miRNA-gene Regulatory Network Research Articles

Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and sarcopenia.

Sarcopenia is a condition characterized by age-related loss of muscle mass and strength. Increasing evidence suggests that patients with sarcopenia have higher rates of coronavirus 2019 (COVID-19) infection and poorer post-infection outcomes. However, the exact mechanism and connections between the two is unknown. In this study, we used high-throughput data from the GEO database for sarcopenia (GSE111016) and COVID-19 (GSE171110) to identify common differentially expressed genes (DEGs). We conducted GO and KEGG pathway analyses, as well as PPI network analysis on these DEGs. Using seven algorithms from the Cytoscape plug-in cytoHubba, we identified 15 common hub genes. Further analyses included enrichment, PPI interaction, TF-gene and miRNA-gene regulatory networks, gene-disease associations, and drug prediction. Additionally, we evaluated immune cell infiltration with CIBERSORT and assessed the diagnostic accuracy of hub genes for sarcopenia and COVID-19 using ROC curves. In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19. GO and KEGG analyses revealed functions and pathways between the two diseases. TF-genes and TF-miRNA regulatory network suggest that FOXOC1 and hsa-mir-155-5p may be identified as key regulators, while gene-disease analysis showed strong correlations with hub genes in schizophrenia and bipolar disorder. Immune infiltration showed a correlation between the degree of immune infiltration and the level of infiltration of different immune cell subpopulations of hub genes in different datasets. The ROC curves for ALDH1L2 and KLF5 genes demonstrated their potential as diagnostic markers for both sarcopenia and COVID-19. This study suggests that sarcopenia and COVID-19 may share pathogenic pathways, and these pathways and hub genes offer new targets and strategies for early diagnosis, effective treatment, and tailored therapies for sarcopenia patients with COVID-19.

MiRNA Expression Profiles in Isolated Ventricular Cardiomyocytes: Insights into Doxorubicin-Induced Cardiotoxicity.

Doxorubicin (DOX), widely used as a chemotherapeutic agent for various cancers, is limited in its clinical utility by its cardiotoxic effects. Despite its widespread use, the precise mechanisms underlying DOX-induced cardiotoxicity at the cellular and molecular levels remain unclear, hindering the development of preventive and early detection strategies. To characterize the cytotoxic effects of DOX on isolated ventricular cardiomyocytes, focusing on the expression of specific microRNAs (miRNAs) and their molecular targets associated with endogenous cardioprotective mechanisms such as the ATP-sensitive potassium channel (KATP), Sirtuin 1 (SIRT1), FOXO1, and GSK3β. We isolated Guinea pig ventricular cardiomyocytes by retrograde perfusion and enzymatic dissociation. We assessed cell morphology, Reactive Oxygen Species (ROS) levels, intracellular calcium, and mitochondrial membrane potential using light microscopy and specific probes. We determined the miRNA expression profile using small RNAseq and validated it using stem-loop qRT-PCR. We quantified mRNA levels of some predicted and validated molecular targets using qRT-PCR and analyzed protein expression using Western blot. Exposure to 10 µM DOX resulted in cardiomyocyte shortening, increased ROS and intracellular calcium levels, mitochondrial membrane potential depolarization, and changes in specific miRNA expression. Additionally, we observed the differential expression of KATP subunits (ABCC9, KCNJ8, and KCNJ11), FOXO1, SIRT1, and GSK3β molecules associated with endogenous cardioprotective mechanisms. Supported by miRNA gene regulatory networks and functional enrichment analysis, these findings suggest that DOX-induced cardiotoxicity disrupts biological processes associated with cardioprotective mechanisms. Further research must clarify their specific molecular changes in DOX-induced cardiac dysfunction and investigate their diagnostic biomarkers and therapeutic potential.

Single-cell and transcriptomic analyses reveal the influence of diabetes on ovarian cancer.

