miRNA Co-expression Network Research Articles

MiR-129-5p as a biomarker for pathology and cognitive decline in Alzheimer’s disease

BackgroundAlzheimer’s dementia (AD) pathogenesis involves complex mechanisms, including microRNA (miRNA) dysregulation. Integrative network and machine learning analysis of miRNA can provide insights into AD pathology and prognostic/diagnostic biomarkers.MethodsWe performed co-expression network analysis to identify network modules associated with AD, its neuropathology markers, and cognition using brain tissue miRNA profiles from the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP) (N = 702) as a discovery dataset. We performed association analysis of hub miRNAs with AD, its neuropathology markers, and cognition. After selecting target genes of the hub miRNAs, we performed association analysis of the hub miRNAs with their target genes and then performed pathway-based enrichment analysis. For replication, we performed a consensus miRNA co-expression network analysis using the ROS/MAP dataset and an independent dataset (N = 16) from the Gene Expression Omnibus (GEO). Furthermore, we performed a machine learning approach to assess the performance of hub miRNAs for AD classification.ResultsNetwork analysis identified a glucose metabolism pathway-enriched module (M3) as significantly associated with AD and cognition. Five hub miRNAs (miR-129-5p, miR-433, miR-1260, miR-200a, and miR-221) of M3 had significant associations with AD clinical and/or pathologic traits, with miR129-5p by far the strongest across all phenotypes. Gene-set enrichment analysis of target genes associated with their corresponding hub miRNAs identified significantly enriched biological pathways including ErbB, AMPK, MAPK, and mTOR signaling pathways. Consensus network analysis identified two AD-associated consensus network modules and two hub miRNAs (miR-129-5p and miR-221). Machine learning analysis showed that the AD classification performance (area under the curve (AUC) = 0.807) of age, sex, and APOE ε4 carrier status was significantly improved by 6.3% with inclusion of five AD-associated hub miRNAs.ConclusionsIntegrative network and machine learning analysis identified miRNA signatures, especially miR-129-5p, as associated with AD, its neuropathology markers, and cognition, enhancing our understanding of AD pathogenesis and leading to better performance of AD classification as potential diagnostic/prognostic biomarkers.

MicroRNA-mRNA Analysis Reveals Tissue-Specific Regulation of microRNA in Mangrove Clam (Geloina erosa).

Geloina erosa is an important benthic animal in the mangrove, serving as an indicator organism for coastal environmental pollution. This study aimed to investigate the tissue-specific expression of miRNAs and their regulatory roles in predicted targets in G. erosa. Through miRNA sequencing and co-expression network analysis, we extensively studied the miRNA expression in three tissues: gills, hepatopancreas, and muscle. The results revealed a total of 1412 miRNAs, comprising 1047 known miRNAs, and 365 newly predicted miRNAs. These miRNAs exhibited distinct tissue-specific expression patterns. In the miRNA target gene prediction, a total of 7404 potential predicted targets were identified, representing approximately 33% of all unique transcripts associated with miRNAs. Further co-expression network analysis revealed nine modules, each showing a positive correlation with specific tissues (gills, hepatopancreas, or muscle). The blue module showed a significant correlation with gills (r = 0.83, p-value = 0.006), the black module was significantly related to the hepatopancreas (r = 0.78, p-value = 0.01), and the purple module was significantly correlated with muscle (r = 0.83, p-value = 0.006). Within these modules, related miRNAs tended to cluster together, while their correlations with other modules were relatively weak. Functional enrichment analysis was performed on miRNAs and their predicted targets in each tissue. In the gills, miRNAs primarily regulate immune-related genes, substance transport, and cytoskeletal organization. In the hepatopancreas, miRNAs suppressed genes involved in shell formation and played a role in cellular motor activity and metabolism. In muscle, miRNAs participate in metabolism and photoreceptive processes, as well as immune regulation. In summary, this study provides valuable insights into the tissue-specific regulation of miRNAs in G. erosa, highlighting their potential roles in immune response, metabolism, and environmental adaptation. These findings offer important clues for understanding the molecular mechanisms and biological processes in G. erosa, laying the foundation for further validation and elucidation of these regulatory relationships.

Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma

BackgroundMemory B cells and microRNAs (miRNAs) play important roles in the progression of gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD). However, few studies have investigated the use of memory B-cell-associated miRNAs in predicting the prognosis of STAD.MethodsWe identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA‒miRNA coexpression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity to anticancer drugs in the two groups were analysed.ResultsFour memory B-cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significant correlations to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan‒Meier (K‒M) survival curve analysis showed that the prognosis was poor in the high-risk group. Comprehensive analysis showed that patients in the high-risk group had higher immune scores, matrix scores, and immune cell infiltration and a poor immune response. In terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was associated with the greatest sensitivity.ConclusionsIn summary, we identified memory B-cell-associated miRNA prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Among patients in the high-risk group, STAD showed the highest sensitivity to CGP-60474. This study provides prognostic insights into individualized and precise treatment for STAD patients.

Identification and Verification of Key MiRNAs Associated with Intervertebral Disc Degeneration.

Intervertebral Disc Degeneration (IDD) is a heterogeneous spinal disease whose underlying molecular mechanism is unclear. This study aimed to identify, profile, and analyze microRNAs (miRNAs) related to IDD. Microarray Gene Expression IDD data (GSE63492) were downloaded from Gene Expression Omnibus datasets. We employed Weighted Gene Co-Expression Network Analysis (WGCNA) to construct a miRNA co-expression network, and the miRNAs related to the IDD stage were detected. The number of differentially expressed miRNAs between normal and degenerated nucleus pulposus tissues was calculated. Twenty-three clinical specimens were used to validate the expression of miRNAs using qRT-PCR. WGCNA identified 48 miRNAs significantly related to the IDD stage, and 94 miRNAs that were significantly different between normal and degenerated nucleus pulposus tissues. We selected 32 overlapping miRNAs and identified 347 corresponding target genes. The integrative analysis revealed the biological function and pathways of these targeted genes. Analysis of clinical specimens validated that hsa-miR-4534 was upregulated in IDD, whereas hsa-miR-1827 and hsa-miR- 185-5p were downregulated in IDD. This study has identified a subset of miRNAs that are related to IDD pathogenesis and hub miRNAs that are keys to the IDD co-expression network, which may potentially be utilized as indicators for treatment.

Combined Transcriptomic and Metabolomic Analysis of Women with Polycystic Ovary Syndrome.

Background Polycystic ovary syndrome (PCOS) is a complex class of endocrine disorders with insulin resistance, compensatory hyperinsulinemia, and obesity. However, the pathogenesis and therapies of PCOS have not been fully elucidated. Exosomal miRNAs have the potential to serve as biomarkers and therapies for a wide range of medical conditions. Method We collected follicular fluid from 5 PCOS patients and 5 healthy people. High-throughput sequencing technology to identify differentially expressed miRNAs and untargeted metabolome identify differential metabolites in follicular fluid exosomal. RT-qPCR and AUC analysis were performed. Result miRNA high-throughput sequencing identified 124 differential miRNAs. RT-qPCR analysis confirmed the sequencing results. These differential miRNA target genes are mainly involved in metabolic pathways. Metabolomics studies identified 31 differential metabolites. miRNA and lncRNA coexpression networks in metabolic pathways rigorously screened 28 differentially expressed miRNAs. This network would identify miRNA signatures associated with metabolic processes in PCOS. Meanwhile, the area under curve of receiver operating characteristic revealed that hsa-miR-196a-3p, hsa-miR-143-5p, hsa-miR-106a-3p, hsa-miR-34a-5p, and hsa-miR-20a-5p were potential biomarkers for the diagnosis of PCOS. Conclusion Collectively, these results demonstrate the potential pathogenesis of PCOS, and follicular fluid exosomal miRNAs may be efficient targets for the diagnosis and treatment of PCOS in long-term clinical studies.

