Identification of gene biomarkers in patients with postmenopausal osteoporosis

Interpreting the molecular mechanisms of RBBP4/7 and their roles in human diseases (Review)

Histone chaperones serve a pivotal role in maintaining human physiological processes. They interact with histones in a stable manner, ensuring the accurate and efficient execution of DNA replication, repair and transcription. Retinoblastoma binding protein (RBBP)4 and RBBP7 represent a crucial pair of histone chaperones, which not only govern the molecular behavior of histones H3 and H4, but also participate in the functions of several protein complexes, such as polycomb repressive complex 2 and nucleosome remodeling and deacetylase, thereby regulating the cell cycle, histone modifications, DNA damage and cell fate. A strong association has been indicated between RBBP4/7 and some major human diseases, such as cancer, age‑related memory loss and infectious diseases. The present review assesses the molecular mechanisms of RBBP4/7 in regulating cellular biological processes, and focuses on the variations in RBBP4/7 expression and their potential mechanisms in various human diseases, thus providing new insights for their diagnosis and treatment.

Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis

Introduction

Sepsis is a worldwide epidemic, with high morbidity and mortality. Cuproptosis is a form of cell death that is associated with a wide range of diseases. This study aimed to explore genes associated with cuproptosis in sepsis, construct predictive models and screen for potential targets.

Methods

The LASSO algorithm and SVM-RFE model has been analysed the expression of cuproptosis-related genes in sepsis and immune infiltration characteristics and identified the marker genes under a diagnostic model. Gene-drug networks, mRNA-miRNA networks and PPI networks were constructed to screen for potential biological targets. The expression of marker genes was validated based on the GSE57065 dataset. Consensus clustering method was used to classify sepsis samples.

Results

We found 381 genes associated with the development of sepsis and discovered significantly differentially expressed cuproptosis-related genes of 16 cell types in sepsis and immune infiltration with CD8/CD4 T cells being lower. NFE2L2, NLRP3, SLC31A1, DLD, DLAT, PDHB, MTF1, CDKN2A and DLST were identified as marker genes by the LASSO algorithm and the SVM-RFE model. AUC > 0.9 was constructed for PDHB and MTF1 alone respectively. The validation group data for PDHB (P=0.00099) and MTF1 (P=7.2e-14) were statistically significant. Consistent clustering analysis confirmed two subtypes. The C1 subtype may be more relevant to cellular metabolism and the C2 subtype has some relevance to immune molecules.The results of animal experiments showed that the gene expression was consistent with the bioinformatics analysis.

Discussion

Our study systematically explored the relationship between sepsis and cuproptosis and constructed a diagnostic model. And, several cuproptosis-related genes may interfere with the progression of sepsis through immune cell infiltration.

Integrating Traditional Machine Learning and Deep Learning for Precision Screening of Anticancer Peptides: A Novel Approach for Efficient Drug Discovery

The rapid and effective identification of anticancer peptides (ACPs) by computer technology provides a new perspective for cancer treatment. In the identification process of ACPs, accurate sequence encoding and effective classification models are crucial for predicting their biological activity. Traditional machine learning methods have been widely applied in sequence analysis, but deep learning provides a new approach to capture sequence complexity. In this study, a two-stage ACPs classification model was innovatively proposed. Three novel coding strategies were explored; two mainstream Natural Language Processing (NLP) models and 11 machine learning models were fused to identify ACPs, which significantly improved the prediction accuracy of ACPs. We analyzed the correlation between peptide chain amino acids and evaluated the relevant performance of the model by the ROC curve and t-SNE dimensionality reduction technique. The results indicated that the deep learning and machine learning fusion models of M3E-base and KNeighborsDist models, especially when considering the semantic information on amino acid sequences, achieved the highest average accuracy (AvgAcc) of 0.939, with an AUC value as high as 0.97. Then, in vitro cell experiments were used to verify that the two ACPs predicted by the model had antitumor efficacy. This study provides a convenient and effective method for screening ACPs. With further optimization and testing, these strategies have the potential to play an important role in drug discovery and design.

Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis

The shared mechanisms between pediatric acute lymphoblastic leukaemia (ALL) and pediatric sepsis are currently unclear. This study was aimed to explore the shared key genes of pediatric ALL and pediatric sepsis. The datasets involved were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control samples in GSE13904 and GSE79533 were intersected. The least absolute shrinkage and selection operator (LASSO) and the boruta analyses were performed in GSE13904 and GSE79533 separately based on shared DEGs, and shared key genes were obtained by taking the intersection of sepsis-related key genes and ALL-related key genes. Three shared key genes (HCK, NOG, RNF125) were obtained, that have a good diagnostic value for both sepsis and ALL. The correlation between shared key genes and differentially expressed immune cells was higher in GSE13904 and conversely, the correlation of which was lower in GSE79533. Suggesting that the sharing key genes had a different impact on the immune environment in pediatric ALL and pediatric sepsis. We make the case that this study provides a new perspective to study the relationship between pediatric ALL and pediatric sepsis.

Quercetin suppresses ovariectomy-induced osteoporosis in rat mandibles by regulating autophagy and the NLRP3 pathway

With the aging population and the popularity of implant prostheses, an increasing number of postmenopausal osteoporosis (PMOP) patients require implant restorations; however, poor bone condition affects the long-term stability of implant prostheses. This study aimed to investigate the therapeutic effect of quercetin (QR) compared with alendronate (ALN), the primary treatment for PMOP, on mandibular osteoporosis (OP) induced by ovariectomy (OVX) in female rats. Adult female rats were treated with QR (50 mg/kg/day), ALN (6.25 mg/kg/week) by gavage for 8 weeks, chloroquine (CQ, 10 mg/kg/twice a week), and cytokine release inhibitory drug 3 (MCC950, 10 mg/kg/three times a week) by intraperitoneal injection for 8 weeks after bilateral OVX. Blood samples were collected prior to euthanasia; the mandibles were harvested and subjected to micro-computed tomography (micro-CT) and pathological analysis. QR administration controlled weight gain and significantly improved the bone microstructure in OVX rats, increasing bone mass, and bone mineral density (BMD), reducing bone trabecular spacing, and decreasing osteoclast numbers. Western blotting, real-time quantitative PCR (RT-qPCR), and serum markers confirmed that QR inhibited interleukin- 1β (IL-1β) and interleukin-18 (IL-18) on the nucleotide-binding oligomerization domain (NOD)-like receptor (NLR) protein 3 (NLRP3) pathway thereby inhibiting osteoclast differentiation, immunofluorescence and western blotting also confirmed that QR inhibited autophagy in OVX rats and suppressed the number of tartrate-resistant acid phosphatase (TRAP)-stained positive osteoclasts. The findings suggest that QR may protect the bone structure and prevent bone loss in osteoporotic rats by inhibiting the NLRP3 pathway and autophagy in osteoclasts with comparable effects to ALN, thus QR may have the potential to be a promising alternative supplement for the preventive and therapeutic treatment of PMOP.

Selection of M7G-related lncRNAs in kidney renal clear cell carcinoma and their putative diagnostic and prognostic role

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. This study aims to develop new biomarkers for KIRC and explore the impact of biomarkers on the immunotherapeutic efficacy for KIRC, providing a theoretical basis for the treatment of KIRC patients.

METHODS

Transcriptome data for KIRC was obtained from the The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Weighted gene co-expression network analysis identified KIRC-related modules of long noncoding RNAs (lncRNAs). Intersection analysis was performed differentially expressed lncRNAs between KIRC and normal control samples, and lncRNAs associated with N(7)-methylguanosine (m7G), resulting in differentially expressed m7G-associated lncRNAs in KIRC patients (DE-m7G-lncRNAs). Machine Learning was employed to select biomarkers for KIRC. The prognostic value of biomarkers and clinical features was evaluated using Kaplan-Meier (K-M) survival analysis, univariate and multivariate Cox regression analysis. A nomogram was constructed based on biomarkers and clinical features, and its efficacy was evaluated using calibration curves and decision curves. Functional enrichment analysis was performed to investigate the functional enrichment of biomarkers. Correlation analysis was conducted to explore the relationship between biomarkers and immune cell infiltration levels and common immune checkpoint in KIRC samples.

RESULTS

By intersecting 575 KIRC-related module lncRNAs, 1773 differentially expressed lncRNAs, and 62 m7G-related lncRNAs, we identified 42 DE-m7G-lncRNAs. Using XGBoost and Boruta algorithms, 8 biomarkers for KIRC were selected. Kaplan-Meier survival analysis showed significant survival differences in KIRC patients with high and low expression of the PTCSC3 and RP11-321G12.1. Univariate and multivariate Cox regression analyses showed that AP000696.2, PTCSC3 and clinical characteristics were independent prognostic factors for patients with KIRC. A nomogram based on these prognostic factors accurately predicted the prognosis of KIRC patients. The biomarkers showed associations with clinical features of KIRC patients, mainly localized in the cytoplasm and related to cytokine-mediated immune response. Furthermore, immune feature analysis demonstrated a significant decrease in immune cell infiltration levels in KIRC samples compared to normal samples, with a negative correlation observed between the biomarkers and most differentially infiltrating immune cells and common immune checkpoints.

