Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome

Sex- and species-specific contribution of CD99 to T cell costimulation during multiple sclerosis

BACKGROUND

Differences in immune responses between women and men are leading to a strong sex bias in the incidence of autoimmune diseases that predominantly affect women, such as multiple sclerosis (MS). MS manifests in more than twice as many women, making sex one of the most important risk factor. However, it is incompletely understood which genes contribute to sex differences in autoimmune incidence. To address that, we conducted a gene expression analysis in female and male human spleen and identified the transmembrane protein CD99 as one of the most significantly differentially expressed genes with marked increase in men. CD99 has been reported to participate in immune cell transmigration and T cell regulation, but sex-specific implications have not been comprehensively investigated.

METHODS

In this study, we conducted a gene expression analysis in female and male human spleen using the Genotype-Tissue Expression (GTEx) project dataset to identify differentially expressed genes between women and men. After successful validation on protein level of human immune cell subsets, we assessed hormonal regulation of CD99 as well as its implication on T cell regulation in primary human T cells and Jurkat T cells. In addition, we performed in vivo assays in wildtype mice and in Cd99-deficient mice to further analyze functional consequences of differential CD99 expression.

RESULTS

Here, we found higher CD99 gene expression in male human spleens compared to females and confirmed this expression difference on protein level on the surface of T cells and pDCs. Androgens are likely dispensable as the cause shown by in vitro assays and ex vivo analysis of trans men samples. In cerebrospinal fluid, CD99 was higher on T cells compared to blood. Of note, male MS patients had lower CD99 levels on CD4+ T cells in the CSF, unlike controls. By contrast, both sexes had similar CD99 expression in mice and Cd99-deficient mice showed equal susceptibility to experimental autoimmune encephalomyelitis compared to wildtypes. Functionally, CD99 increased upon human T cell activation and inhibited T cell proliferation after blockade. Accordingly, CD99-deficient Jurkat T cells showed decreased cell proliferation and cluster formation, rescued by CD99 reintroduction.

CONCLUSIONS

Our results demonstrate that CD99 is sex-specifically regulated in healthy individuals and MS patients and that it is involved in T cell costimulation in humans but not in mice. CD99 could potentially contribute to MS incidence and susceptibility in a sex-specific manner.

Identification of potential biomarkers for aging diagnosis of mesenchymal stem cells derived from the aged donors

BACKGROUND

The clinical application of human bone-marrow derived mesenchymal stem cells (MSCs) for the treatment of refractory diseases has achieved remarkable results. However, there is a need for a systematic evaluation of the quality and safety of MSCs sourced from donors. In this study, we sought to assess one potential factor that might impact quality, namely the age of the donor.

METHODS

We downloaded two data sets from each of two Gene Expression Omnibus (GEO), GSE39035 and GSE97311 databases, namely samples form young (< 65 years of age) and old (> 65) donor groups. Through, bioinformatics analysis and experimental validation to these retrieved data, we found that MSCs derived from aged donors can lead to differential expression of gene profiles compared with those from young donors, and potentially affect the function of MSCs, and may even induce malignant tumors.

RESULTS

We identified a total of 337 differentially expressed genes (DEGs), including two upregulated and eight downregulated genes from the databases of both GSE39035 and GSE97311. We further identified 13 hub genes. Six of them, TBX15, IGF1, GATA2, PITX2, SNAI1 and VCAN, were highly expressed in many human malignancies in Human Protein Atlas database. In the MSCs in vitro senescent cell model, qPCR analysis validated that all six hub genes were highly expressed in senescent MSCs. Our findings confirm that aged donors of MSCs have a significant effect on gene expression profiles. The MSCs from old donors have the potential to cause a variety of malignancies. These TBX15, IGF1, GATA2, PITX2, SNAI1, VCAN genes could be used as potential biomarkers to diagnosis aging state of donor MSCs, and evaluate whether MSCs derived from an aged donor could be used for therapy in the clinic. Our findings provide a diagnostic basis for the clinical use of MSCs to treat a variety of diseases.

CONCLUSIONS

Therefore, our findings not only provide guidance for the safe and standardized use of MSCs in the clinic for the treatment of various diseases, but also provide insights into the use of cell regeneration approaches to reverse aging and support rejuvenation.

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people.

Identification of brain endothelial cell-specific genes and pathways in ischemic stroke by integrated bioinformatical analysis

BACKGROUND

Ischemic stroke (IS) is a life-threatening condition with limited treatment options; thus, finding the potential key genes for novel therapeutic targets is urgently needed. This study aimed to explore novel candidate genes and pathways of brain microvessel endothelial cells (ECs) in IS by bioinformatics analysis.

MATERIALS AND METHODS

The gene expression profiles of brain tissues or brain ECs in IS mice were downloaded from the online gene expression omnibus (GEO) to obtain the differentially expressed genes (DEGs) by R software. Functional enrichment analyses were used to cluster the functions and signaling pathways of the DEGs, while DEG-associated protein-protein interaction network was performed to identify hub genes. The target microRNAs and competitive endogenous RNA networks of key hub genes were constructed by Cytoscape.

RESULTS

Totally 84 DEGs were obtained from 6 brain tissue samples and 4 brain vascular EC samples both from IS mice in the datasets GSE74052 and GSE137482, with significant enrichment in immune responses, such as immune system processes and T-cell activation. Eight hub genes filtered by Cytoscape were validated by two other GEO datasets, wherein key genes of interest were verified by reverse transcription-polymerase chain reaction using an in vitro ischemic model of EC cultures. Our data indicated that AURKA and CENPF might be potential therapeutic target genes for IS, and Malat1/Snhg12/Xist-miR-297b-3p-CENPF, as well as Mir17 hg-miR-34b-3p-CENPF, might be RNA regulatory pathways to control IS progression.