Identification of Peripheral Blood miRNA Biomarkers in First-Episode Drug-Free Schizophrenia Patients Using Bioinformatics Strategy.

Schizophrenia (SCZ) is a polygenic, complex mental disorder of which a diagnosis is often made based on psychiatric history and clinical observation with few available objectives and detectable biomarkers. To identify co-expressed miRNA modules in schizophrenia patients and verify the possibility of using peripheral blood miRNAs as novel biomarkers, high-throughput sequencing was performed on 15 first-episode schizophrenia patients (FES) and 15 healthy controls (CTL). We found 79 differential expressed miRNAs (DEMs) in FES patients and three FES-related co-expression miRNA modules by miRNA-seq data standardized difference analysis and weighted gene co-expression network analysis (WGCNA). Then, 41 hub miRNAs were screened from the intersection of key modules and DEMs, among which miR-9-5p, miR-144-3p, miR-328-3p, and miR-4467 were selected for qRT-PCR verification in a larger sample (FES = 35, CTL = 60). The level of miR-9-5p in FES patients was downregulated, and miR-4467 was upregulated with better diagnostic performance (AUC = 0.719). The target genes of miR-9-5p engage in the biological processes (BP) such as body behaviour, neuronal differentiation regulation, nervous system development, and neurotrophin signaling pathways. Their hub target genes were also located, including NEDD4, EIF4G1, FBXL16, and FBXL3. Summarily, miR-9-5p and miR-4467 hold promise in blood diagnosis for SCZ, and miR-9-5p might affect the onset and development of SCZ through target regulation of neurodevelopment-related mRNAs. Our findings revealed the complex relationship between the miRNA co-expression network and FES, providing more verifiable biomarkers for SCZ early diagnosis and clues for the etiology of schizophrenia.

Integrated Analysis of mRNA and MicroRNA Co-expressed Network for the Differentiation of Bovine Skeletal Muscle Cells After Polyphenol Resveratrol Treatment.

Resveratrol (RSV) has been confirmed to benefit human health. Resveratrol supplemented in the feeds of animals improved pork, chicken, and duck meat qualities. In this study, we identified differentially expressed (DE) messenger RNAs (mRNAs) (n = 3,856) and microRNAs (miRNAs) (n = 93) for the weighted gene co-expression network analysis (WGCNA) to investigate the co-expressed DE mRNAs and DE miRNAs in the primary bovine myoblasts after RSV treatment. The mRNA results indicated that RSV treatments had high correlations with turquoise module (0.91, P-value = 0.01) and blue module (0.93, P-value < 0.01), while only the turquoise module (0.96, P-value < 0.01) was highly correlated with the treatment status using miRNA data. After biological enrichment analysis, the 2,579 DE genes in the turquoise module were significantly enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The top two GO terms were actin filament-based process (GO:0030029) and actin cytoskeleton organization (GO:0030036). The top two KEGG pathways were regulation of actin cytoskeleton (bta04810) and tight junction (bta04530). Then, we constructed the DE mRNA co-expression and DE miRNA co-expression networks in the turquoise module and the mRNA–miRNA targeting networks based on their co-expressions in the key module. In summary, the RSV-induced miRNAs participated in the co-expression networks that could affect mRNA expressions to regulate the primary myoblast differentiation. Our study provided a better understanding of the roles of RSV in inducing miRNA and of the characteristics of DE miRNAs in the key co-expressed module in regulation of mRNAs and revealed new candidate regulatory miRNAs and genes for the beef quality traits.

Expression profile, transcriptional and post-transcriptional regulation of genes involved in hydrogen sulphide metabolism connecting the balance between development and stress adaptation in plants: a data-mining bioinformatics approach.

