MicroRNA and transcription factor co-regulatory network analysis reveals miR-19 inhibits CYLD in T-cell acute lymphoblastic leukemia

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy. The understanding of its gene expression regulation and molecular mechanisms still remains elusive. Started from experimentally verified T-ALL-related miRNAs and genes, we obtained 120 feed-forward loops (FFLs) among T-ALL-related genes, miRNAs and TFs through combining target prediction. Afterwards, a T-ALL miRNA and TF co-regulatory network was constructed, and its significance was tested by statistical methods. Four miRNAs in the miR-17-92 cluster and four important genes (CYLD, HOXA9, BCL2L11 and RUNX1) were found as hubs in the network. Particularly, we found that miR-19 was highly expressed in T-ALL patients and cell lines. Ectopic expression of miR-19 represses CYLD expression, while miR-19 inhibitor treatment induces CYLD protein expression and decreases NF-κB expression in the downstream signaling pathway. Thus, miR-19, CYLD and NF-κB form a regulatory FFL, which provides new clues for sustained activation of NF-κB in T-ALL. Taken together, we provided the first miRNA-TF co-regulatory network in T-ALL and proposed a model to demonstrate the roles of miR-19 and CYLD in the T-cell leukemogenesis. This study may provide potential therapeutic targets for T-ALL and shed light on combining bioinformatics with experiments in the research of complex diseases.     

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA (miRNA) biomarkers for hepatocellular carcinoma (HCC) using methods in systems biology. Differentially expressed mRNAs, miRNAs, and long non-coding RNAs (lncRNAs) were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas (TCGA) database. Subsequently, a prognosis-associated mRNA co-expression network, an mRNA–miRNA regulatory network, and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis. Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs. An expression module including 120 mRNAs was significantly correlated with HCC patient survival. Combined with patient survival data, several mRNAs and miRNAs, including CHST4, SLC22A8, STC2, hsa-miR-326, and hsa-miR-21 were identified from the network to predict HCC patient prognosis. Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC. Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways. The present study provides a bioinformatics method for biomarker screening, leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.     

Reconstructing the coding and non-coding RNA regulatory networks of miRNAs and mRNAs in breast cancer

microRNAs (miRNAs) are a class of small non-coding RNAs that deregulate and/or decrease the expression of target messenger RNAs (mRNAs), which specifically contribute to complex diseases. In our study, we reanalyzed an integrated data to promote classification performance by rebuilding miRNA–mRNA modules, in which a group of deregulated miRNAs cooperatively regulated a group of significant mRNAs. In five-fold cross validation, the multiple processes flow considered the biological and statistical significant correlations. First, of statistical significant miRNAs, 6 were identified as core miRNAs. Second, in the 13 significant pathways enriched by gene set enrichment analysis (GSEA), 705 deregulated mRNAs were found. Based on the union of predicted sets and correlation sets, 6 modules were built. Finally, after verified by test sets, three indexes, including area under the ROC curve (AUC), Accuracy and Matthews correlation coefficients (MCCs), indicated only 4 modules (miR-106b-CIT-KPNA2-miR-93, miR-106b-POLQ-miR-93, miR-107-BTRC-UBR3-miR-16 and miR-200c-miR-16-EIF2B5-miR-15b) had discriminated ability and their classification performance were prior to that of the single molecules. By applying this flow to different subtypes, Module 1 was the consistent module across subtypes, but some different modules were still specific to each subtype. Taken together, this method gives new insight to building modules related to complex diseases and simultaneously can give a supplement to explain the mechanism of breast cancer (BC).     

Survival prediction in patients with colon adenocarcinoma via multiomics data integration using a deep learning algorithm

The present study proposed a deep learning (DL) algorithm to predict survival in patients with colon adenocarcinoma (COAD) based on multiomics integration. The survival-sensitive model was constructed using an autoencoder for DL implementation based on The Cancer Genome Atlas (TCGA) data of patients with COAD. The autoencoder framework was compared with PCA, NMF, t-SNE, and univariable Cox-PH model for identifying survival-related features. The prognostic robustness of the inferred survival risk groups was validated using three independent confirmation cohorts. Differential expression analysis, Pearson’s correlation analysis, construction of miRNA–target gene network, and function enrichment analysis were performed. Two risk groups with significant survival differences were identified in TCGA set using the autoencoder-based model (log-rank P-value = 5.51e⁻⁰⁷). The autoencoder framework showed superior performance compared with PCA, NMF, t-SNE, and the univariable Cox-PH model based on the C-index, log-rank P-value, and Brier score. The robustness of the classification model was successfully verified in three independent validation sets. There were 1271 differentially expressed genes, 10 differentially expressed miRNAs, and 12 hypermethylated genes between the survival risk groups. Among these, miR-133b and its target genes (GNB4, PTPRZ1, RUNX1T1, EPHA7, GPM6A, BICC1, and ADAMTS5) were used to construct a network. These genes were significantly enriched in ECM–receptor interaction, focal adhesion, PI3K–Akt signaling pathway, and glucose metabolism-related pathways. The risk subgroups obtained through a multiomics data integration pipeline using the DL algorithm had good robustness. miR-133b and its target genes could be potential diagnostic markers. The results would assist in elucidating the possible pathogenesis of COAD.     