CONCLUSIONS

Our work identified two brain EC-specific expressed genes in IS, namely, AURKA and CENPF, as potential gene targets for IS treatment. In addition, we presented miR-297b-3p/miR-34b-3p-CENPF as the potential RNA regulatory axes to prevent pathogenesis of IS.

Analysis of genetic variability in Turner syndrome linked to long-term clinical features

Background

Women with Turner syndrome (TS) (45,X and related karyotypes) have an increased prevalence of conditions such as diabetes mellitus, obesity, hypothyroidism, autoimmunity, hypertension, and congenital cardiovascular anomalies (CCA). Whilst the risk of developing these co-morbidities may be partly related to haploinsufficiency of key genes on the X chromosome, other mechanisms may be involved. Improving our understanding of underlying processes is important to develop personalized approaches to management.

Objective

We investigated whether: 1) global genetic variability differs in women with TS, which might contribute to co-morbidities; 2) common variants in X genes - on the background of haploinsufficiency - are associated with phenotype (a "two-hit" hypothesis); 3) the previously reported association of autosomal TIMP3 variants with CCA can be replicated.

Methods

Whole exome sequencing was undertaken in leukocyte DNA from 134 adult women with TS and compared to 46,XX controls (n=23), 46,XX women with primary ovarian insufficiency (n=101), and 46,XY controls (n=11). 1) Variability in autosomal and X chromosome genes was analyzed for all individuals; 2) the relation between common X chromosome variants and the long-term phenotypes listed above was investigated in a subgroup of women with monosomy X; 3) TIMP3 variance was investigated in relation to CCA.

Results

Standard filtering identified 6,457,085 autosomal variants and 126,335 X chromosome variants for the entire cohort, whereas a somatic variant pipeline identified 16,223 autosomal and 477 X chromosome changes. 1) Overall exome variability of autosomal genes was similar in women with TS and control/comparison groups, whereas X chromosome variants were proportionate to the complement of X chromosome material; 2) when adjusted for multiple comparisons, no X chromosome gene/variants were strongly enriched in monosomy X women with key phenotypes compared to monosomy X women without these conditions, although several variants of interest emerged; 3) an association between TIMP3 22:32857305:C-T and CCA was found (CCA 13.6%; non-CCA 3.4%, p<0.02).

Conclusions

Women with TS do not have an excess of genetic variability in exome analysis. No obvious X-chromosome variants driving phenotype were found, but several possible genes/variants of interest emerged. A reported association between autosomal TIMP3 variance and congenital cardiac anomalies was replicated.

Organ Abnormalities Caused by Turner Syndrome

Turner syndrome (TS), a genetic disorder due to incomplete dosage compensation of X-linked genes, affects multiple organ systems, leading to hypogonadotropic hypogonadism, short stature, cardiovascular and vascular abnormalities, liver disease, renal abnormalities, brain abnormalities, and skeletal problems. Patients with TS experience premature ovarian failure with a rapid decline in ovarian function caused by germ cell depletion, and pregnancies carry a high risk of adverse maternal and fetal outcomes. Aortic abnormalities, heart defects, obesity, hypertension, and liver abnormalities, such as steatosis, steatohepatitis, biliary involvement, liver cirrhosis, and nodular regenerative hyperplasia, are commonly observed in patients with TS. The SHOX gene plays a crucial role in short stature and abnormal skeletal phenotype in patients with TS. Abnormal structure formation of the ureter and kidney is also common in patients with TS, and a non-mosaic 45,X karyotype is significantly associated with horseshoe kidneys. TS also affects brain structure and function. In this review, we explore various phenotypic and disease manifestations of TS in different organs, including the reproductive system, cardiovascular system, liver, kidneys, brain, and skeletal system.

Bioinformatics analysis based on crucial genes of endothelial cells in rheumatoid

Objectives: Synovial neovascularization is an early and remarkable event that promotes the development of rheumatoid arthritis (RA) synovial hyperplasia. This study aimed to find potential diagnostic markers and molecular therapeutic targets for RA at the mRNA molecular level. Method: We download the expression profile dataset GSE46687 from the gene expression ontology (GEO) microarray, and used R software to screen out the differentially expressed genes between the normal group and the disease group. Then we performed functional enrichment analysis, used the STRING database to construct a protein-protein interaction (PPI) network, and identify candidate crucial genes, infiltration of the immune cells and targeted molecular drug. Results: Rheumatoid arthritis datasets included 113 differentially expressed genes (DEGs) including 104 upregulated and 9 downregulated DEGs. The enrichment analysis of genes shows that the differential genes are mainly enriched in condensed chromosomes, ribosomal subunits, and oxidative phosphorylation. Through PPI network analysis, seven crucial genes were identified: RPS13, RPL34, RPS29, RPL35, SEC61G, RPL39L, and RPL37A. Finally, we find the potential compound drug for RA. Conclusion: Through this method, the pathogenesis of RA endothelial cells was further explained. It provided new therapeutic targets, but the relationship between these genes and RA needs further research to be validated in the future.

Identifying potential biomarkers for the diagnosis and treatment of IgA nephropathy based on bioinformatics analysis

BACKGROUND

IgA nephropathy (IgAN) has become the leading cause of end-stage renal disease in young adults. Nevertheless, the current diagnosis exclusively relies on invasive renal biopsy, and specific treatment is deficient. Thus, our study aims to identify potential crucial genes, thereby providing novel biomarkers for the diagnosis and therapy of IgAN.