There has been a significant surge in the global prevalence of diabetes mellitus (DM), which increases the susceptibility of individuals to ovarian cancer (OC). However, the relationship between DM and OC remains largely unexplored. The objective of this study is to provide preliminary insights into the shared molecular regulatory mechanisms and potential biomarkers between DM and OC. Multiple datasets from the GEO database were utilized for bioinformatics analysis. Single cell datasets from the GEO database were analysed. Subsequently, immune cell infiltration analysis was performed on mRNA expression data. The intersection of these datasets yielded a set of common genes associated with both OC and DM. Using these overlapping genes and Cytoscape, a protein‒protein interaction (PPI) network was constructed, and 10 core targets were selected. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then conducted on these core targets. Additionally, advanced bioinformatics analyses were conducted to construct a TF-mRNA-miRNA coregulatory network based on identified core targets. Furthermore, immunohistochemistry staining (IHC) and real-time quantitative PCR (RT-qPCR) were employed for the validation of the expression and biological functions of core proteins, including HSPAA1, HSPA8, SOD1, and transcription factors SREBF2 and GTAT2, in ovarian tumors. The immune cell infiltration analysis based on mRNA expression data for both DM and OC, as well as analysis using single-cell datasets, reveals significant differences in mononuclear cell levels. By intersecting the single-cell datasets, a total of 119 targets related to mononuclear cells in both OC and DM were identified. PPI network analysis further identified 10 hub genesincludingHSP90AA1, HSPA8, SNRPD2, UBA52, SOD1, RPL13A, RPSA, ITGAM, PPP1CC, and PSMA5, as potential targets of OC and DM. Enrichment analysis indicated that these genes are primarily associated with neutrophil degranulation, GDP-dissociation inhibitor activity, and the IL-17 signaling pathway, suggesting their involvement in the regulation of the tumor microenvironment. Furthermore, the TF-gene and miRNA-gene regulatory networks were validated using NetworkAnalyst. The identified TFs included SREBF2, GATA2, and SRF, while the miRNAs included miR-320a, miR-378a-3p, and miR-26a-5p. Simultaneously, IHC and RT-qPCR reveal differential expression of core targets in ovarian tumors after the onset of diabetes. RT-qPCR further revealed that SREBF2 and GATA2 may influence the expression of core proteins, including HSP90AA1, HSPA8, and SOD1. This study revealed the shared gene interaction network between OC and DM and predicted the TFs and miRNAs associated with core genes in monocytes. Our research findings contribute to identifying potential biological mechanisms underlying the relationship between OC and DM.

A Machine Learning Approach to Identify Potential miRNA-Gene Regulatory Network Contributing to the Pathogenesis of SARS-CoV-2 Infection.

Worldwide, many lives have been lost in the recent outbreak of coronavirus disease. The pathogen responsible for this disease takes advantage of the host machinery to replicate itself and, in turn, causes pathogenesis in humans. Human miRNAs are seen to have a major role in the pathogenesis and progression of viral diseases. Hence, an in-silico approach has been used in this study to uncover the role of miRNAs and their target genes in coronavirus disease pathogenesis. This study attempts to perform the miRNA seq data analysis to identify the potential differentially expressed miRNAs. Considering only the experimentally proven interaction databases TarBase, miRTarBase, and miRecords, the target genes of the miRNAs have been identified from the mirNET analytics platform. The identified hub genes were subjected to gene ontology and pathway enrichment analysis using EnrichR. It is found that a total of 9 miRNAs are deregulated, out of which 2 were upregulated (hsa-mir-3614-5p and hsa-mir-3614-3p) and 7 were downregulated (hsa-mir-17-5p, hsa-mir-106a-5p, hsa-mir-17-3p, hsa-mir-181d-5p, hsa-mir-93-3p, hsa-mir-28-5p, and hsa-mir-100-5p). These miRNAs help us to classify the diseased and healthy control patients accurately. Moreover, it is also found that crucial target genes (UBC and UBB) of 4 signature miRNAs interact with viral replicase polyprotein 1ab of SARS-Coronavirus. As a result, it is noted that the virus hijacks key immune pathways like various cancer and virus infection pathways and molecular functions such as ubiquitin ligase binding and transcription corepressor and coregulator binding.