CONCLUSION

In summary, this study discovered eight prognostic biomarkers associated with KIRC patients. These biomarkers showed significant correlations with clinical features, immune cell infiltration, and immune checkpoint expression in KIRC patients, laying a theoretical foundation for the diagnosis and treatment of KIRC.

Insulin-like growth factor binding protein 2: a core biomarker of left ventricular dysfunction in dilated cardiomyopathy

BACKGROUND

RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.

METHODS

In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.

RESULTS

A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells.

CONCLUSION

In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.

Construction and clinical validation of a nomogram-based predictive model for diabetic retinopathy in type 2 diabetes

OBJECTIVE

This study aimed to identify risk factors for diabetic retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM) and construct a nomogram prediction model for DR.

METHODS

T2DM patients (n = 520) who underwent funduscopic examinations from June 2020 to June 2022 were included. Of these patients, 220 had DR, yielding a disease rate of 40.38%. Patients were divided into a training set (n = 364) and a validation set (n = 156) at a 7:3 ratio. Feature variables were selected using LASSO regression, random forests, and decision trees. Venn diagrams identified common DR feature variables. The prediction model's validity was assessed using the C-index, decision curve analysis (DCA), receiver operating characteristic (ROC) curves, and calibration curves.

RESULTS

Factors influencing DR were age, Diabetic Peripheral Neuropathy (DPN), Hemoglobin A1C (HbA1C) levels, High-Density Lipoprotein (HDL) cholesterol, Low-Density Lipoprotein (LDL) cholesterol, Neutrophil-to-Lymphocyte Ratio (NLR), Triglycerides (TG), Blood Urea Nitrogen (BUN), and disease duration. Univariate analysis excluded LDL as being unrelated to DR. The DR prediction model, constructed using the remaining eight variables, showed internal validation metrics with a C-index of 0.937, area under the ROC curve (AUC) of 0.773, and DCA net benefit of 11%-95%. The external validation metrics demonstrated a C-index of 0.916, AUC of 0.735, and DCA net benefit of 17%-93%. Calibration curves indicated high consistency.

CONCLUSION

This study developed a nomogram prediction model to assess the risk of DR in patients with T2DM. The model demonstrated high precision through internal validation.

Identification of signature genes for renal ischemia‒reperfusion injury based on machine learning and WGCNA

Background

As an inevitable event after kidney transplantation, ischemia‒reperfusion injury (IRI) can lead to a decrease in kidney transplant success. The search for signature genes of renal ischemia‒reperfusion injury (RIRI) is helpful in improving the diagnosis and guiding clinical treatment.

Methods

We first downloaded 3 datasets from the GEO database. Then, differentially expressed genes (DEGs) were identified and applied for functional enrichment analysis. After that, we performed three machine learning methods, including random forest (RF), Lasso regression analysis, and support vector machine recursive feature elimination (SVM-RFE), to further predict candidate genes. WGCNA was also executed to screen candidate genes from DEGs. Then, we took the intersection of candidate genes to obtain the signature genes of RIRI. Receiver operating characteristic (ROC) analysis was conducted to measure the predictive ability of the signature genes. Kaplan‒Meier analysis was used for association analysis between signature genes and graft survival. Verifying the expression of signature genes in the ischemia cell model.

Results

A total of 117 DEGs were screened out. Subsequently, RF, Lasso regression analysis, SVM-RFE and WGCNA identified 17, 25, 18 and 74 candidate genes, respectively. Finally, 3 signature genes (DUSP1, FOS, JUN) were screened out through the intersection of candidate genes. ROC analysis suggested that the 3 signature genes could well diagnose and predict RIRI. Kaplan‒Meier analysis indicated that patients with low FOS or JUN expression had a longer OS than those with high FOS or JUN expression. Finally, we validated using the ischemia cell model that compared to the control group, the expression level of JUN increased under hypoxic conditions.

Conclusions

Three signature genes (DUSP1, FOS, JUN) offer a good prediction for RIRI outcome and may serve as potential therapeutic targets for RIRI intervention, especially JUN. The prediction of graft survival by FOS and JUN may improve graft survival in patients with RIRI.

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.