METHODS

Three microarray datasets were downloaded from GEO official website. Differentially expressed genes (DEGs) were identified by limma package. GO and KEGG analysis were conducted. Tissue/organ-specific DEGs were distinguished via BioGPS. GSEA was utilized to elucidate the predominant enrichment pathways. The PPI network of DEGs was established, and hub genes were mined through Cytoscape. The CTD database was employed to determine the association between hub genes and IgAN. Infiltrating immune cells and their relationship to hub genes were evaluated based on CIBERSORT. Furthermore, the diagnostic effectiveness of hub markers was subsequently predicted using the ROC curves. The CMap database was applied to investigate potential therapeutic drugs. The expression level and diagnostic accuracy of TYROBP was validated in the cell model of IgAN and different renal pathologies.

RESULTS

A total of 113 DEGs were screened, which were mostly enriched in peptidase regulator activity, regulation of cytokine production, and collagen-containing extracellular matrix. Among these DEGs, 67 genes manifested pronounced tissue and organ specificity. GSEA analysis revealed that the most significant enriched gene sets were involved in proteasome pathway. Ten hub genes (KNG1, FN1, ALB, PLG, IGF1, EGF, HRG, TYROBP, CSF1R, and ITGB2) were recognized. CTD showed a close connection between ALB, IGF, FN1 and IgAN. Immune infiltration analysis elucidated that IGF1, EGF, HRG, FN1, ITGB2, and TYROBP were closely associated with infiltrating immune cells. ROC curves reflected that all hub genes, especially TYROBP, exhibited a good diagnostic value for IgAN. Verteporfin, moxonidine, and procaine were the most significant three therapeutic drugs. Further exploration proved that TYROBP was not only highly expressed in IgAN, but exhibited high specificity for the diagnosis of IgAN.

CONCLUSIONS

This study may offer novel insights into the mechanisms involved in IgAN occurrence and progression and the selection of diagnostic markers and therapeutic targets for IgAN.

The Changing Face of Turner Syndrome

Turner syndrome (TS) is a condition in females missing the second sex chromosome (45,X) or parts thereof. It is considered a rare genetic condition and is associated with a wide range of clinical stigmata, such as short stature, ovarian dysgenesis, delayed puberty and infertility, congenital malformations, endocrine disorders, including a range of autoimmune conditions and type 2 diabetes, and neurocognitive deficits. Morbidity and mortality are clearly increased compared with the general population and the average age at diagnosis is quite delayed. During recent years it has become clear that a multidisciplinary approach is necessary toward the patient with TS. A number of clinical advances has been implemented, and these are reviewed. Our understanding of the genomic architecture of TS is advancing rapidly, and these latest developments are reviewed and discussed. Several candidate genes, genomic pathways and mechanisms, including an altered transcriptome and epigenome, are also presented.

Bioinformatics-integrated screening of systemic sclerosis-specific expressed markers to identify therapeutic targets

Background

Systemic sclerosis (SSc) is a rare autoimmune disease characterized by extensive skin fibrosis. There are no effective treatments due to the severity, multiorgan presentation, and variable outcomes of the disease. Here, integrated bioinformatics was employed to discover tissue-specific expressed hub genes associated with SSc, determine potential competing endogenous RNAs (ceRNA) regulatory networks, and identify potential targeted drugs.

Methods

In this study, four datasets of SSc were acquired. To identify the genes specific to tissues or organs, the BioGPS web database was used. For differentially expressed genes (DEGs), functional and enrichment analyses were carried out, and hub genes were screened and shown in a network of protein-protein interactions (PPI). The potential lncRNA-miRNA-mRNA ceRNA network was constructed using the online databases. The specifically expressed hub genes and ceRNA network were validated in the SSc mouse and in normal mice. We also used the receiver operating characteristic (ROC) curve to determine the diagnostic values of effective biomarkers in SSc. Finally, the Drug-Gene Interaction Database (DGIdb) identified specific medicines linked to hub genes.

Results

The pooled datasets identified a total of 254 DEGs. The tissue/organ-specifically expressed genes involved in this analysis are commonly found in the hematologic/immune system and bone/muscle tissue. The enrichment analysis of DEGs revealed the significant terms such as regulation of actin cytoskeleton, immune-related processes, the VEGF signaling pathway, and metabolism. Cytoscape identified six gene cluster modules and 23 hub genes. And 4 hub genes were identified, including Serpine1, CCL2, IL6, and ISG15. Consistently, the expression of Serpine1, CCL2, IL6, and ISG15 was significantly higher in the SSc mouse model than in normal mice. Eventually, we found that MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1 may be promising RNA regulatory pathways in SSc. Besides, ten potential therapeutic drugs associated with the hub gene were identified.

Conclusions

This study revealed tissue-specific expressed genes, SERPINE1, CCL2, IL6, and ISG15, as effective biomarkers and provided new insight into the mechanisms of SSc. Potential RNA regulatory pathways, including MALAT1-miR-206-CCL2, let-7a-5p-IL6, and miR-196a-5p-SERPINE1, contribute to our knowledge of SSc. Furthermore, the analysis of drug-hub gene interactions predicted TIPLASININ, CARLUMAB and BINDARIT as candidate drugs for SSc.

Bioinformatic analysis identifies the immunological profile of turner syndrome with different X chromosome origins

INTRODUCTION

Turner syndrome (TS) is a chromosomal disorder that affects phenotypic females who have one intact X chromosome and complete or partial absence of the second sex chromosome in association with one or more clinical manifestations. However, the immunological profile of TS with different X chromosome origins is incompletely understood.

METHODS

In this study, transcriptomic expression profiles of 26 TS (45,X) samples and 10 normal karyotype (46,XX) samples derived from GSE46687 cohort were employed. Differentially expressed immune-related genes (DEIRGs) between monosomy X TS patients with different X chromosome origins and normal females were investigated respectively. Subsequently, functional annotation, protein-protein interaction (PPI) network analysis, immunocyte infiltration evaluation, tissue-specific gene expression and Weighted gene co expression network analysis (WGCNA) were performed to explore the immunological characteristic in TS with different X chromosome origins.