Integrated Bioinformatics Investigation of Novel Biomarkers of Uterine Leiomyosarcoma Diagnosis and Outcome.

Uterine leiomyosarcomas (uLMS) have a poor prognosis and a high percentage of recurrent disease. Bioinformatics has become an integral element in rare cancer studies by overcoming the inability to collect a large enough study population. This study aimed to investigate and highlight crucial genes, pathways, miRNAs, and transcriptional factors (TF) on uLMS samples from five Gene Expression Omnibus datasets and The Cancer Genome Atlas Sarcoma study. Forty-one common differentially expressed genes (DEGs) were enriched and annotated by the DAVID software. With protein-protein interaction (PPI) network analysis, we selected ten hub genes that were validated with the TNMplotter web tool. We used the USCS Xena browser for survival analysis. We also predicted TF-gene and miRNA-gene regulatory networks along with potential drug molecules. TYMS and TK1 correlated with overall survival in uLMS patients. Finally, our results propose further validation of hub genes (TYMS and TK1), miR-26b-5p, and Sp1 as biomarkers of pathogenesis, prognosis, and differentiation of uLMS. Regarding the aggressive behavior and poor prognosis of uLMS, with the lack of standard therapeutic regimens, in our opinion, the results of our study provide enough evidence for further investigation of the molecular basis of uLMS occurrence and its implication in the diagnosis and therapy of this rare gynecological malignancy.

Genome-wide profiling of miRNA-gene regulatory networks in mouse postnatal heart development-implications for cardiac regeneration.

After birth, mammalian cardiomyocytes substantially lose proliferative capacity with a concomitant switch from glycolytic to oxidative mitochondrial energy metabolism. Micro-RNAs (miRNAs) regulate gene expression and thus control various cellular processes. Their roles in the postnatal loss of cardiac regeneration are however still largely unclear. Here, we aimed to identify miRNA-gene regulatory networks in the neonatal heart to uncover role of miRNAs in regulation of cell cycle and metabolism. We performed global miRNA expression profiling using total RNA extracted from mouse ventricular tissue samples collected on postnatal day 1 (P01), P04, P09, and P23. We used the miRWalk database to predict the potential target genes of differentially expressed miRNAs and our previously published mRNA transcriptomics data to identify verified target genes that showed a concomitant differential expression in the neonatal heart. We then analyzed the biological functions of the identified miRNA-gene regulatory networks using enriched Gene Ontology (GO) and KEGG pathway analyses. Altogether 46 miRNAs were differentially expressed in the distinct stages of neonatal heart development. For twenty miRNAs, up- or downregulation took place within the first 9 postnatal days thus correlating temporally with the loss of cardiac regeneration. Importantly, for several miRNAs, including miR-150-5p, miR-484, and miR-210-3p there are no previous reports about their role in cardiac development or disease. The miRNA-gene regulatory networks of upregulated miRNAs negatively regulated biological processes and KEGG pathways related to cell proliferation, while downregulated miRNAs positively regulated biological processes and KEGG pathways associated with activation of mitochondrial metabolism and developmental hypertrophic growth. This study reports miRNAs and miRNA-gene regulatory networks with no previously described role in cardiac development or disease. These findings may help in elucidating regulatory mechanism of cardiac regeneration and in the development of regenerative therapies.

Bioinformatics analysis of the pathogenic link between Epstein-Barr virus infection, systemic lupus erythematosus and diffuse large B cell lymphoma

Epstein-Barr virus (EBV) is a risk factor for diffuse large B-cell lymphoma (DLBCL) and systemic lupus erythematosus (SLE). While prior research has suggested a potential correlation between SLE and DLBCL, the molecular mechanisms remain unclear. The present study aimed to explore the contribution of EBV infection to the pathogenesis of DLBCL in the individuals with SLE using bioinformatics approaches. The Gene Expression Omnibus database was used to compile the gene expression profiles of EBV-infected B cells (GSE49628), SLE (GSE61635), and DLBCL (GSE32018). Altogether, 72 shared common differentially expressed genes (DEGs) were extracted and enrichment analysis of the shared genes showed that p53 signaling pathway was a common feature of the pathophysiology. Six hub genes were selected using protein–protein interaction (PPI) network analysis, including CDK1, KIF23, NEK2, TOP2A, NEIL3 and DEPDC1, which showed preferable diagnostic values for SLE and DLBCL and involved in immune cell infiltration and immune responses regulation. Finally, TF-gene and miRNA-gene regulatory networks and 10 potential drugs molecule were predicted. Our study revealed the potential molecular mechanisms by which EBV infection contribute to the susceptibility of DLBCL in SLE patients for the first time and identified future biomarkers and therapeutic targets for SLE and DLBCL.