The Hub Genes Related to Osteoporosis Were Identified by Bioinformatics Analysis

Osteoporosis (OP) is commonly encountered, which is a kind of systemic injury of bone mass and microstructure, leading to brittle fractures. With the aging of the population, this disease will pose a more serious impact on medical, social, and economic aspects, especially postmenopausal osteoporosis (PMOP). This study is aimed at figuring out potential therapeutic targets and new biomarkers in OP via bioinformatics tools. After differentially expressed gene (DEG) analysis, we successfully identified 97 upregulated and 172 downregulated DEGs. They are mainly concentrated in actin binding, regulation of cytokine production, muscle cell promotion, chemokine signaling pathway, and cytokine-cytokine receiver interaction. According to the diagram of protein-protein interaction (PPI), we obtained 10 hub genes: CCL5, CXCL10, EGFR, HMOX1, IL12B, CCL7, TBX21, XCL1, PGR, and ITGA1. Expression analysis showed that Egfr, Hmox1, and Pgr were significantly upregulated in estrogen-treated osteoporotic patients, while Ccl5, Cxcl10, Il12b, Ccl7, Tbx21, Xcl1, and Itga1 were significantly downregulated. In addition, the analysis results of Pearson's correlation revealed that CCL7 has a strong positive association with IL12b, TBX21, and CCL5 and so was CCL5 with IL12b. Conversely, EGFR has a strong negative association with XCL1 and CXCL10. In conclusion, this study screened 10 hub genes related to OP based on the GEO database, laying a biological foundation for further research on new biomarkers and potential therapeutic targets in OP.

C1QC, VSIG4, and CFD as Potential Peripheral Blood Biomarkers in Atrial Fibrillation-Related Cardioembolic Stroke

Atrial fibrillation (AF) is a major risk factor for ischemic stroke. We aimed to identify novel potential biomarkers with diagnostic value in patients with atrial fibrillation-related cardioembolic stroke (AF-CE).Publicly available gene expression profiles related to AF, cardioembolic stroke (CE), and large artery atherosclerosis (LAA) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified and then functionally annotated. The support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic AF-CE biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the ROC curve (AUC). In order to verify the predictive results, the blood samples of 13 healthy controls, 20 patients with CE, and 20 patients with LAA stroke were acquired for RT-qPCR, and the correlation between biomarkers and clinical features was further explored. Lastly, a nomogram and the companion website were developed to predict the CE-risk rate. Three feature genes (C1QC, VSIG4, and CFD) were selected and validated in the training and the external datasets. The qRT-PCR evaluation showed that the levels of blood biomarkers (C1QC, VSIG4, and CFD) in patients with AF-CE can be used to differentiate patients with AF-CE from normal controls (P < 0.05) and can effectively discriminate AF-CE from LAA stroke (P < 0.05). Immune cell infiltration analysis revealed that three feature genes were correlated with immune system such as neutrophils. Clinical impact curve, calibration curves, ROC, and DCAs of the nomogram indicate that the nomogram had good performance. Our findings showed that C1QC, VSIG4, and CFD can potentially serve as diagnostic blood biomarkers of AF-CE; novel nomogram and the companion website can help clinicians to identify high-risk individuals, thus helping to guide treatment decisions for stroke patients.

Genetic analysis of potential biomarkers and therapeutic targets in ferroptosis from psoriasis

Introduction

Ferroptosis is associated with multiple pathophysiological processes. Inhibition of ferroptosis has received much concern for some diseases. Nonetheless, there is no study comprehensively illustrating functions of ferroptosis-related genes (FRGs) in psoriasis.

Methods

In this study, FRGs together with psoriasis-associated data were obtained in Ferroptosis Database (FerrDb) and gene expression omnibus (GEO) database separately. This work identified altogether 199 psoriasis-associated DE-FRGs, and they were tightly associated with immunity and autophagy modulation. Thereafter, the present study utilized SVM-RFE and LASSO algorithms to identify NR5A2, CISD1, GCLC, PRKAA2, TRIB2, ABCC5, ACSF2, TIMM9, DCAF7, PEBP1, and MDM2 from those 199 DE-FRGs to be marker genes. As revealed by later functional annotation, the marker genes possibly had important effects on psoriasis through being involved in diverse psoriasis pathogenesis-related pathways such as cell cycle, toll-like receptor (TLR), chemokine, and nod-like receptor (NLR) pathways. Moreover, altogether 37 drugs that targeted 11 marker genes were acquired. Besides, based on CIBERSORT analysis, alterations of immune microenvironment in psoriasis cases were possibly associated with PRKAA2, PEBP1, CISD1, and ACSF2.