RESULTS

34 and 52 DEIRGs were respectively identified in 45,Xm and 45,Xp patients compared with normal individuals. The identified DEIRGs in Xm group were significantly enriched in pathways associated with cancer. In Xp TS patients, the most enriched signals were immune response-related. A majority of genes involved in the above pathways were downregulated. PPI analysis identified 4 (FLT3, IL3RA, CSF2RA, PIK3R3) and 6 (PDGFRB, CSF2, IL5, PRL, CCL17 and IL2)hub genes for Xm and Xp groups, respectively. CIBERSORT results showed that the proportion of Tregs in the Xm group and the naive B cells and resting NK cells in the Xp group significantly increased, respectively. Tissue-specific expression results indicated that BDCA4+_dentritic cells and CD19+ B cells were the prominent specific expressed tissues in Xp patients. Results of WGCNA support the above analysis.

CONCLUSIONS

This study aims at studying the immunological characteristics of TS with different X chromosome origins. Pathways in cancer in Xm group and immune response in Xp group were suppressed. 4 and 6 hub IRGs were identified as biomarkers for Xm and Xp patients, respectively. B cells played important roles in Xp patients. Further studies are needed to draw more attention to the functional validation of these hub genes and the roles of B cells.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease

Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using "limma" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.

Upregulated Immunogenic Cell-Death-Associated Gene Signature Predicts Reduced Responsiveness to Immune-Checkpoint-Blockade Therapy and Poor Prognosis in High-Grade Gliomas

Background: Immunogenic cell death (ICD) has emerged as a potential mechanism mediating adaptive immune response and tumor immunity in anti-cancer treatment. However, the signature of ICD in high-grade gliomas (HGGs) remains largely unknown, and its relevance to immunotherapies is still undetermined. The purpose of this study is to identify ICD-associated genotypes in order to explore their relevance to tumor immunity, patient prognosis and therapeutic efficacy of immune checkpoint blockade (ICB) therapy in HGGs. Methods: Bulk RNA-seq data and clinical information on 169 and 297 patients were obtained from the Cancer Genome Atlas (TCGA) and China Glioma Genome Atlas (CGGA), respectively. The functional enrichment and characterization of ICD genotyping were detected, and the ICD prognostic signature prediction model was constructed using least absolute shrinkage and selection operator (LASSO) regression. The responsiveness to immunotherapy was predicted according to the scoring of the ICD prognostic signature. Results: The HGG patients with high ICD gene signature (C1) showed poor outcomes, increased activity of immune modulation and immune escape, high levels of immune-checkpoint markers, and HLA-related genes, which may explain their reduced response to ICB immunotherapy. A gene set of the ICD signature, composing FOXP3, IL6 LY96, MYD88 and PDIA3, showed an independent prognostic value in both the TCGA and the CGGA HGG cohort. Conclusions: Our in silico analyses identified the ICD gene signature in HGGs with potential implications for predicting the responsiveness to ICB immune therapy and patient outcomes.

GBP2 acts as a member of the interferon signalling pathway in lupus nephritis

Lupus nephritis (LN) is a common and serious clinical manifestation of systemic lupus erythematosus. However, the pathogenesis of LN is not fully understood. The currently available treatments do not cure the disease and appear to have a variety of side effects in the long term. The purpose of this study was to search for key molecules involved in the LN immune response through bioinformatics techniques to provide a reference for LN-specific targeted therapy. The GSE112943 dataset was downloaded from the Gene Expression Omnibus database, and 20 of the samples were selected for analysis. In total, 2330 differentially expressed genes were screened. These genes were intersected with a list of immune genes obtained from the IMMPORT immune database to obtain 128 differentially expressed immune-related genes. Enrichment analysis showed that most of these genes were enriched in the interferon signalling pathway. Gene set enrichment analysis revealed that the sample was significantly enriched for expression of the interferon signalling pathway. Further analysis of the core gene cluster showed that nine genes, GBP2, VCAM1, ADAR, IFITM1, BST2, MX2, IRF5, OAS1 and TRIM22, were involved in the interferon signalling pathway. According to our analysis, the guanylate binding protein 2 (GBP2), interferon regulatory factor 5 and 2′-5′-oligoadenylate synthetase 1 (OAS1) genes are involved in three interferon signalling pathways. At present, we do not know whether GBP2 is associated with LN. Therefore, this study focused on the relationship between GBP2 and LN pathogenesis. We speculate that GBP2 may play a role in the pathogenesis of LN as a member of the interferon signalling pathway. Further immunohistochemical results showed that the expression of GBP2 was increased in the renal tissues of LN patients compared with the control group, confirming this conjecture. In conclusion, GBP2 is a member of the interferon signalling pathway that may have implications for the pathogenesis of LN and serves as a potential biomarker for LN.

Identification of Potential Biomarkers for Ryanodine Receptor 1 (RYR1) Mutation-Associated Myopathies Using Bioinformatics Approach

Background

Myopathies related to Ryanodine receptor 1 (RYR1) mutation are the most common nondystrophy muscle disorder in humans. Early detection and diagnosis of RYR1 mutation-associated myopathies may lead to more timely treatment of patients, which contributes to the management and preparation for malignant hyperthermia. However, diagnosis of RYR1 mutation-associated myopathies is delayed and challenging. The absence of diagnostic morphological features in muscle biopsy does not rule out the possibility of pathogenic variations in RYR1. Accordingly, it is helpful to seek biomarkers to diagnose RYR1 mutation-associated myopathies.