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.

Comparative co-expression analysis of RNA-Seq transcriptome revealing key genes, miRNA and transcription factor in distinct metabolic pathways in diabetic nerve, eye, and kidney disease.

Diabetes and its related complications are associated with long term damage and failure of various organ systems. The microvascular complications of diabetes considered in this study are diabetic retinopathy, diabetic neuropathy, and diabetic nephropathy. The aim is to identify the weighted co-expressed and differentially expressed genes (DEGs), major pathways, and their miRNA, transcription factors (TFs) and drugs interacting in all the three conditions. The primary goal is to identify vital DEGs in all the three conditions. The overlapped five genes (AKT1, NFKB1, MAPK3, PDPK1, and TNF) from the DEGs and the co-expressed genes were defined as key genes, which differentially expressed in all the three cases. Then the protein-protein interaction network and gene set linkage analysis (GSLA) of key genes was performed. GSLA, gene ontology, and pathway enrichment analysis of the key genes elucidates nine major pathways in diabetes. Subsequently, we constructed the miRNA-gene and transcription factor-gene regulatory network of the five gene of interest in the nine major pathways were studied. hsa-mir-34a-5p, a major miRNA that interacted with all the five genes. RELA, FOXO3, PDX1, and SREBF1 were the TFs interacting with the major five gene of interest. Finally, drug-gene interaction network elucidates five potential drugs to treat the genes of interest. This research reveals biomarker genes, miRNA, TFs, and therapeutic drugs in the key signaling pathways, which may help us, understand the processes of all three secondary microvascular problems and aid in disease detection and management.

Identifying hub genes and miRNAs in Crohn's disease by bioinformatics analysis.

Introduction: Crohn’s disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD. Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and CD tissues were identified using the GEO2R online tool. The Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were conducted using the clusterProfiler function in the R package. Protein-protein interaction network (PPI) analysis and visualization were performed with STRING and Cytoscape. Ten hub genes were identified using cytoHubba’s MCC algorithm and validated with datasets GSE6731 and GSE52746. Finally, the miRNA gene regulatory network was constructed by Cytoscape and NetworkAnalyst to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 97 DEGs were identified, consisting of 88 downregulated genes and 9 upregulated genes. The enriched functions and pathways of the DEGs include immune system process, response to stress, response to cytokine and extracellular region. KEGG pathway analysis indicates that the genes were significantly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway, Rheumatoid arthritis and TNF signaling pathway. In combination with the results of the protein-protein interaction (PPI) network and CytoHubba, 10 hub genes including IL1B, CXCL8, CXCL10, CXCL1, CXCL2, CXCL5, ICAM1, IL1RN, TIMP1 and MMP3 were selected. Based on the DEG-miRNAs network construction, 5 miRNAs including hsa-mir-21-5p, hsa-mir-93-5p, hsa-mir-98-5p, hsa-mir-1-3p and hsa-mir-335-5p were identified as potential critical miRNAs. Conclusion: In conclusion, a total of 97 DEGs, 10 hub genes and 5 miRNAs that may be involved in the progression or occurrence of CD were identified in this study, which could be regarded as biomarkers of CD.

Integrated analysis of miRNA and mRNA expression profiles in response to gut microbiota depletion in the abdomens of female Bactrocera dorsalis.