Discussion

Taken together, this work established the diagnostic potency and shed more lights on psoriasis-related mechanism. More investigations are warranted to validate its value in diagnosing psoriasis before it is applied in clinic.

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.

Typing characteristics of metabolism-related genes in osteoporosis

Objective: Osteoporosis is a common musculoskeletal disease. Fractures caused by osteoporosis place a huge burden on global healthcare. At present, the mechanism of metabolic-related etiological heterogeneity of osteoporosis has not been explored, and no research has been conducted to analyze the metabolic-related phenotype of osteoporosis. This study aimed to identify different types of osteoporosis metabolic correlates associated with underlying pathogenesis by machine learning.

Methods: In this study, the gene expression profiles GSE56814 and GSE56815 of osteoporosis patients were downloaded from the GEO database, and unsupervised clustering analysis was used to identify osteoporosis metabolic gene subtypes and machine learning to screen osteoporosis metabolism-related characteristic genes. Meanwhile, multi-omics enrichment was performed using the online Proteomaps tool, and the results were validated using external datasets GSE35959 and GSE7429. Finally, the immune and stromal cell types of the signature genes were inferred by the xCell method.

Results: Based on unsupervised cluster analysis, osteoporosis metabolic genotyping can be divided into three distinct subtypes: lipid and steroid metabolism subtypes, glycolysis-related subtypes, and polysaccharide subtypes. In addition, machine learning SVM identified 10 potentially metabolically related genes, GPR31, GATM, DDB2, ARMCX1, RPS6, BTBD3, ADAMTSL4, COQ6, B3GNT2, and CD9.

Conclusion: Based on the clustering analysis of gene expression in patients with osteoporosis and machine learning, we identified different metabolism-related subtypes and characteristic genes of osteoporosis, which will help to provide new ideas for the metabolism-related pathogenesis of osteoporosis and provide a new direction for follow-up research.

Identification of osteoporosis based on gene biomarkers using support vector machine

Osteoporosis is a major health concern worldwide. The present study aimed to identify effective biomarkers for osteoporosis detection. In osteoporosis, 559 differentially expressed genes (DEGs) were enriched in PI3K-Akt signaling pathway and Foxo signaling pathway. Weighted gene co-expression network analysis showed that green, pink, and tan modules were clinically significant modules, and that six genes (VEGFA, DDX5, SOD2, HNRNPD, EIF5B, and HSP90B1) were identified as “real” hub genes in the protein–protein interaction network, co-expression network, and 559 DEGs. The sensitivity and specificity of the support vector machine (SVM) for identifying patients with osteoporosis was 100%, with an area under curve of 1 in both training and validation datasets. Our results indicated that the current system using the SVM method could identify patients with osteoporosis.

The relationship between FOSB and SOCS3 gene polymorphisms and the susceptibility to periodontitis and osteopenia in the Chinese population

The aim is to analyze the relationship and significance of the FOS, FOSB, Suppressors of cytokine signaling (SOCS), and hypoxia-inducible factor 1 (HIF1) gene loci and their polymorphisms with periodontitis and osteopenia in the Chinese population. In this case-control study, data on questionnaires, periodontal examination, bone mineral density, and FOS, FOSB, SOCS3, and HIF1 gene loci and their polymorphisms were obtained from 474 participants. The data were analyzed using the analysis of variance, Kruskal-Wallis test, χ² test, and logistic regression. The incidence of osteopenia was significantly increased in patients with periodontitis compared to controls (58.6 vs. 34.4%, P < 0.001). Accordingly, the risk was increased 2.21-fold compared with controls (95% CI 2.09-4.95). Osteopenia patients had a significantly higher risk of periodontitis than patients with normal bone density (OR = 3.22, 95% CI 2.09-4.94). There were significant positive associations between FOSB and SOCS3 polymorphisms and periodontitis and osteopenia susceptibility. Individuals carrying the G/G genotype of the FOSB gene rs708905 locus had an increased risk of periodontitis (OR = 5.06, 95% CI 2.36-10.86) and osteopenia (OR = 3.26, 95% CI 1.34-7.96). Compared with the C/C genotype, the A/A genotype of the FOSB rs8105114 locus was associated with a significantly higher risk of periodontitis (OR = 2.14, 95% CI 1.02-4.53) and osteopenia (OR = 2.85, 95% CI 1.12-7.22). Compared with the A/A genotype, the risk of periodontitis in the G/G genotype of the SOCS3 rs7207782 locus was increased 3.10-fold (P < 0.001), and the risk of osteopenia was increased 2.01-fold (P = 0.023). There was a bidirectional relationship between periodontitis and osteopenia. The rs708905 G/G and rs8105114 A/A genotypes of FOSB and the rs7207782 G/G genotype of SOCS3 were risk factors for both periodontitis and osteopenia in the Chinese population, which could increase knowledge about disease‑specific and cross‑disease genetic pattern.

Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm

Background

Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.

Methods

Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes.

Results

GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset.

Conclusion

Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

Relationship between osteoporosis and benign paroxysmal positional vertigo based on evidence-based medicine and bioinformatics

It has been reported that osteoporosis is a possible risk factor of benign paroxysmal positional vertigo (BPPV).

PURPOSE

We analyzed the correlation between osteoporosis and BPPV and the possible mechanism by performing evidence-based medicine meta-analysis and bioinformatics analysis.

METHODS

Initially, English articles related to osteoporosis and BPPV were obtained through PubMed and EMBASE databases. Stata12.0 software was used for meta-analysis to calculate the odd ratio (OR) and 95% confidence interval (CI) of outcome indicators, and the heterogeneity was evaluated by subgroup analysis, publication bias evaluation, and sensitivity analysis. In addition, microarray datasets related to BPPV and osteoporosis were obtained from gene expression omnibus (GEO) database to screen differentially expressed genes. At last, a mouse model of osteoporosis was established by bilateral oophorectomy for validation. RT-qPCR and Western blot analysis were performed to determine expression of related factors in mouse tissues.

RESULTS

Osteoporosis was suggested as an important risk factor for BPPV through meta-analysis of these 12 articles. It was found that PPP2CA was upregulated in BPPV and low bone mineral density (BMD) samples. Moreover, PPP2CA induced dephosphorylation of BCL2, which may be involved in BPPV through regulation of BMD. Through this mechanism, silencing of PPP2CA could elevate the incidence of BPPV by promoting bone remodeling and reducing the density of otoconia around the macula.

CONCLUSIONS

PPP2CA reduces BMD expression by inducing dephosphorylation of BCL2, which may be one of the mechanisms responsible for the onset of BPPV in osteoporosis.

Network Pharmacology Integrated with Molecular Docking Explores the Mechanisms of Naringin against Osteoporotic Fracture by Regulating Oxidative Stress

Naringin (NG), as the most abundant component of Drynariae Rhizoma (Chinese name: Gusuibu), has been proved to be an antioxidant flavonoid on promoting osteoporotic fracture (OF) healing, but relevant research is scanty on the underlying mechanisms. We adopted target prediction, protein-protein interaction (PPI) analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and molecular docking to establish a system pharmacology database of NG against OF. Totally 105 targets of naringin were obtained, including 26 common targets with OF. A total of 415 entries were obtained through GO Biological Process enrichment analysis (P < 0.05), and 37 entries were obtained through KEGG pathway enrichment analysis with seven signaling pathways included (P < 0.05), which were primarily concerned with p53, IL-17, TNF, estrogen, and PPAR signaling pathways. According to the results of molecular docking, naringin is all bound in the active pockets of the core targets with 3-9 hydrogen bonds through some connections such as hydrophobic interactions, Pi-Pi stacked interactions, and salt bridge, demonstrating that naringin binds tightly to the core targets. In general, naringin may treat OF through multiple targets and multiple pathways via regulating oxidative stress, etc. Notably, it is first reported that NG may regulate osteoclast differentiation and oxidative stress through the expression of the core targets so as to treat OF.

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

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.

Neuroprotective Effects of Anti-high Mobility Group Box-1 Monoclonal Antibody Against Methamphetamine-Induced Dopaminergic Neurotoxicity

High mobility group box-1 (HMGB1) is a ubiquitous non-histone nuclear protein that plays a key role as a transcriptional activator, with its extracellular release provoking inflammation. Inflammatory responses are essential in methamphetamine (METH)-induced acute dopaminergic neurotoxicity. In the present study, we examined the effects of neutralizing anti-HMGB1 monoclonal antibody (mAb) on METH-induced dopaminergic neurotoxicity in mice. BALB/c mice received a single intravenous administration of anti-HMGB1 mAb prior to intraperitoneal injections of METH (4 mg/kg × 2, at 2-h intervals). METH injections induced hyperthermia, an increase in plasma HMGB1 concentration, degeneration of dopaminergic nerve terminals, accumulation of microglia, and extracellular release of neuronal HMGB1 in the striatum. These METH-induced changes were significantly inhibited by intravenous administration of anti-HMGB1 mAb. In contrast, blood-brain barrier disruption occurred by METH injections was not suppressed. Our findings demonstrated the neuroprotective effects of anti-HMGB1 mAb against METH-induced dopaminergic neurotoxicity, suggesting that HMGB1 could play an initially important role in METH toxicity.