Methods

Skeletal muscle tissue microarray datasets of RYR1 mutation-associated myopathies or healthy persons were built in accordance with the gene expression synthesis (GEO) database. Differentially expressed genes (DEGs) were identified on the basis of R software. Genes specific to tissue/organ were identified through BioGPS. An enrichment analysis of DEGs was conducted in accordance with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We also built protein-protein interaction (PPI) networks to explore the function and enrichment pathway of DEGs and the identification of hub genes. Lastly, the ROC curve was drawn for hub genes achieving specific expressions within skeletal muscle. Moreover, the area under the curve (AUC) was obtained to calculate the predictive value of key genes. The transcription factors of hub genes achieving specific expressions within skeletal muscle were predicted with the use of the iRegulon plugin.

Results

We identified 170 DEGs among 11 muscle biopsy samples of healthy subjects and 17 muscle biopsy samples of RYR1 mutation-associated myopathy patients in the dataset. Among the above DEGs, 30 genes achieving specific expressions within tissues/organs were found. GO and KEGG enrichment analysis of DEGs mainly focused on muscle contraction, actin-mediated cell contraction, actin filament-based movement, and muscular sliding. 12 hub genes were identified with the use of Cytoscape. Four hub genes were specifically expressed in skeletal muscle tissue, including MYH1 (AUC: 0.856), TNNT3 (AUC: 0.840), MYLPF (AUC: 0.786), and ATP2A1 (AUC: 0.765). The iRegulon predicted results suggested that the transcription factor MYF6 was found with the highest reliability.

Conclusions

Four skeletal muscle tissue-specific genes were identified, including MYH1, TNNT3, MYLPF, and ATP2A1, as the potential biomarkers for diagnosing and treating RYR1 mutation-associated myopathies, which provided insights into the transcriptome-level development mechanism. The transcription factor MYF6 may be a vital upstream regulator of the above biomarkers.

Turner Syndrome

Turner syndrome (TS) affects approximately 1 out of every 1500–2500 live female births, with clinical features including short stature, premature ovarian failure, dysmorphic features and other endocrine, skeletal, cardiovascular, renal, gastrointestinal and neurodevelopmental organ system involvement. TS, a common genetic syndrome, is caused by sex chromosome aneuploidy, mosaicism or abnormalities with complete or partial loss of function of the second X chromosome. Advances in genetic and genomic testing have further elucidated other possible mechanisms that contribute to pathogenic variability in phenotypic expression that are not necessarily explained by monosomy or haploinsufficiency of the X chromosome alone. The role of epigenetics in variations of gene expression and how this knowledge can contribute to more individualized therapy is currently being explored. TS is established as a multisystemic condition, with several endocrine manifestations of TS affecting growth, puberty and fertility having significant impact on quality of life. Treatment guidelines are in place for the management of these conditions; however, further data on optimal management is needed.

Integrative Analyses of Genes Associated With Otologic Disorders in Turner Syndrome

Background: Loss or partial loss of one X chromosome induces Turner syndrome (TS) in females, causing major medical concerns, including otologic disorders. However, the underlying genetic pathophysiology of otologic disorders in TS is mostly unclear.

Methods: Ear-related genes of TS (TSEs) were identified by analyzing differentially expressed genes (DEGs) in two Gene Expression Omnibus (GEO)-derived expression profiles and ear-genes in the Comparative Toxicogenomic Database (CTD). Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) analyses; Gene Set Enrichment Analysis (GSEA); and Gene Set Variation Analysis (GSVA) were adopted to study biological functions. Moreover, hub genes within the TSEs were identified by assessing protein-protein interaction (PPI), gene-microRNA, and gene-transcription factor (TF) networks. Drug-Gene Interaction Database (DGIdb) analysis was performed to predict molecular drugs for TS. Furthermore, three machine-learning analysis outcomes were comprehensively compared to explore optimal biomarkers of otologic disorders in TS. Finally, immune cell infiltration was analyzed.

Results: The TSEs included 30 significantly upregulated genes and 14 significantly downregulated genes. Enrichment analyses suggested that TSEs play crucial roles in inflammatory responses, phospholipid and glycerolipid metabolism, transcriptional processes, and epigenetic processes, such as histone acetylation, and their importance for inner ear development. Subsequently, we described three hub genes in the PPI network and confirmed their involvement in Wnt/β-catenin signaling pathway and immune cell regulation and roles in maintaining normal auditory function. We also constructed gene-microRNA and gene-TF networks. A novel biomarker (SLC25A6) of the pathogenesis of otologic disorders in TS was identified by comprehensive comparisons of three machine-learning analyses with the best predictive performance. Potential therapeutic agents in TS were predicted using the DGIdb. Immune cell infiltration analysis showed that TSEs are related to immune-infiltrating cells.

Conclusion: Overall, our findings have deepened the understanding of the pathophysiology of otologic disorders in TS and made contributions to present a promising biomarker and treatment targets for in-depth research.

Integrated RNA gene expression analysis identified potential immune-related biomarkers and RNA regulatory pathways of acute myocardial infarction

Background

Acute lesions are among the most important causes of death due to vascular lesions worldwide. However, there are no accurate genetic markers for Acute myocardial infarction (AMI). This project will use microarray integration analysis in bioinformatics analysis to find and validate relevant AMI gene markers.

Methods

Five microarray gene expression datasets were downloaded through the GEO database. We identified 50 significant DEGs by comparing and analyzing gene expression between 92 AMI and 57 standard samples. The BioGPS database screened differentially expressed genes specific to the immune system. DEGs were mainly involved in immune-related biological processes based on Enrichment analysis. Eight hub genes and three-gene cluster modules were subsequently screened using Cytoscape and validated using Box plot’s grouping comparison and ROC curves. Combined group comparison results and ROC curves analysis concluded that AQP9, IL1B, and IL1RN might be potential gene markers for the AMI process. We used the StarBase database to predict target miRNAs for eight essential genes. The expected results were used to screen and obtain target lncRNAs. Then Cytoscape was used to create CeRNA networks. By searching the literature in PubMed, we concluded that AQP9, IL1B, and IL1RN could be used as gene markers for AMI, while FSTL3-miR3303p-IL1B/IL1RN and ACSL4-miR5905p-IL1B could be used as RNA regulatory pathways affecting AMI disease progression.