Insect gut microbiota has been reported to participate in regulating host multiple biological processes including metabolism and reproduction. However, the corresponding molecular mechanisms remain largely unknown. Recent studies suggest that microRNAs (miRNAs) are involved in complex interactions between the gut microbiota and the host. Here, we used next-generation sequencing technology to characterize miRNA and mRNA expression profiles and construct the miRNA-gene regulatory network in response to gut microbiota depletion in the abdomens of female Bactrocera dorsalis. A total of 3016 differentially expressed genes (DEGs) and 18 differentially expressed miRNAs (DEMs) were identified. Based on the integrated analysis of miRNA and mRNA sequencing data, 229 negatively correlated miRNA-gene pairs were identified from the miRNA-mRNA network. Gene ontology enrichment analysis indicated that DEMs could target several genes involved in the metabolic process, oxidation-reduction process, oogenesis, and insulin signaling pathway. Finally, real-time quantitative polymerase chain reaction further verified the accuracy of RNA sequencing results. In conclusion, our study provides the profiles of miRNA and mRNA expressions under antibiotics treatment and provides an insight into the roles of miRNAs and their target genes in the interaction between the gut microbiota and its host.

Identification of Differentially Expressed Genes and miRNAs for Ulcerative Colitis Using Bioinformatics Analysis

Introduction: Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine whose cause and underlying mechanisms are not fully understood. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in UC. Materials and methods: Three UC datasets (GSE179285, GSE75214, GSE48958) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and UC tissues were identified using the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using Metascape. Protein-protein interaction network (PPI) analysis and visualization using STRING and Cytoscape. Finally, the miRNA gene regulatory network was constructed by Cytoscape to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 446 DEGs were identified, consisting of 309 upregulated genes and 137 downregulated genes. The enriched functions and pathways of the DEGs include extracellular matrix, regulation of cell adhesion, inflammatory response, response to cytokine, monocarboxylic acid metabolic process, response to toxic substance. The analysis of KEGG pathway indicates that the DEGs were significantly enriched in Complement and coagulation cascades, Amoebiasis, TNF signaling pathway, bile secretion, and Mineral absorption. Combining the results of the PPI network and CytoHubba, 9 hub genes including CXCL8, ICAM1, CXCR4, CD44, IL1B, MMP9, SPP1, TIMP1, and HIF1A were selected. Based on the DEG-miRNAs network construction, 7 miRNAs including miR-335-5p, mir-204-5p, miR-93-5p, miR106a-5p, miR-21-5p, miR-146a-5p, and miR-155-5p were identified as potential critical miRNAs. Conclusion: In summary, we identified DEGs that may be involved in the progression or occurrence of UC. A total of 446 DEGs,9 hub genes and 7 miRNAs were identified, which may be considered as biomarkers of UC. Further studies, however, are needed to elucidate the biological functions of these genes in UC.

Key Genes Associated With Non-Alcoholic Fatty Liver Disease and Polycystic Ovary Syndrome.

Background: Polycystic ovary syndrome (PCOS) is the most common metabolic and endocrinopathies disorder in women of reproductive age and non-alcoholic fatty liver (NAFLD) is one of the most common liver diseases worldwide. Previous research has indicated potential associations between PCOS and NAFLD, but the underlying pathophysiology is still not clear. The present study aims to identify the differentially expressed genes (DEGs) between PCOS and NAFLD through the bioinformatics method, and explore the associated molecular mechanisms. Methods: The microarray datasets GSE34526 and GSE63067 were downloaded from Gene Expression Omnibus (GEO) database and analyzed to obtain the DEGs between PCOS and NAFLD with the GEO2R online tool. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the DEGs were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, NetworkAnalyst was used to construct the network between the targeted microRNAs (miRNAs) and the hub genes. Results: A total of 52 genes were identified as DEGs in the above two datasets. GO and KEGG enrichment analysis indicated that DEGs are mostly enriched in immunity and inflammation related pathways. In addition, nine hub genes, including TREM1, S100A9, FPR1, NCF2, FCER1G, CCR1, S100A12, MMP9, and IL1RN were selected from the PPI network by using the cytoHubba and MCODE plug-in. Then, four miRNAs, including miR-20a-5p, miR-129-2-3p, miR-124-3p, and miR-101-3p, were predicted as possibly the key miRNAs through the miRNA-gene network construction. Conclusion: In summary, we firstly constructed a miRNA-gene regulatory network depicting interactions between the predicted miRNA and the hub genes in NAFLD and PCOS, which provides novel insights into the identification of potential biomarkers and valuable therapeutic leads for PCOS and NAFLD.