Factors Associated with E-Cigarette Use in U.S. Young Adult Never Smokers of Conventional Cigarettes: A Machine Learning Approach

E-cigarette use is increasing among young adult never smokers of conventional cigarettes, but the awareness of the factors associated with e-cigarette use in this population is limited. The goal of this work was to use machine learning (ML) algorithms to determine the factors associated with current e-cigarette use among US young adult never cigarette smokers. Young adult (18-34 years) never cigarette smokers from the 2016 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) who reported current or never e-cigarette use were used for the analysis (n = 79,539). Variables associated with current e-cigarette use were selected by two ML algorithms (Boruta and Least absolute shrinkage and selection operator (LASSO)). Odds ratios were calculated to determine the association between e-cigarette use and the variables selected by the ML algorithms, after adjusting for age, gender and race/ethnicity and incorporating the BRFSS complex design. The prevalence of e-cigarette use varied across states. Factors previously reported in the literature, such as age, race/ethnicity, alcohol use, depression, as well as novel factors associated with e-cigarette use, such as disabilities, obesity, history of diabetes and history of arthritis were identified. These results can be used to generate further hypotheses for research, increase public awareness and help provide targeted e-cigarette education.

Cytokines CCL2 and CXCL1 may be potential novel predictors of early bone loss

Osteoporosis is a common disorder characterized by decreased bone mineral density (BMD) and increased fracture risk. The current techniques detect real-time BMD precisely but do not provide adequate information to predict early bone loss. If bone loss could be diagnosed and predicted early, severe osteoporosis and unexpected fractures could be prevented, allowing for an improved quality of life for individuals. In the present study, an ovariectomized rat model of bone loss was established and the serum levels of 78 potential cytokines were determined using a protein array. The BMD of ovariectomized rats was dynamically measured by micro-CT and the early stage of bone loss was defined at the fourth week after surgery. The expression of several serum protein cytokines was indicated to be altered in the ovariectomized rats during an 8-week time-course of bone loss. Linear regression analysis revealed that the serum levels of C-C motif chemokine ligand 2 (CCL2, also known as monocyte chemoattractant protein 1) and C-X-C motif chemokine ligand 1 (CXCL1) were significantly associated with a reduction in BMD. The significance of these two factors in indicating bone mass reduction was further verified by analyzing serum samples from 24 patients with BMD using ELISA and performing a linear regression analysis. The serum levels of CCL2 and CXCL1 were inversely correlated with the bone mass. Therefore, the cytokines CCL2 and CXCL1 may be potential novel predictors of early bone loss and may be clinically relevant for the early diagnosis and prevention of osteoporosis.

Tomatidine Alleviates Osteoporosis by Downregulation of p53

Background

As a common metabolic disorder, osteoporosis is characterized by decreasing bone mass density and increased possibility of fragility fracture. The incidence of senile osteoporosis increases year by year. There is no gold standard of treatment for osteoporosis. Tomatidine is the aglycone derivative of tomatine, having the ability to treat various diseases, including osteoporosis. However, the mechanism by which tomatidine improves osteoporosis has not been fully elucidated. Tomatidine is a potential and promising drug for osteoporosis.

Material/Methods

In this study, the KEGG pathways that tomatidine-targeted genes enriched in were obtained using bioinformatics methods. The KEGG pathways involved in osteoporosis that were also associated with tomatidine-targeted genes were selected. After analysis of these pathways, essential genes that may be involved in this biological process were identified and validated experimentally.

Results

We found 110 osteoporosis related KEGG pathways and 76 tomatidine-targeted genes-related KEGG pathways were obtained. 39 shared KEGG pathways were identified. The top 5 pathways were: pathway of chronic myeloid leukemia, pathway of B cell receptor signaling, pathway in cancer, bladder cancer pathway, and progesterone-mediated oocyte maturation pathway. MAPK1, MAP2K1, MAPK3, RAF1 were involved in all the 5 pathways. The p53 signaling pathway and the MAPK signaling pathway were involved in the 5 KEGG pathways. In vitro experiments showed that downregulating p53 expression could be potentially protective for osteoporosis.

Conclusions

Tomatidine can improve osteoporosis, and one of the mechanisms of its action is achieved by modulating p53. Tomatidine may be a promising drug for osteoporosis.

Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia

BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network analysis, to develop a diagnostic gene signature for the diagnosis of ventilator-associated pneumonia (VAP). MATERIAL AND METHODS GSE30385 from the Gene Expression Omnibus (GEO) database identified differentially expressed genes (DEGs) associated with patients with VAP. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment identified the molecular functions of the DEGs. The least absolute shrinkage and selection operator (LASSO) regression analysis algorithm was used to select key genes. Three modeling methods, including logistic regression analysis, random forest analysis, and fully-connected neural network analysis, also known as also known as the feed-forward multi-layer perceptron (MLP), were used to identify the diagnostic gene signature for patients with VAP. RESULTS Sixty-six DEGs were identified for patients who had VAP (VAP+) and who did not have VAP (VAP-). Ten essential or feature genes were identified. Upregulated genes included matrix metallopeptidase 8 (MMP8), arginase 1 (ARG1), haptoglobin (HP), interleukin 18 receptor 1 (IL18R1), and NLR family apoptosis inhibitory protein (NAIP). Down-regulated genes included complement factor D (CFD), pleckstrin homology-like domain family A member 2 (PHLDA2), plasminogen activator, urokinase (PLAU), laminin subunit beta 3 (LAMB3), and dual-specificity phosphatase 2 (DUSP2). Logistic regression, random forest, and MLP analysis showed receiver operating characteristic (ROC) curve area under the curve (AUC) values of 0.85, 0.86, and 0.87, respectively. CONCLUSIONS Logistic regression analysis, random forest analysis, and MLP analysis identified a ten-gene signature for the diagnosis of VAP.

Effects of hip replacement combined with alendronate sodium on postoperative healing of osteoporotic femoral neck fracture and levels of CTX-1 and BALP in patients

This study aimed to explore the improvement of hip replacement combined with alendronate sodium on the condition of patients with osteoporotic femoral neck fracture and factors affecting the efficacy of patients. In total, 140 patients with femoral neck fracture from July 2015 to October 2017 in the Affiliated Xuzhou Hospital of Jiangsu University were collected. Of these, 61 patients were treated with hip replacement as the control group and 79 patients were treated with alendronate sodium as the observation group on the basis of the control group. ELISA was used to detect levels of carboxy-terminal opeptide of type I collagen (CTX–I) and bone alkaline phosphatase (BALP) in serum of patients before and after treatment. Harris score was used to compare the clinical efficacy of patients after treatment. Changes in the expression of CTX–I and BALP before and after treatment were compared between the two groups, and the correlation between CTX–I and BALP levels and Harris score was analyzed. According to the clinical efficacy of patients, the two groups were divided into the significant effect group and poor effect group. Risk factors affecting the efficacy of patients were analyzed, and the ROC of subjects with risk factors was drawn. After treatment, the expression of BALP in serum increased significantly compared with that before treatment, and the expression of CTX–I decreased significantly. After treatment, the expression of BALP in serum in the observation group was significantly higher than that in the control group (P<0.05). Multivariate analysis revealed that age, time of operation, CTX–I after treatment and BALP after treatment were independent risk factors affecting the efficacy of patients. In conclusion, hip replacement combined with alendronate sodium can effectively improve the clinical efficacy of patients, and age, time of operation, CTX–I after treatment and BALP after treatment are found to be independent risk factors affecting the postoperative efficacy of patients.

Visfatin Connection: Present and Future in Osteoarthritis and Osteoporosis

Musculoskeletal pathologies (MSPs) such as osteoarthritis (OA) and osteoporosis (OP), are a set of disorders that cause severe pain, motion difficulties, and even permanent disability. In developed countries, the current incidence of MSPs reaches about one in four adults and keeps escalating as a consequence of aging and sedentarism. Interestingly, OA and OP have been closely related to similar risk factors, including aging, metabolic alterations, and inflammation. Visfatin, an adipokine with an inflammatory and catabolic profile, has been associated with several OA and OP metabolic risk factors, such as obesity, insulin resistance, and type II diabetes. Furthermore, visfatin has been associated with the innate immune receptor toll-like receptor 4 (TLR4), which plays a key role in cartilage and bone inflammatory and catabolic responses. Moreover, visfatin has been related to several OA and OP pathologic features. The aim of this work is to bring together basic and clinical data regarding the common role of visfatin in these pathologies and their major shared risk factors. Finally, we discuss the pitfalls of visfatin as a potential biomarker and therapeutic target in both pathologies.