Extremely Low-Frequency Electromagnetic Fields Affect the miRNA-Mediated Regulation of Signaling Pathways in the GC-2 Cell Line

Extremely low-frequency electromagnetic fields (ELF-EMFs) can affect male reproductive function, but the underlying mechanism of this effect remains unknown. miRNA-mediated regulation has been implicated as an important epigenetic mechanism for regulatory pathways. Herein, we profiled miRNA expression in response to ELF-EMFs in vitro. Mouse spermatocyte-derived GC–2 cells were intermittently exposed to a 50 Hz ELF-EMF for 72 h (5 min on/10 min off) at magnetic field intensities of 1 mT, 2 mT and 3 mT. Cell viability was assessed using the CCK–8 assay. Apoptosis and the cell cycle were analyzed with flow cytometry. miRNA expression was profiled using Affymetrix Mouse Genechip miRNA 3.0 arrays. Our data showed that the growth, apoptosis or cell cycle arrest of GC–2 cells exposed to the 50 Hz ELF-EMF did not significantly change. However, we identified a total of 55 miRNAs whose expression significantly changed compared with the sham group, including 19 differentially expressed miRNAs (7 miRNAs were upregulated, and 12 were downregulated) in the 1 mT exposure group and 36 (9 miRNAs were upregulated, and 27 were downregulated) in the 3 mT exposure group. The changes in the expression of 15 selected miRNAs measured by real-time PCR were consistent with the microarray results. A network analysis was used to predict core miRNAs and target genes, including miR-30e-5p, miR-210-5p, miR-196b-5p, miR-504-3p, miR-669c-5p and miR-455-3p. We found that these miRNAs were differentially expressed in response to different magnetic field intensities of ELF-EMFs. GO term and KEGG pathway annotation based on the miRNA expression profiling results showed that miRNAs may regulate circadian rhythms, cytokine-cytokine receptor interactions and the p53 signaling pathway. These results suggested that miRNAs could serve as potential biomarkers, and the miRNA-mediated regulation of signaling pathways might play significant roles in the biological effects of ELF-EMFs.     

Tissue and Process Specific microRNA–mRNA Co-Expression in Mammalian Development and Malignancy

An association between enrichment and depletion of microRNA (miRNA) binding sites, 3′ UTR length, and mRNA expression has been demonstrated in various developing tissues and tissues from different mature organs; but functional, context-dependent miRNA regulations have yet to be elucidated. Towards that goal, we examined miRNA–mRNA interactions by measuring miRNA and mRNA in the same tissue during development and also in malignant conditions. We identified significant miRNA-mediated biological process categories in developing mouse cerebellum and lung using non-targeted mRNA expression as the negative control. Although miRNAs in general suppress target mRNA messages, many predicted miRNA targets demonstrate a significantly higher level of co-expression than non-target genes in developing cerebellum. This phenomenon is tissue specific since it is not observed in developing lungs. Comparison of mouse cerebellar development and medulloblastoma demonstrates a shared miRNA–mRNA co-expression program for brain-specific neurologic processes such as synaptic transmission and exocytosis, in which miRNA target expression increases with the accumulation of multiple miRNAs in developing cerebellum and decreases with the loss of these miRNAs in brain tumors. These findings demonstrate the context-dependence of miRNA–mRNA co-expression.     