Bioinformatic-based Identification of Genes Associated with Aortic Valve Stenosis.

Aortic valve stenosis (AS) disease is the most common valvular disease in developed countries. The pathology of AS is complex, and its main processes include calcification of the valve stroma and involve genetic factors, lipoprotein deposition and oxidation, chronic inflammation, osteogenic transition of cardiac valve interstitial cells, and active valve calcification. The aim of this study was to identify potential genes associated with AS. Three original gene expression profiles (GSE153555, GSE12644, and GSE51472) were downloaded from the Gene Expression Omnibus (GEO) database and analyzed by GEO2R tool or 'limma' in R to identify differentially expressed genes (DEGs). Functional enrichment was analyzed using the ClusterProfiler package in R Bioconductor. STRING was utilized for the Protein-Protein Interaction (PPI) Network construct, and tissue-specific gene expression were identified using BioGPS database. The hub genes were screened out using the Cytoscape software. Related miRNAs were predicted in Targetscan, miWalk, miRDB, Hoctar, and TarBase. A total of 58 upregulated genes and 20 downregulated genes were screened out, which were mostly enriched in matrix remodeling and the immune system process. A module was thus clustered into by PPI network analysis, which mainly involved in Fc gamma R-mediated phagocytosis, Osteoclast differentiation. Ten genes (IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1) with the highest degree scores were subsequently identified as the hub genes for AS by applying the CytoHubba plugin. And hsa-miR-1276 was finally identified as potential miRNA and miRNA-gene regulatory network was constructed using NetworkAnalyst. Our analysis suggested that IBSP, NCAM1, MMP9, FCGR3B, COL4A3, FCGR1A, THY1, RUNX2, ITGA4, and COL10A1 might be hub genes associated with AS, and hsa-miR-1276 was potential miRNA. This result could provide novel insight into pathology and therapy of AS in the future.

MiRNA expression analysis of the sheep follicle during the prerecruitment, dominant, and mature stages of development under FSH stimulation

Follicle stimulating hormone (FSH) has been widely reported to influence ovarian follicular development, and miRNAs play a significant role in mammalian follicular development by regulating their target genes. Therefore, it is of interest to explore the roles of miRNAs in sheep follicular development during FSH stimulation. In the current study, we constructed miRNA expression profiles of small follicles (SFs, prerecruitment stage), medium follicles (MFs, dominance stage), and large follicles (LFs, maturation stage). Three and 50 significant differentially expressed miRNAs (DEMs) were identified in the MF vs. SF and LF vs. SF comparisons, respectively, and none were identified in the LF vs. MF comparison. Oar-miR-10a was significantly downregulated in MFs compared with SFs. In LFs compared with SFs, miR-212-3p, miR-212-5p and miR-202-5p were significantly upregulated, and miR-27a-3p, miR-181a-5p, miR-204-5p, and miR-182-5p were significantly downregulated. Furthermore, we predicted the target genes of significant DEMs and performed functional enrichment analyses of these target genes. Analyses of KEGG pathways and GO terms showed that the putative target genes were significantly enriched in ovarian steroidogenesis, glutathione metabolism, positive regulation of cell differentiation, positive regulation of cell development, and cellular response to oxygen-containing compounds. Analyses of miRNA–gene regulatory networks suggested that miR-181a-5p–CYP11A1, (miR-27a-3p and miR-129-5p)–LDLR, (miR-212-3p and miR-212-5p)–EFNA5, (miR-181a-5p, miR-182-5p, and miR-27a-3p)–INHBA, and miR-182-5p–SOD2 might be involved in follicular development. The present study provides basic data and suggests research directions for further exploration of the roles of miRNAs in sheep follicular development under FSH stimulation.

