bioinformatics and machine learning methods, in order to provide novel strategies for the diagnosis and treatment of the disease.

METHODS

Single cell and transcriptome data sets were acquired from Gene Expression Omnibus (GEO). The data was then subjected to cell communication analysis, pseudotime analysis, and high dimensional WGCNA (hdWGCNA) analysis to identify key immune cell subpopulations and module genes. Subsequently, ConsensusClusterPlus analysis was performed on the key module genes to identify different diseased subgroups in the osteoporosis (OP) training set samples. The immune characteristics between subgroups were evaluated using Cibersort, EPIC, and MCP counter algorithms. OP's hub genes were screened using 10 machine learning algorithms and 113 algorithm combinations. The relationship between hub genes and immunity and pathways was established by evaluating the immune and pathway scores of the training set samples through the ESTIMATE, MCP-counter, and ssGSEA algorithms. Real-time fluorescence quantitative PCR (RT-qPCR) testing was conducted on serum samples collected from osteoporosis patients and healthy adults.

RESULTS

In OP samples, the proportions of bone marrow-derived mesenchymal stem cells (BM-MSCs) and neutrophils increased significantly by 6.73% (from 24.01% to 30.74%) and 6.36% (from 26.82% to 33.18%), respectively. We found 16 intersection genes and four hub genes (DND1, HIRA, SH3GLB2, and F7). RT-qPCR results showed reduced expression levels of DND1, HIRA, and SH3GLB2 in clinical blood samples of OP patients. Moreover, the four hub genes showed positive correlations with neutrophils (0.65-0.90), immature B cells (0.76-0.92), and endothelial cells (0.79-0.87), while showing negative correlations with myeloid-derived suppressor cells (negative 0.54-0.73), T follicular helper cells (negative 0.71-0.86), and natural killer T cells (negative 0.75-0.85).

CONCLUSION

Neutrophils play a crucial role in the occurrence and development of osteoporosis. The four hub genes potentially inhibit metabolic activities and trigger inflammation by interacting with other immune cells, thereby significantly contributing to the onset and diagnosis of OP.

The SPI1/SMAD5 cascade in the promoting effect of icariin on osteogenic differentiation of MC3T3-E1 cells: a mechanism study

BACKGROUND

Dysregulation of osteogenic differentiation is a crucial event during osteoporosis. The bioactive phytochemical icariin has become an anti-osteoporosis candidate. Here, we elucidated the mechanisms underlying the promoting function of icariin in osteogenic differentiation.

METHODS

Murine pre-osteoblast MC3T3-E1 cells were stimulated with dexamethasone (DEX) to induce osteogenic differentiation, which was evaluated by an Alizarin Red staining assay and ALP activity measurement. The mRNA amounts of SPI1 and SMAD5 were detected by real-time quantitative PCR. Expression analysis of proteins, including osteogenic markers (OPN, OCN and RUNX2) and autophagy-associated proteins (LC3, Beclin-1, and ATG5), was performed by immunoblotting. The binding of SPI1 and the SMAD5 promoter was predicted by the Jaspar2024 algorithm and confirmed by chromatin immunoprecipitation (ChIP) experiments. The regulation of SPI1 in SMAD5 was examined by luciferase assays.

RESULTS

During osteogenic differentiation of MC3T3-E1 cells, SPI1 and SMAD5 were upregulated. Functionally, SPI1 overexpression enhanced autophagy and osteogenic differentiation of MC3T3-E1 cells, while SMAD5 downregulation exhibited opposite effects. Mechanistically, SPI1 could enhance SMAD5 transcription and expression. Downregulation of SMAD5 also reversed SPI1 overexpression-induced autophagy and osteogenic differentiation in MC3T3-E1 cells. In MC3T3-E1 cells under DEX stimulation, icariin increased SMAD5 expression by upregulating SPI1. Furthermore, icariin could attenuate SPI1 depletion-imposed inhibition of autophagy and osteogenic differentiation of MC3T3-E1 cells.

CONCLUSION

Our findings demonstrate that the SPI1/SMAD5 cascade, with the ability to enhance osteogenic differentiation, underlies the promoting effect of icariin on osteogenic differentiation of MC3T3-E1 cells.