Recent research focused on novel aspects of sulphur and sulphur-containing molecules in fundamental plant processes has highlighted the importance of these compounds. Currently, the focus has shifted to the efficacy of hydrogen sulphide (H2 S) as signalling compounds that regulate different development and stress mitigation in plants. Accordingly, we used an in silico approach to study the differential expression patterns of H2 S metabolic genes at different growth/development stages and their tissue-specific expression patterns under a range of abiotic stresses. Moreover, to understand the multilevel regulation of genes involved in H2 S metabolism, we performed computation-based promoter analysis, alternative splice variant analysis, prediction of putative miRNA targets and co-expression network analysis. Gene expression analysis suggests that H2 S biosynthesis is highly influenced by developmental and stress stimuli. The functional annotation of promoter structures reveales a wide range of plant hormone and stress responsive cis-regulatory elements (CREs) that regulate H2 S metabolism. Co-expression analysis suggested that genes involved in H2 S metabolism are also associated with different metabolic processes. In this data-mining study, the primary focus was to understand the genetic architecture governing pathways of H2 S metabolism in different cell compartments under various developmental and stress signalling cascades. The present study will help to understand the genetic architecture of H2 S metabolism via cysteine metabolism and the functional roles of these genes in development and stress tolerance mechanisms.

Weighted miRNA co-expression network reveals potential roles of apoptosis related pathways and crucial genes in thoracic aortic aneurysm

BackgroundThoracic aortic aneurysm (TAA) is a potentially life-threatening disease for which few medical therapies are available. Thus, it is critically important to investigate the underlying molecular mechanisms of TAA, and identify potential targets for TAA treatment.MethodsDifferentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs) were screened, and a weighted correlation network analysis (WGCNA) was employed to construct a weighted miRNA co-expression network using GSE110527. The DEMs were then mapped into the whole co-expression network of all samples, and a DEM coexpression network was created. Molecular Complex Detection (MCODE) was used to identify crucial miRNAs. Target genes were predicted using the miRTarbase database, and further screened by identifying genes that overlapped with the DEGs of GSE26155. The screened target genes were validated using GSE9106, and the successfully validated genes were considered as crucial genes. Finally, a miRNA risk score for diagnosing TAA was calculated by undertaking a least absolute shrinkage and selection operator (LASSO) regression.ResultsThe tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) signaling pathway was found in DEM functional enrichment. Crucial miRNAs were identified and target genes were predicted and associated with the regulation of the TRAIL signaling pathway. Next, 113 important target genes were identified as overlapping with the DEGs of GSE26155. These genes were further validated, and 5 successfully validated genes were considered as crucial genes. Finally, the miRNA risk score calculated by the LASSO regression was shown to have potential diagnostic value.ConclusionsWe performed a WGCNA analysis to construct a weighted miRNA co-expression network, predicted target genes of crucial miRNAs, identified crucial genes, and finally calculated a miRNA risk score. The results showed that pathways and genes associated with apoptosis appear to play an important role in TAA pathogenesis, and that medications targeting apoptosis might slow TAA progression. Future in vitro and in vivo experimental studies need to be undertaken to further validate our findings and investigate the mechanistic details of these crucial miRNAs and crucial genes.

Investigation of the miRNA and mRNA Coexpression Network and Their Prognostic Value in Hepatocellular Carcinoma.