CircNet 2.0: an updated database for exploring circular RNA regulatory networks in cancers.

Circular RNAs (circRNAs), which are single-stranded RNA molecules that have individually formed into a covalently closed continuous loop, act as sponges of microRNAs to regulate transcription and translation. CircRNAs are important molecules in the field of cancer diagnosis, as growing evidence suggests that they are closely related to pathological cancer features. Therefore, they have high potential for clinical use as novel cancer biomarkers. In this article, we present our updates to CircNet (version 2.0), into which circRNAs from circAtlas and MiOncoCirc, and novel circRNAs from The Cancer Genome Atlas database have been integrated. In total, 2732 samples from 37 types of cancers were integrated into CircNet 2.0 and analyzed using several of the most reliable circRNA detection algorithms. Furthermore, target miRNAs were predicted from the full-length circRNA sequence using three reliable tools (PITA, miRanda and TargetScan). Additionally, 384 897 experimentally verified miRNA–target interactions from miRTarBase were integrated into our database to facilitate the construction of high-quality circRNA–miRNA–gene regulatory networks. These improvements, along with the user-friendly interactive web interface for data presentation, search, and visualization, showcase the updated CircNet database as a powerful, experimentally validated resource, for providing strong data support in the biomedical fields. CircNet 2.0 is currently accessible at https://awi.cuhk.edu.cn/∼CircNet.

Potential biomarkers of abnormal osseointegration of implants in type II diabetes mellitus

BackgroundType II diabetes mellitus (T2DM) is an important risk factor for osseointegration of implants. The aim of this study was to explore key genes of T2DM affecting bone metabolism through bioinformatic analysis of published RNA sequencing data, identify potential biomarkers, and provide a reference for finding the molecular mechanism of abnormal osseointegration caused by T2DM.MethodsWe identified differentially expressed mRNAs and miRNAs from the Gene Expression Omnibus database using the R package ‘limma’ and analysed the predicted target genes using Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Ontology analysis. At the same time, miRNA–mRNA interactions were explored using miRWalk 2.0.ResultsWe constructed an miRNA-gene regulatory network and a protein–protein interaction network. The enrichment pathways of differentially expressed mRNAs included extracellular matrix receptor interactions, protein digestion and absorption, the PI3K-Akt signalling pathway, cytokine–cytokine receptor interactions, chemokine signalling pathways, and haematopoietic cell lineage functions. We analysed the expression of these differentially expressed mRNAs and miRNAs in T2DM rats and normal rats with bone implants and identified Smpd3, Itga10, and rno-mir-207 as possible key players in osseointegration in T2DM.ConclusionSmpd3, Itga10, and rno-mir-207 are possible biomarkers of osseointegration in T2DM. This study sheds light on the possible molecular mechanism of abnormal osseointegration caused by bone metabolism disorder in T2DM.

Bioinformatic analysis and validation of microRNA-508-3p as a protective predictor by targeting NR4A3/MEK axis in pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) featured a debilitating progressive disorder. Here, we intend to determine diagnosis‐valuable biomarkers for PAH and decode the fundamental mechanisms of the biological function of these markers. Two mRNA microarray profiles (GSE70456 and GSE117261) and two microRNA microarray profiles (GSE55427 and GSE67597) were mined from the Gene Expression Omnibus platform. Then, we identified the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs), respectively. Besides, we investigated online miRNA prediction tools to screen the target gene of DEMs. In this study, 185 DEGs and three common DEMs were screened as well as 1266 target genes of the three DEMs were identified. Next, 16 overlapping dysregulated genes from 185 DEGs and 1266 target gene were obtained. Meanwhile, we constructed the miRNA gene regulatory network and determined miRNA‐508‐3p‐NR4A3 pair for deeper exploring. Experiment methods verified the functional expression of miR‐508‐3p in PAH and its signalling cascade. We observed that ectopic miR‐508‐3p expression promotes proliferation and migration of pulmonary artery smooth muscle cell (PASMC). Bioinformatic, dual‐luciferase assay showed NR4A3 represents directly targeted gene of miR‐508‐3p. Mechanistically, we demonstrated that down‐regulation of miR‐508‐3p advances PASMC proliferation and migration via inducing NR4A3 to activate MAPK/ERK kinase signalling pathway. Altogether, our research provides a promising diagnosis of predictor and therapeutic avenues for patients in PAH.