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis

BACKGROUND

Osteoporosis (OP), the "silent epidemic" of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing eventually. Unique among OP's characteristics is the altered metabolic profile of affected cells, particularly in pyrimidine metabolism (PyM), a crucial pathway for nucleotide turnover and pyrimidine decomposition. While metabolic adaptation is acknowledged as a therapeutic target in various diseases, the specific role of PyM genes (PyMGs) in OP's molecular response remains to be clarified.

METHODS

In pursuit of elucidating and authenticating PyMGs relevant to OP, we embarked on a comprehensive bioinformatics exploration. This entailed the integration of Weighted Gene Co-expression Network Analysis (WGCNA) with a curated list of 37 candidate PyMGs, followed by the examination of their biological functions and pathways via Gene Set Variation Analysis (GSVA). The Least Absolute Shrinkage and Selection Operator (LASSO) technique was harnessed to identify crucial hub genes. We evaluated the diagnostic prowess of five PyMGs in OP detection and explored their correlation with OP's clinical traits, further validating their expression profiles through independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).

RESULTS

Our analytical rigor unveiled five PyMGs-IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N-with significant ties to OP. A deeper dive into their biological functions highlighted their roles in estrogen response modulation, cytosolic calcium ion concentration regulation, and GABAergic synaptic transmission. Remarkably, these PyMGs emerged as potent diagnostic biomarkers for OP, distinguishing affected individuals with substantial accuracy.

CONCLUSIONS

This investigation brings to light five PyMGs intricately associated with OP, heralding new avenues for biomarker discovery and providing insights into its pathophysiological underpinnings. These findings not only deepen our comprehension of OP's complexity but also herald the advent of more refined diagnostic and therapeutic modalities.

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.

Investigating the impact of Wnt pathway-related genes on biomarker and diagnostic model development for osteoporosis in postmenopausal females

The Wnt signaling pathway is essential for bone development and maintaining skeletal homeostasis, making it particularly relevant in osteoporosis patients. Our study aimed to identify distinct molecular clusters associated with the Wnt pathway and develop a diagnostic model for osteoporosis in postmenopausal Caucasian women. We downloaded three datasets (GSE56814, GSE56815 and GSE2208) related to osteoporosis from the GEO database. Our analysis identified a total of 371 differentially expressed genes (DEGs) between low and high bone mineral density (BMD) groups, with 12 genes associated with the Wnt signaling pathway, referred to as osteoporosis-associated Wnt pathway-related genes. Employing four independent machine learning models, we established a diagnostic model using the 12 osteoporosis-associated Wnt pathway-related genes in the training set. The XGB model showed the most promising discriminative potential. We further validate the predictive capability of our diagnostic model by applying it to three external datasets specifically related to osteoporosis. Subsequently, we constructed a diagnostic nomogram based on the five crucial genes identified from the XGB model. In addition, through the utilization of DGIdb, we identified a total of 30 molecular compounds or medications that exhibit potential as promising therapeutic targets for osteoporosis. In summary, our comprehensive analysis provides valuable insights into the relationship between the osteoporosis and Wnt signaling pathway.

Modified Buyang Huanwu Decoction alleviates diabetic liver injury via inhibiting oxidative stress in db/db mice

OBJECTIVES

In diabetes, chronic hyperglycemia increases the overactivation of oxidative phosphorylation of mitochondria in the liver, resulting in oxidative stress (OS) damage. The Nrf2 signaling pathway plays a key role in preventing hepatic oxidative injury and inflammation. This study aims to investigate the therapeutic effect and mechanism of Modified Buyang Huanwu Decoction (mBYHWD) on diabetic liver injury (DLI) by regulating oxidative stress mediated by Nrf2 signaling pathway.

METHODS

The experiment was divided into three groups: a control group (db/m mice, Con), a diabetes model group (db/db mice, Mod), and a traditional Chinese medicine group (db/m mice, mBYHWD). Post-treatment, serum from each group was analyzed to assess changes of blood glucose, blood lipid, and liver function. These results were combined with data mining to explore the possible pathogenesis of DLI. Liver tissues were collected to observe the pathological morphology and detect related proteins.