Purpose To identify pivotal differentially expressed miRNAs and genes and construct their regulatory network in hepatocellular carcinoma. Methods mRNA (GSE101728) and microRNA (GSE108724) microarray datasets were obtained from the NCBI Gene Expression Omnibus (GEO) database. Then, we identified the differentially expressed miRNAs and mRNAs. Sequentially, transcription factor enrichment and gene ontology (GO) enrichment analysis for miRNA were performed. Target genes of these differential miRNAs were obtained using packages in R language (R package multiMiR). After that, downregulated miRNAs were matched with target mRNAs which were upregulated, while upregulated miRNAs were paired with downregulated target mRNA using scripts written in Perl. An miRNA-mRNA network was constructed and visualized in Cytoscape software. For miRNAs in the network, survival analysis was performed. And for genes in the network, we did gene ontology (GO) and KEGG pathway enrichment analysis. Results A total of 35 miRNAs and 295 mRNAs were involved in the network. These differential genes were enriched in positive regulation of cell-cell adhesion, positive regulation of leukocyte cell-cell adhesion, and so on. Eight differentially expressed miRNAs were found to be associated with the OS of patients with HCC. Among which, miR-425 and miR-324 were upregulated while the other six, including miR-99a, miR-100, miR-125b, miR-145, miR-150, and miR-338, were downregulated. Conclusion In conclusion, these results can provide a potential research direction for further studies about the mechanisms of how miRNA affects malignant behavior in hepatocellular carcinoma.

Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients

The weighted gene co-expression network analysis (WGCNA) has been used to explore gene expression datasets by constructing biological networks based on the likelihood expression profile among genes. In recent years, WGCNA found application in biomarker discovery studies, including miRNA. Serum samples from 20 patients with hepatocellular carcinoma (HCC) were profiled through miRNA 3.0 gene array and miRNAs biomarker candidates were identified through WGCNA. Results were validated by qRT-PCR in 102 HCC serum samples collected at diagnosis. WGCNA identified 16 miRNA modules, nine of them were significantly associated with the clinical characteristics of the patient. The Red module had a significant negative correlation with patients Survival (− 0.59, p = 0.007) and albumin (− 0.52, p = 0.02), and a positive correlation with PCR (0.61, p = 0.004) and alpha-fetoprotein (0.51, p = 0.02). In the red module, 16 circulating miRNAs were significantly associated with patient survival. MiR-3185 and miR-4507 were identified as predictors of patient survival after the validation phase. At diagnosis, high expression of circulating miR-3185 and miR-4507 identifies patients with longer survival (HR 2.02, 95% CI 1.10–3.73, p = 0.0086, and HR of 1.75, 95% CI 1.02–3.02, p = 0.037, respectively). Thought a WGCNA we identified miR-3185 and miR-4507 as promising candidate biomarkers predicting a longer survival in HCC patients.

Identification of Hub Genes Associated with Hypertension and Their Interaction with miRNA Based on Weighted Gene Coexpression Network Analysis (WGCNA) Analysis.

BackgroundHypertension is one of the most widespread health conditions in the world, and the molecular mechanism of it is still unclear. In this study, we identified the hub genes (hub miRNA genes) associated with hypertension and explored the relationship between hypertension miRNA-gene by constructing a mRNA co-expression network and a miRNA co-expression network, which can help to reveal the mechanism and predict the prognosis of hypertension progression.Material/MethodsBased on gene expression profile data of hypertensive samples from the Gene Expression Omnibus database, WGCNA was used to detect hypertension-related biomarkers and key mRNA and miRNA modules. Then, DAVID was used to perform gene-annotation enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) and miRPath were used for pathway analysis of mRNA and miRNAs genes.ResultsWe identified 3 key modules relating to hypertension, 2 mRNA modules named Msaddlebrown and Mgreenyellow and 1 miRNA module named Msalmon. In addition, 12 hub genes (RPL21, RPS28, LOC442727/PTGAP10, LOC100129599/RPS29P14, TBXAS1, FCER1G, CFP, FURIN, PECAM1, IGSF6, NCF1C, and LOC285296/UNC93B3) and 7 hub miRNAs (hsa-miR-1268a/b, hsa-miR-513c-3p, hsa-miR-4799-5p, hsa-miR-296-3p, hsa-miR-5195-5p, hsa-miR-219-2-3p, and hsa-miR-548d-5p) relating to hypertension were identified. HIF-1 signaling pathway and insulin signaling pathway were closely related to the 3 key modules. We also discovered 4 miRNAs (hsa-miR-548am-3p, hsa-miR-513c-3p, hsa-miR-182-5p, and hsa-miR-548d-5p) and 6 genes (IGF1R, GSK3B, FOXO1, PRKAR2B, HIF1A, and PIK3R1) were the core nodes in the hypertension-related miRNA-gene network, and hsa-miR-548am-3p was at the center of the network.ConclusionsThese findings will help improve the understanding of the pathogenesis of hypertension, and the discovered genes can serve as signatures for early diagnosis of hypertension.