Identification of a MicroRNA Signature Associated With Lymph Node Metastasis in Endometrial Endometrioid Cancer.

BackgroundLymph node metastasis (LNM) is an important prognostic factor in endometrial cancer. Anomalous microRNAs (miRNAs) are associated with cell functions and are becoming a powerful tool to characterize malignant transformation and metastasis. The aim of this study was to construct a miRNA signature to predict LNM in endometrial endometrioid carcinoma (EEC).MethodCandidate target miRNAs related to LNM in EEC were screened by three methods including differentially expressed miRNAs (DEmiRs), weighted gene co-expression network analysis (WGCNA), and decision tree algorithms. Samples were randomly divided into the training and validation cohorts. A miRNA signature was built using a logistic regression model and was evaluated by the area under the curve (AUC) of receiver operating characteristic curve (ROC) and decision curve analysis (DCA). We also conducted pathway enrichment analysis and miRNA–gene regulatory network to look for potential genes and pathways engaged in LNM progression. Survival analysis was performed, and the miRNAs were tested whether they expressed differently in another independent GEO database.ResultThirty-one candidate miRNAs were screened and a final 15-miRNA signature was constructed by logistic regression. The model showed good calibration in the training and validation cohorts, with AUC of 0.824 (95% CI, 0.739–0.912) and 0.821 (95% CI, 0.691–0.925), respectively. The DCA demonstrated the miRNA signature was clinically useful. Hub miRNAs in signature seemed to contribute to EEC progression via mitotic cell cycle, cellular protein modification process, and molecular function. MiR-34c was statistically significant in survival that a higher expression of miR-34c indicated a higher survival time. MiR-34c-3p, miR-34c-5p, and miR-34b-5p were expressed differentially in GSE75968.ConclusionThe miRNA signature could work as a noninvasive method to detect LNM in EEC with a high prediction accuracy. In addition, miR-34c cluster may be a key biomarker referring LNM in endometrial cancer.

Integration of Transcriptional and Post-transcriptional Analysis Revealed the Early Response Mechanism of Sugarcane to Cold Stress.

Cold stress causes major losses to sugarcane production, yet the precise molecular mechanisms that cause losses due to cold stress are not well-understood. To survey miRNAs and genes involved in cold tolerance, RNA-seq, miRNA-seq, and integration analyses were performed on Saccharum spontaneum. Results showed that a total of 118,015 genes and 6,034 of these differentially expressed genes (DEGs) were screened. Protein–protein interaction (PPI) analyses revealed that ABA signaling via protein phosphatase 2Cs was the most important signal transduction pathway and late embryogenesis abundant protein was the hub protein associated with adaptation to cold stress. Furthermore, a total of 856 miRNAs were identified in this study and 109 of them were differentially expressed in sugarcane responding to cold stress. Most importantly, the miRNA–gene regulatory networks suggested the complex post-transcriptional regulation in sugarcane under cold stress, including 10 miRNAs−42 genes, 16 miRNAs−70 genes, and three miRNAs−18 genes in CT vs. LT0.5, CT vs. LT1, and CT0.5 vs. LT1, respectively. Specifically, key regulators from 16 genes encoding laccase were targeted by novel-Chr4C_47059 and Novel-Chr4A_40498, while five LRR-RLK genes were targeted by Novel-Chr6B_65233 and Novel-Chr5D_60023, 19 PPR repeat proteins by Novel-Chr5C_57213 and Novel-Chr5D_58065. Our findings suggested that these miRNAs and cell wall-related genes played vital regulatory roles in the responses of sugarcane to cold stress. Overall, the results of this study provide insights into the transcriptional and post-transcriptional regulatory network underlying the responses of sugarcane to cold stress.