RESULTS

The results demonstrated that mBYHWD significantly reduced blood lipids and improved liver function following diabetic liver injury. The histopathological results demonstrated that mBYHWD could significantly ameliorate damage of diabetic hepatocytes. Protein analysis revealed that mBYHWD treatment significantly increased the expression of antioxidant proteins in diabetic liver tissue and inhibited inflammation.

CONCLUSIONS

The therapeutic mechanism of mBYHWD on DLI may involve activating the Nrf2 signaling pathway to improve oxidative stress, inhibit inflammation, and reduce liver tissue fibrosis.

Bioinformatics and machine learning were used to validate glutamine metabolism-related genes and immunotherapy in osteoporosis patients

BACKGROUND

Osteoporosis (OP), often referred to as the "silent disease of the twenty-first century," poses a significant public health concern due to its severity, chronic nature, and progressive course, predominantly affecting postmenopausal women and elderly individuals. The pathogenesis and progression of this disease have been associated with dysregulation in tumor metabolic pathways. Notably, the metabolic utilization of glutamine has emerged as a critical player in cancer biology. While metabolic reprogramming has been extensively studied in various malignancies and linked to clinical outcomes, its comprehensive investigation within the context of OP remains lacking.

METHODS

This study aimed to identify and validate potential glutamine metabolism genes (GlnMgs) associated with OP through comprehensive bioinformatics analysis. The identification of GlnMgs was achieved by integrating the weighted gene co-expression network analysis and a set of 28 candidate GlnMgs. Subsequently, the putative biological functions and pathways associated with GlnMgs were elucidated using gene set variation analysis. The LASSO method was employed to identify key hub genes, and the diagnostic efficacy of five selected GlnMgs in OP detection was assessed. Additionally, the relationship between hub GlnMgs and clinical characteristics was investigated. Finally, the expression levels of the five GlnMgs were validated using independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).

RESULTS

Five GlnMgs, namely IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N, were identified in this study. To gain insights into their biological functions, particular emphasis was placed on synaptic transmission GABAergic, inward rectifier potassium channel activity, and the cytoplasmic side of the lysosomal membrane. Furthermore, the diagnostic potential of these five GlnMgs in distinguishing individuals with OP yielded promising results, indicating their efficacy as discriminative markers for OP.

CONCLUSIONS

This study discovered five GlnMgs that are linked to OP. They shed light on potential new biomarkers for OP and tracking its progression.

Immune-related gene IL17RA as a diagnostic marker in osteoporosis

Objectives: Bone immune disorders are major contributors to osteoporosis development. This study aims to identify potential diagnostic markers and molecular targets for osteoporosis treatment from an immunological perspective. Method: We downloaded dataset GSE56116 from the Gene Expression Omnibus database, and identified differentially expressed genes (DEGs) between normal and osteoporosis groups. Subsequently, differentially expressed immune-related genes (DEIRGs) were identified, and a functional enrichment analysis was performed. A protein-protein interaction network was also constructed based on data from STRING database to identify hub genes. Following external validation using an additional dataset (GSE35959), effective biomarkers were confirmed using RT-qPCR and immunohistochemical (IHC) staining. ROC curves were constructed to validate the diagnostic values of the identified biomarkers. Finally, a ceRNA and a transcription factor network was constructed, and a Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed to explore the biological functions of these diagnostic markers. Results: In total, 307 and 31 DEGs and DEIRGs were identified, respectively. The enrichment analysis revealed that the DEIRGs are mainly associated with Gene Ontology terms of positive regulation of MAPK cascade, granulocyte chemotaxis, and cytokine receptor. protein-protein interaction network analysis revealed 10 hub genes: FGF8, KL, CCL3, FGF4, IL9, FGF9, BMP7, IL17RA, IL12RB2, CD40LG. The expression level of IL17RA was also found to be significantly high. RT-qPCR and immunohistochemical results showed that the expression of IL17RA was significantly higher in osteoporosis patients compared to the normal group, as evidenced by the area under the curve Area Under Curve of 0.802. Then, we constructed NEAT1-hsa-miR-128-3p-IL17RA, and SNHG1-hsa-miR-128-3p-IL17RA ceRNA networks in addition to ERF-IL17RA, IRF8-IL17RA, POLR2A-IL17RA and ERG-IL17RA transcriptional networks. Finally, functional enrichment analysis revealed that IL17RA was involved in the development and progression of osteoporosis by regulating local immune and inflammatory processes in bone tissue. Conclusion: This study identifies the immune-related gene IL17RA as a diagnostic marker of osteoporosis from an immunological perspective, and provides insight into its biological function.