Analysis of the Interaction Network of Hub miRNAs-Hub Genes, Being Involved in Idiopathic Pulmonary Fibers and Its Emerging Role in Non-small Cell Lung Cancer.

Idiopathic pulmonary fibrosis (IPF) is a fibrotic interstitial lung disease with lesions confined to the lungs. To identify meaningful microRNA (miRNA) and gene modules related to the IPF progression, GSE32537 (RNA-sequencing data) and GSE32538 (miRNA-sequencing data) were downloaded and processed, and then weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression networks and miRNA co-expression networks. GSE10667, GSE70866, and GSE27430 were used to make a reasonable validation for the results and evaluate the clinical significance of the genes and the miRNAs. Six hub genes (COL3A1, COL1A2, OGN, COL15A1, ASPN, and MXRA5) and seven hub miRNAs (hsa-let-7b-5p, hsa-miR-26a-5p, hsa-miR-25-3p, hsa-miR-29c-3p, hsa-let-7c-5p, hsa-miR-29b-3p, and hsa-miR-26b-5p) were clarified and validated. Meanwhile, iteration network of hub miRNAs-hub genes was constructed, and the emerging role of the network being involved in non-small cell lung cancer (NSCLC) was also analyzed by several webtools. The expression levels of hub genes were different between normal lung tissues and NSCLC tissues. Six genes (COL3A1, COL1A2, OGN, COL15A1, ASPN, and MXRA5) and three miRNAs (hsa-miR-29c-3p, hsa-let-7c-5p, and hsa-miR-29b-3p) were related to the survival time of lung adenocarcinoma (LUAD). The interaction network of hub miRNAs-hub genes might provide common mechanisms involving in IPF and NSCLC. More importantly, useful clues were provided for clinical treatment of both diseases based on novel molecular advances.

Investigation of miRNA and mRNA Co-expression Network in Ependymoma.

Ependymoma (EPN) is a rare primary tumor of the central nervous system (CNS) that affects both children and adults. Despite the definition and classification of distinct molecular subgroups, there remains a group of EPNs with a balanced genome, which makes it difficult to predict a prognosis of patients with EPN. The role of miRNA-mRNA network on EPN is still poorly understood. We assessed the involvement of miRNA-mRNA pairs in EPN by applying a weighted co-expression network analysis (WGCNA) approach. Using whole genome expression profile analysis followed by functional enrichment, we detected hub genes involved in active proliferation and DNA replication of nerve cells. Key genes including CYP11B1, KRT33B, RUNX1T1, SIK1, MAP3K4, MLANA, and SFRP5 identified in co-expression networks were regulated by miR-15a and miR-24-1. These seven miRNA-mRNA pairs were considered to influence not only pathways in cancer and tumor suppression process, but also MAPK, NF-kappaB, and WNT signaling pathways which were associated with tumorigenesis and development. This study provides a novel insight into potential diagnostic biomarkers of EPN and may have value in choosing therapeutic targets with clinical utility.