Conclusions

Our study identified three genes that may be potential genetic markers for AMI’s early diagnosis and treatment. In addition, we suggest that FSTL3-miR-330-3p-IL1B/IL1RN and ACSL4-miR-590-5p-IL1B may be possible RNA regulatory pathways to control AMI disease progression.

Bioinformation Analysis Reveals IFIT1 as Potential Biomarkers in Central Nervous System Tuberculosis

Objective

Central nervous system tuberculosis is the most serious form of extrapulmonary tuberculosis. We aim to discover potential biomarkers involved in the development of the disease.

Methods

Through gene difference analysis, construction of a protein interaction network and tissue specific analysis and other bioinformatics analysis methods, we found out the relatively high expression of important substances in the central nervous system, interferon induced protein with tetratricopeptide repeats 1. Subsequently, the lesion tissue and the resection margin tissue away from the lesion were collected from the 6 cases of central nervous system tuberculosis patients diagnosed from 2019 to 2020, and the pathological manifestations were observed by Hematoxylin and Eosin (H&E) staining, and the expression of IFIT1 was verified by immunohistochemistry.

Results

A total of 101 differential genes were analyzed between extrapulmonary tuberculosis patients and normal people, and they were mainly enriched in the interferon pathway. The protein interaction network unearthed 34 key genes. Through tissue specific analysis, it was found that IFIT1 is relatively high in the central nervous system. H&E staining showed the expression of multinucleated macrophages, and immunohistochemistry showed that IFIT1 was significantly positively expressed in the lesion tissue.

Conclusion

IFIT1 is an important substance involved in central nervous system tuberculosis.

Exploration of the core genes in ulcerative interstitial cystitis/bladder pain syndrome

Objective:

Interstitial cystitis (IC)/bladder pain syndrome (BPS) is a chronic inflammatory disease that can cause bladder pain and accompanying symptoms, such as long-term urinary frequency and urgency. IC/BPS can be ulcerative or non-ulcerative. The aim of this study was to explore the core genes involved in the pathogenesis of ulcerative IC, and thus the potential biomarkers for clinical treatment.

Materials and Methods:

First, the gene expression dataset GSE11783 was downloaded using the Gene Expression Omnibus (GEO) database and analyzed using the limma package in R to identify differentially expressed genes (DEGs). Then, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for Gene Ontology (GO) functional analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis. Finally, the protein-protein interaction (PPI) network was constructed, and key modules and hub genes were determined using the STRING and Cytoscape software. The resulting key modules were then analyzed for tissue-specific gene expression using BioGPS.

Results:

A total of 216 up-regulated DEGs and 267 down-regulated genes were identified, and three key modules and nine hub genes were obtained.

Conclusion:

The core genes (CXCL8, CXCL1, IL6) obtained in this study may be potential biomarkers of interstitial cystitis with guiding significance for clinical treatment.

Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis

BACKGROUND

Rheumatoid arthritis (RA) is the most common chronic autoimmune connective tissue disease. However, early RA is difficult to diagnose due to the lack of effective biomarkers. This study aimed to identify new biomarkers and mechanisms for RA disease progression at the transcriptome level through a combination of microarray and bioinformatics analyses.

METHODS

Microarray datasets for synovial tissue in RA or osteoarthritis (OA) were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified by R software. Tissue/organ-specific genes were recognized by BioGPS. Enrichment analyses were performed and protein-protein interaction (PPI) networks were constructed to understand the functions and enriched pathways of DEGs and to identify hub genes. Cytoscape was used to construct the co-expressed network and competitive endogenous RNA (ceRNA) networks. Biomarkers with high diagnostic value for the early diagnosis of RA were validated by GEO datasets. The ggpubr package was used to perform statistical analyses with Student's t-test.

RESULTS

A total of 275 DEGs were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. Among these DEGs, 71 tissue/organ-specific expressed genes were recognized. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that DEGs are mostly enriched in immune response, immune-related biological process, immune system, and cytokine signal pathways. Fifteen hub genes and gene cluster modules were identified by Cytoscape. Eight haematologic/immune system-specific expressed hub genes were verified by GEO datasets. GZMA, PRC1, and TTK may be potential biomarkers for diagnosis of early RA. NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158- miR-212-3p/miR-132-3p/miR-129-5p-TTK might be potential RNA regulatory pathways to regulate the disease progression of early RA.

CONCLUSIONS

This work identified three haematologic/immune system-specific expressed genes, namely, GZMA, PRC1, and TTK, as potential biomarkers for the early diagnosis and treatment of RA and provided insight into the mechanisms of disease development in RA at the transcriptome level. In addition, we proposed that NEAT1-miR-212-3p/miR-132-3p/miR-129-5p-TTK, XIST-miR-25-3p/miR-129-5p-GZMA, and TTK_hsa_circ_0077158-miR-212-3p/miR-132-3p/miR-129-5p-TTK are potential RNA regulatory pathways that control disease progression in early RA.