NEAT1/microRNA 339-5p/SPI1 Axis Feedback Loop Contributes to Osteogenic Differentiation in Acute Suppurative Osteomyelitis in Children

Objective

Long non-coding RNA plays an important role in osteogenic differentiation. Nuclear enriched abundant transcript 1 (NEAT1) has been revealed to promote osteogenic differentiation in human bone marrow mesenchymal stem cells (hBMSCs), but the underlying regulatory mechanism remains unknown in acute suppurative osteomyelitis of children.

Methods

Osteogenic medium (OM) was used to induce osteogenic differentiation. Quantitative real-time PCR and Western blotting were used to evaluate gene expression. The effects of NEAT1, microRNA 339-5p (miR-339-5p), and salmonella pathogenicity island 1 (SPI1) on osteogenic differentiation were assessed in vitro using alizarin red S staining assays and alkaline phosphatase activity. Interactions between NEAT1, miR-339-5p, and SPI1 were identified using immunoprecipitation, luciferase reporter assays, and chromatin immunoprecipitation.

Results

During osteogenic differentiation, expression of NEAT1 was up-regulated in hBMSCs, and miR-339-5p level was down during osteogenic differentiation. Knockdown of NEAT1 reduced the osteogenic differentiation of hBMSCs, and down-regulation of miR-339-5p may counteract the effect of NEAT1 silencing. SPI1 was a target of miR-339-5p by luciferase reporter assay and was also a transcription factor of NEAT1 by chromatin immunoprecipitation. A positive NEAT1-miR-339-5p-SPI1 feedback loop was found to be present during osteogenic differentiation in hBMSCs.

Conclusion

It was the first study to reveal that the NEAT1-miR-339-5p-SPI1 feedback loop can promote osteogenic differentiation in hBMSCs and shed a new light on the role of NEAT1 during osteogenic differentiation.

Non-invasive diagnostic potential of salivary miR-25-3p for periodontal disease and osteoporosis among a cohort of elderly patients with type 2 diabetes mellitus

OBJECTIVE

Osteoporosis (OP) and periodontal disease (PD) are two common health issues that threaten the older population and potentially connected each other in the context of type 2 diabetes mellitus (T2DM). Dysregulated expression of microRNAs (miRNAs) may contribute to the development and progression of both OP and PD among elderly T2DM patients. The present study aimed to evaluate the accuracy of miR-25-3p expression for the detection of OP and PD when compared to a mixed group of patients with T2DM.

METHODS

The study recruited 45 T2DM patients with normal bone mineral density (BMD) and healthy periodontium, 40 type 2 diabetic osteoporosis patients coexistent with PD, 50 type 2 diabetic osteoporosis patients with healthy periodontium, and 52 periodontally healthy individuals. miRNA expression measurements in the saliva were determined by real-time PCR.

RESULTS

The salivary expression of miR-25-3p was higher in type 2 diabetic osteoporosis patients than patients with T2DM only and healthy individuals (P < 0.05). Among type 2 diabetic osteoporosis patients, those with PD exhibited a higher salivary expression of miR-25-3p than those with healthy periodontium (P < 0.05). Among type 2 diabetic patients with healthy periodontium, a higher salivary expression of miR-25-3p was noted in those with OP than those without (P < 0.05). We also found a higher salivary expression of miR-25-3p in T2DM patients than healthy individuals (P < 0.05). It was revealed that the salivary expression of miR-25-3p was increased as the T scores of BMD of patients were lowered, the PPD and CAL values of patients were enhanced. The salivary expression of miR-25-3p used as a test to predict a diagnosis of PD among type 2 diabetic osteoporosis patients, a diagnosis of OP among type 2 diabetic patients, and a diagnosis of T2DM among healthy individuals produced AUC of 0.859. 0.824, and 0.886, respectively.

CONCLUSION

The findings obtained from the study support salivary miR-25-3p confers non-invasive diagnostic potential for PD and OP among a cohort of elderly T2DM patients.