Integrative analysis of miRNA–mRNA network in high altitude retinopathy by bioinformatics analysis

High-altitude retinopathy (HAR) is an ocular manifestation of acute oxygen deficiency at high altitudes. Although the pathophysiology of HAR has been revealed by many studies in recent years, the molecular mechanism is not yet clear. Our study aimed to systematically identify the genes and microRNA (miRNA) and explore the potential biomarkers associated with HAR by integrated bioinformatics analysis. The mRNA and miRNA expression profiles were obtained from the Gene Expression Omnibus database. We performed Gene Ontology functional annotations and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Potential target gene analysis and miRNA–mRNA network analysis were also conducted. Quantitative RT-PCR (qRT-PCR) was used to validate the results of the bioinformatics analysis. Through a series of bioinformatics analyses and experiments, we selected 16 differentially expressed miRNAs (DE-miRNAs) and 157 differentially expressed genes related to acute mountain sickness (AMS) and constructed a miRNA–mRNA network containing 240 relationship pairs. The hub genes were filtered from the protein-protein interaction network: IL7R, FOS, IL10, FCGR2A, DDX3X, CDK1, BCL11B and HNRNPH1, which were all down-regulated in the AMS group. Then, nine up-regulated DE-miRNAs and eight hub genes were verified by qRT-PCR in our hypoxia-induced HAR cell model. The expression of miR-3177-3p, miR-369-3p, miR-603, miR-495, miR-4791, miR-424-5p, FOS, IL10 and IL7R was consistent with our bioinformatics results. In conclusion, FOS, IL10, IL-7R and 7 DE-miRNAs may participate in the development of HAR. Our findings will contribute to the identification of biomarkers and promote the effective prevention and treatment of HAR in the future.     

Identification of Drosophila MicroRNA targets

MicroRNAs (miRNAs) are short RNA molecules that regulate gene expression by binding to target messenger RNAs and by controlling protein production or causing RNA cleavage. To date, functions have been assigned to only a few of the hundreds of identified miRNAs, in part because of the difficulty in identifying their targets. The short length of miRNAs and the fact that their complementarity to target sequences is imperfect mean that target identification in animal genomes is not possible by standard sequence comparison methods. Here we screen conserved 3′ UTR sequences from the Drosophila melanogaster genome for potential miRNA targets. The screening procedure combines a sequence search with an evaluation of the predicted miRNA–target heteroduplex structures and energies. We show that this approach successfully identifies the five previously validated let-7, lin-4, and bantam targets from a large database and predict new targets for Drosophila miRNAs. Our target predictions reveal striking clusters of functionally related targets among the top predictions for specific miRNAs. These include Notch target genes for miR-7, proapoptotic genes for the miR-2 family, and enzymes from a metabolic pathway for miR-277. We experimentally verified three predicted targets each for miR-7 and the miR-2 family, doubling the number of validated targets for animal miRNAs. Statistical analysis indicates that the best single predicted target sites are at the border of significance; thus, target predictions should be considered as tentative until experimentally validated. We identify features shared by all validated targets that can be used to evaluate target predictions for animal miRNAs. Our initial evaluation and experimental validation of target predictions suggest functions for two miRNAs. For others, the screen suggests plausible functions, such as a role for miR-277 as a metabolic switch controlling amino acid catabolism. Cross-genome comparison proved essential, as it allows reduction of the sequence search space. Improvements in genome annotation and increased availability of cDNA sequences from other genomes will allow more sensitive screens. An increase in the number of confirmed targets is expected to reveal general structural features that can be used to improve their detection. While the screen is likely to miss some targets, our study shows that valid targets can be identified from sequence alone.     

Integrated analyses identify the involvement of microRNA-26a in epithelial–mesenchymal transition during idiopathic pulmonary fibrosis

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, and highly lethal fibrotic lung disease with poor treatment and unknown etiology. Emerging evidence suggests that epithelial–mesenchymal transition (EMT) has an important role in repair and scar formation following epithelial injury during pulmonary fibrosis. Although some miRNAs have been shown to be dysregulated in the pathophysiological processes of IPF, limited studies have payed attention on the participation of miRNAs in EMT in lung fibrosis. In our study, we identified and constructed a regulation network of differentially expressed IPF miRNAs and EMT genes. Additionally, we found the downregulation of miR-26a in mice with experimental pulmonary fibrosis. Further studies showed that miR-26a regulated HMGA2, which is a key factor in the process of EMT and had the maximum number of regulating miRNAs in the regulation network. More importantly, inhibition of miR-26a resulted in lung epithelial cells transforming into myofibroblasts in vitro and in vivo, whereas forced expression of miR-26a alleviated TGF-β1- and BLM-induced EMT in A549 cells and in mice, respectively. Taken together, our study deciphered the essential role of miR-26a in the pathogenesis of EMT in pulmonary fibrosis, and suggests that miR-26a may be a potential therapeutic target for IPF.