Effects of Psychosomatic Mutual Aid Treatment on Anxiety and Depression in Turner Syndrome

Background: Turner syndrome (TS) affects approximately one out of 2,500 females. Previous research indicates that girls with TS experience psychosocial impairment in addition to their physical health issues. However, there is no current data demonstrating whether reducing the clinical symptoms of girls or women with TS through hormone replacement therapy (HRT) combined with psychological interventions, referred to as psychosomatic mutual aid treatment (PMAT), improves physical and psychological self-identification, so that psychological problems such as anxiety, depression, low self-esteem, social loneliness, and psychological resilience are improved. Therefore, the objective of this research was to assess the efficacy of PMAT on anxiety and depression in girls and women with TS. Methods: Twenty-six girls and women with TS aged 11-29 years (17.5 ± 4.2 years) were recruited. Anxiety and depression were assessed using Hamilton Anxiety Rating Scale (HAMA) and Zung Self-Rating Depression Scale (SDS) questionnaires, respectively. The 26 TS patients were surveyed for anxiety and depression before the beginning of PMAT and again in January 2020. In addition, 20 healthy volunteer women aged 16-39 years (23.1 ± 5.7 years) were selected as the control group and filled in the questionnaire. Results: Pre-therapy (pre-HRT and Pre-PMAT) there were significant differences between the TS patients (n = 26) and healthy controls (n = 20). In particular, the TS patients had higher anxiety status (P = 0.04) and severity (P = 0.03) (HAMA score), as well as depression status (P = 0.002) and severity (P < 0.001) (SDS score). Post-therapy there was no longer any difference in depression scores, but TS patients still had higher levels of anxiety post-therapy compared with healthy control women (psychic symptoms score, P = 0.03; anxiety status score, P = 0.04; anxiety severity score, P = 0.04). In the TS patients, there was an improvement in depression scores (SDS score P < 0.001; depression severity score, P = 0.005) after therapy but no change in levels of anxiety. Conclusions: PMAT significantly improves depression status, but not anxiety, in girls and women with TS. Clinical Trial Registration: http://www.chictr.org.cn/showproj.aspx?proj=124736, identifier: ChiCTR2100045230.

Identification of Potential Genes in Pathogenesis and Diagnostic Value Analysis of Partial Androgen Insensitivity Syndrome Using Bioinformatics Analysis

BACKGROUND

Androgen insensitivity syndrome (AIS) is a rare X-linked genetic disease and one of the causes of 46,XY disorder of sexual development. The unstraightforward diagnosis of AIS and the gender assignment dilemma still make a plague for this disorder due to the overlapping clinical phenotypes.

METHODS

Peripheral blood mononuclear cells (PBMCs) of partial AIS (PAIS) patients and healthy controls were separated, and RNA-seq was performed to investigate transcriptome variance. Then, tissue-specific gene expression, functional enrichment, and protein-protein interaction (PPI) network analyses were performed; and the key modules were identified. Finally, the RNA expression of differentially expressed genes (DEGs) of interest was validated by quantitative real-time PCR (qRT-PCR).

RESULTS

In our dataset, a total of 725 DEGs were captured, with functionally enriched reproduction and immune-related pathways and Gene Ontology (GO) functions. The most highly specific systems centered on hematologic/immune and reproductive/endocrine systems. We finally filtered out CCR1, PPBP, PF4, CLU, KMT2D, GP6, and SPARC by the key gene clusters of the PPI network and manual screening of tissue-specific gene expression. These genes provide novel insight into the pathogenesis of AIS in the immune system or metabolism and bring forward possible molecular markers for clinical screening. The qRT-PCR results showed a consistent trend in the expression levels of related genes between PAIS patients and healthy controls.

CONCLUSION

The present study sheds light on the molecular mechanisms underlying the pathogenesis and progression of AIS, providing potential targets for diagnosis and future investigation.

Identification of Three Significant Genes Associated with Immune Cells Infiltration in Dysfunctional Adipose Tissue-Induced Insulin-Resistance of Obese Patients via Comprehensive Bioinformatics Analysis

BACKGROUND

Low-grade chronic inflammation in dysfunctional adipose tissue links obesity with insulin resistance through the activation of tissue-infiltrating immune cells. Numerous studies have reported on the pathogenesis of insulin-resistance. However, few studies focused on genes from genomic database. In this study, we would like to explore the correlation of genes and immune cells infiltration in adipose tissue via comprehensive bioinformatics analyses and experimental validation in mice and human adipose tissue.

METHODS

Gene Expression Omnibus (GEO) datasets (GSE27951, GSE55200, and GSE26637) of insulin-resistant individuals or type 2 diabetes patients and normal controls were downloaded to get differently expressed genes (DEGs), and GO and KEGG pathway analyses were performed. Subsequently, we integrated DEGs from three datasets and constructed commonly expressed DEGs' PPI net-works across datasets. Center regulating module of DEGs and hub genes were screened through MCODE and cytoHubba in Cytoscape. Three most significant hub genes were further analyzed by GSEA analysis. Moreover, we verified the predicted hub genes by performing RT qPCR analysis in animals and human samples. Besides, the relative fraction of 22 immune cell types in adipose tissue was detected by using the deconvolution algorithm of CIBERSORT (Cell Type Identification by Estimating Relative Subsets of RNA Transcripts). Furthermore, based on the significantly changed types of immune cells, we performed correlation analysis between hub genes and immune cells. And, we performed immunohistochemistry and immunofluorescence analysis to verify that the hub genes were associated with adipose tissue macrophages (ATM).

RESULTS

Thirty DEGs were commonly expressed across three datasets, most of which were upregulated. DEGs mainly participated in the process of multiple immune cells' infiltration. In protein-protein interaction network, we identified CSF1R, C1QC, and TYROBP as hub genes. GSEA analysis suggested high expression of the three hub genes was correlated with immune cells functional pathway's activation. Immune cell infiltration and correlation analysis revealed that there were significant positive correlations between TYROBP and M0 macrophages, CSF1R and M0 macrophages, Plasma cells, and CD8 T cells. Finally, hub genes were associated with ATMs infiltration by experimental verification.

CONCLUSIONS

This article revealed that CSF1R, C1QC, and TYROBP were potential hub genes associated with immune cells' infiltration and the function of proinflammation, especially adipose tissue macrophages, in the progression of obesity-induced diabetes or insulin-resistance.