Comprehensive Analysis of Competitive Endogenous RNAs Network, Being Associated With Esophageal Squamous Cell Carcinoma and Its Emerging Role in Head and Neck Squamous Cell Carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a common malignancy with poor prognosis and survival rate. To identify meaningful long non-coding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) modules related to the ESCC prognosis, The Cancer Genome Atlas-ESCC was downloaded and processed, and then, a weighted gene co-expression network analysis was applied to construct lncRNA co-expression networks, miRNA co-expression networks, and mRNA co-expression networks. Twenty-one hub lncRNAs, seven hub miRNAs, and eight hub mRNAs were clarified. Additionally, a competitive endogenous RNAs network was constructed, and the emerging role of the network involved in head and neck squamous cell carcinoma (HNSCC) was also analyzed using several webtools. The expression levels of eight hub genes (TBC1D2, ATP6V0E1, SPI1, RNASE6, C1QB, C1QC, CSF1R, and C1QA) were different between normal esophageal tissues and HNSCC tissues. The expression levels of TBC1D2 and ATP6V0E1 were related to the survival time of HNSCC. The competitive endogenous RNAs network might provide common mechanisms involving in ESCC and HNSCC. More importantly, useful clues were provided for clinical treatments of both diseases based on novel molecular advances.

DeepMiR2GO: Inferring Functions of Human MicroRNAs Using a Deep Multi-Label Classification Model.

MicroRNAs (miRNAs) are a highly abundant collection of functional non-coding RNAs involved in cellular regulation and various complex human diseases. Although a large number of miRNAs have been identified, most of their physiological functions remain unknown. Computational methods play a vital role in exploring the potential functions of miRNAs. Here, we present DeepMiR2GO, a tool for integrating miRNAs, proteins and diseases, to predict the gene ontology (GO) functions based on multiple deep neuro-symbolic models. DeepMiR2GO starts by integrating the miRNA co-expression network, protein-protein interaction (PPI) network, disease phenotype similarity network, and interactions or associations among them into a global heterogeneous network. Then, it employs an efficient graph embedding strategy to learn potential network representations of the global heterogeneous network as the topological features. Finally, a deep multi-label classification network based on multiple neuro-symbolic models is built and used to annotate the GO terms of miRNAs. The predicted results demonstrate that DeepMiR2GO performs significantly better than other state-of-the-art approaches in terms of precision, recall, and maximum F-measure.

Role of miR-106b-5p in the regulation of gene profiles in endothelial cells

To evaluate the role of miR-106b-5p in the regulation of gene expression in endothelial cells. The Taqman low-density microRNAs (miRNAs) array (TLDA) was used to identify miRNA expression profiles in the plasma of patients with atherosclerotic coronary artery disease (CAD) (atherosclerosis group, n=9) and individuals without atherosclerotic CAD disease (control group, n=9). A weighed and undirected miRNA coexpression network analysis was performed to investigate the interactions among miRNAs in the two groups. MiR-106b-5p, whose coexpression pattern in atherosclerosis group was most different from that of control group, was further studied. Human umbilical vein endothelial cells (HUVEC) were transfected with miR-106b-5p mimic or negative control mimic, and Affymetrix GeneChip Human Transcriptome Array 2.0 was used to screen the differential gene expression profiles after transfection. And the signal transduction pathway of differential gene profiles was further analyzed in Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway database. After parsing the whole KEGG database, all differentially expressed genes involved pathways were extracted, and the hypergeometric distribution was used to calculate the pathway enrichment. The coexpression pattern of the patients with atherosclerosis (140 nodes, 1 154 edges) differed from that of the non-atherosclerosis control group (140 nodes, 612 edges). The analysis of array data with significant analysis of microarray (SAM) identified 746 significantly deregulated genes (fold change ≥ 1.5 and false discovery rate < 0.01) altered by overexpression of miR-106b-5p with miR-106b-5p mimic in HUVEC. By calculating the pathway enrichment, we found that multiple signaling pathways enriched in differential gene profiles were closely related to the process of formation and rupture of atherosclerotic plaque, including phosphatidylinositol-3 kinase (PI3K)/ protein kinase B (PKB, also called Akt), mammalian target of rapamycin (mTOR), transforming growth factor-β (TGF-β), janus kinase / signal transducer and activator of transcription (Jak-STAT), tumor necrosis factor (TNF), toll like receptor (TLR) and hypoxia-inducible factor 1α (HIF-1α) and other signal pathways. The coexpression pattern of miRNAs in plasma of patients with atherosclerosis is more significantly changed than that of individuals without atherosclerotic disease. MiR-106b-5p, which shows the most significant difference between groups, targets multiple signal pathways in vascular endothelial cells, and might play an important role in the regulatory network of atherosclerotic gene expression.

Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis.

Several microRNAs (miRNAs) have been suggested as novel biomarkers for diagnosing gastric cancer (GC) at an early stage, but the single-marker strategy may ignore the co-regulatory relationships and lead to low diagnostic specificity. Thus, multi-target modular diagnostic biomarkers are urgently needed. In this study, a Zsummary and NetSVM-based method was used to identify GC-related hub miRNAs and activated modules from clinical miRNA co-expression networks. The NetSVM-based sub-network consisting of the top 20 hub miRNAs reached a high sensitivity and specificity of 0.94 and 0.82. The Zsummary algorithm identified an activated module (miR-486, miR-451, miR-185, and miR-600) which might serve as diagnostic biomarker of GC. Three members of this module were previously suggested as biomarkers of GC and its 24 target genes were significantly enriched in pathways directly related to cancer. The weighted diagnostic ROC AUC of this module was 0.838, and an optimized module unit (miR-451 and miR-185) obtained a higher value of 0.904, both of which were higher than that of individual miRNAs. These hub miRNAs and module have the potential to become robust biomarkers for early diagnosis of GC with further validations. Moreover, such modular analysis may offer valuable insights into multi-target approaches to cancer diagnosis and treatment.

Functional Characterization of Non-coding RNAs Through Genomic Data Fusion.

Acting as a class of regulators of gene expression, miRNAs play crucial roles in various biological processes, such as cell proliferation, differentiation, and apoptosis. Increasing evidence indicates that dysregulation of miRNA expression and function is related to the pathogenesis of many human diseases. Here, we applied an integration strategy to identify conserved miRNA co-expression relationships and constructed a miRNA co-expression network based on human and mouse miRNA expression data. We performed large-scale bioinformatics analyses of conserved miRNA co-expression relationships and their functional links and confirmed that these conserved co-expressed miRNA relationships in the network tend to be functionally relevant. Co-expressed miRNA pairs regulated by common TFs are significantly enriched within the same miRNA clusters and/or miRNA families. Mapping well-known disease miRNAs to the network, we identified three miRNA sub-networks that are highly related to cancer risk. Furthermore, we observed that these conserved co-expressed miRNA sub-networks cooperatively regulate cancer-related functions through synergistically repressing crucial components of these processes. Our results suggest that co-expressed miRNAs assist to drive the initiation and progression of cancer in a cooperative manner.

Rapid Communication: MicroRNA co-expression network reveals apoptosis in the reproductive tract during molting in laying hens.

The aim of this study was to determine the regulatory mechanisms of molting and recrudescence via studying the micro-RNA (miRNA) expression in the oviduct of laying hens. We performed a cDNA microarray analysis in the magnum tissue from the oviduct to identify the whole miRNA profiles through the molting and recrudescence periods. A total of 35 laying hens (47-wk-old) were divided into 7 groups (0 d: a control group; 6 and 12 d: 2 molting-period groups fed on a high-zinc diet; and 20, 25, 30, and 35 d: 4 recrudescence-period groups fed on a normal diet after a 12-d period on a high-zinc diet). An miRNA co-expression network (miRCN) was generated using the differentially expressed miRNA (DEM) according to the entire data integration. The significantly co-expressed miRNA (n = 111) were highly differentially expressed from 12 to 20 d, which was a transition period between molting and recrudescence, while their expression patterns were contrary to the estrogen changes. The targets of highly connected miRNA (n = 12) indicated the significant biological pathways and gene ontology (GO) terms, such as MAPK and Wnt signaling and magnesium-ion binding, which are associated with apoptotic activities. These results suggest that the miRNA of the miRCN might play a role in the apoptotic progression of the reproductive tract during molting.