Pulmonary toxicity and RNA sequencing analyses of mouse in response to exposure to cellulose nanofibrils

OBJECTIVE

The growing applications of nanocelluloses in the fields of advanced nanocomposites, electronics, and medical devices necessitate investigation of their potential adverse effects on human health. The lungs are the primary and the most important route for the entry of nanocelluloses into the human body in occupational settings. However, data on the pulmonary toxicity of cellulose nanofibrils (CNFs) and its molecular mechanism are limited. This study investigated the pulmonary toxicity of CNFs and its genomic expression using the RNA sequencing approach.

MATERIALS AND METHODS

Female C57BL/6 mice were administered CNFs at 50 μg/mouse by oropharyngeal aspiration. Samples were collected at 3 and 14 days after exposure to CNFs (DAEC).

RESULTS

At three DAEC, the microscopic sections of lungs revealed a significant inflammatory response. In terms of gene expression alterations, 94 genes were up-regulated, and 107 genes were down-regulated. Most of these differentially expressed genes were involved in the inflammatory and immune responses, including chemokines, NK cells, killer cell lectin-like receptors, CD antigens, T cell-specific GTPases, immunity-related GTPase family M members, and interferon-induced proteins encoding genes. However, only 9 and 26 genes at 14 DAEC were significantly up- and down-regulated, respectively.

CONCLUSIONS

The pathological analysis of lung sections and the analysis of sequencing data suggested that the homeostasis of mice lungs was restored at 14 DAEC. The findings of this study provide insights into the pulmonary toxicity, and underlying toxicological mechanisms, caused by exposure to CNFs, and are useful for the assessment of the potential toxicity of nanocelluloses.

Identification of common differentially expressed genes in Turner (45,X) and Klinefelter (47,XXY) syndromes using bioinformatics analysis

Background

Analysis of patients with chromosomal abnormalities, including Turner syndrome and Klinefelter syndrome, has highlighted the importance of X‐linked gene dosage as a contributing factor for disease susceptibility. Escape from X‐inactivation and X‐linked imprinting can result in transcriptional differences between normal men and women as well as in patients with sex chromosome abnormalities.

Objective

To identify differentially expressed genes among patients with Turner (45,X) and Klinefelter (46,XXY) syndrome using bioinformatics analysis.

Methodology

Two gene expression data sets of Turner (45,X) and Klinefelter syndrome (47,XXY) were obtained from the Gene Omnibus Expression (GEO) database of the National Center for Biotechnology Information (NCBI). Statistical analysis was performed using R Bioconductor libraries. Differentially expressed genes (DEGs) were determined using significance analysis of microarray (SAM). The functional annotation of the DEGs was performed with DAVID v6.8 (The Database for Annotation, Visualization, and Integrated Discovery).

Results

There are no genes over‐expressed simultaneously in both diseases. However, when crossing the list of under‐expressed genes for 45,X cells and the list of over‐expressed genes for 47,XXY cells, there are 16 common genes: SLC25A6, AKAP17A, ASMTL, KDM5C, KDM6A, ATRX, CSF2RA, DHRSX, CD99, ZBED1, EIF1AX, MVB12B, SMC1A, P2RY8, DOCK7, DDX3X, eight of which are involved in the regulation of gene expression by epigenetic mechanisms, regulation of splicing processes and protein synthesis.

Conclusion

Of the 16 identified as under‐expressed in 45,X cells and over‐expressed in 47,XXY cells, 14 are located in X chromosome and 2 in autosomal chromosome; 8 of these genes are involved in the regulation of gene expression: 5 genes are related to epigenetic mechanisms, 2 in regulation of splicing processes, and 1 in the protein synthesis process. Our results are limited by it being the product of a bioinformatic analysis from mRNA isolated from whole blood, this makes necessary further exploration of the relationships between these genes and Turner syndrome and Klinefelter syndrome in the future.

Integrative Analyses of Genes Associated With Osteoporosis in CD16+ Monocyte

BACKGROUND

Osteoporosis is a metabolic bone disease characterized by decreased bone mineral density and abnormal bone quality. Monocytes can secret cytokines for bone resorption, resulting in bone mass loss. However, the mechanism by which monocytes subpopulations lead to osteoporosis remains unclear. The aim of this study was to identify genes associated with osteoporosis in monocytes subsets.

METHODS

Three microarray datasets including GSE7158 (transcription of low/high-peak bone mass), GSE101489 (transcription of CD16+/CD16- monocyte) and GSE93883 (miRNA expression profile of primary osteoporosis) were derived from the Gene Expression Omnibus (GEO) database and analyzed with GEO2R tool to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using Metascape database and GSEA software. STRING was utilized for the Protein-Protein Interaction Network construct. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in miRWalk, miRDB, and TargetScan databases.

RESULTS

Total 368 DEGs from GSE7158 were screened out, which were mostly enriched in signaling, positive regulation of biological process and immune system process. The hub genes were clustered into two modules by PPI network analysis. We identified 15 overlapping DGEs between GSE101489 and GSE7158 microarray datasets. Moreover, all of them were up-regulated genes in both datasets. Then, nine key genes were screened out from above 15 overlapping DEGs using Cytoscape software. It is a remarkable fact that the nine genes were all in one hub gene module of GSE7158. Additionally, 183 target miRNAs were predicted according to the above nine DEGs. After cross-verification with miRNA express profile dataset for osteoporosis (GSE93883), 12 DEmiRNAs were selected. Finally, a miRNA-mRNA network was constructed with the nine key genes and 12 miRNAs, which were involved in osteoporosis.

CONCLUSION

Our analysis results constructed a gene expression framework in monocyte subsets for osteoporosis. This approach could provide a novel insight into osteoporosis.