Reconstruction and analysis of transcription factor-miRNA co-regulatory feed-forward loops in human cancers using filter-wrapper feature selection

Hsa-miR-422a Originated from Short Interspersed Nuclear Element Increases ARID5B Expression by Collaborating with NF-E2

MicroRNAs (miRNAs) are a class of small non-coding RNAs that regulate the expression of target messenger RNA (mRNA) complementary to the 3' untranslated region (UTR) at the post-transcriptional level. Hsa-miR-422a, which is commonly known as miRNA derived from transposable element (MDTE), was derived from short interspersed nuclear element (SINE). Through expression analysis, hsa-miR-422a was found to be highly expressed in both the small intestine and liver of crab-eating monkey. AT-Rich Interaction Domain 5 B (ARID5B) was selected as the target gene of hsa-miR-422a, which has two binding sites in both the exon and 3'UTR of ARID5B. To identify the interaction between hsa-miR-422a and ARID5B, a dual luciferase assay was conducted in HepG2 cell line. The luciferase activity of cells treated with the hsa-miR-422a mimic was upregulated and inversely downregulated when both the hsa-miR-422a mimic and inhibitor were administered. Nuclear factor erythroid-2 (NF-E2) was selected as the core transcription factor (TF) via feed forward loop analysis. The luciferase expression was downregulated when both the hsa-miR-422a mimic and siRNA of NF-E2 were treated, compared to the treatment of the hsa-miR-422a mimic alone. The present study suggests that hsa-miR-422a derived from SINE could bind to the exon region as well as the 3'UTR of ARID5B. Additionally, hsa-miR-422a was found to share binding sites in ARID5Bwith several TFs, including NF-E2. The hsa-miR-422a might thus interact with TF to regulate the expression of ARID5B, as demonstrated experimentally. Altogether, hsa-miR-422a acts as a super enhancer miRNA of ARID5Bby collaborating with TF and NF-E2.

An integrated network analysis approach to identify potential key genes, transcription factors, and microRNAs regulating human hematopoietic stem cell aging

Hematopoietic stem cells (HSCs) undergo functional deterioration with increasing age that causes loss of their self-renewal and regenerative potential. Despite various efforts, significant success in identifying molecular regulators of HSC aging has not been achieved, one prime reason being the non-availability of appropriate human HSC samples. To demonstrate the scope of integrating and re-analyzing the HSC transcriptomics data available, we used existing tools and databases to structure a sequential data analysis pipeline to predict potential candidate genes, transcription factors, and microRNAs simultaneously. This sequential approach comprises (i) collecting matched young and aged mice HSC sample datasets, (ii) identifying differentially expressed genes, (iii) identifying human homologs of differentially expressed genes, (iv) inferring gene co-expression network modules, and (v) inferring the microRNA-transcription factor-gene regulatory network. Systems-level analyses of HSC interaction networks provided various insights based on which several candidates were predicted. For example, 16 HSC aging-related candidate genes were predicted (e.g., CD38, BRCA1, AGTR1, GSTM1, etc.) from GCN analysis. Following this, the shortest path distance-based analyses of the regulatory network predicted several novel candidate miRNAs and TFs. Among these, miR-124-3p was a common regulator in candidate gene modules, while TFs MYC and SP1 were identified to regulate various candidate genes. Based on the regulatory interactions among candidate genes, TFs, and miRNAs, a potential regulation model of biological processes in each of the candidate modules was predicted, which provided systems-level insights into the molecular complexity of each module to regulate HSC aging.

Integrated Analysis of MicroRNA (miRNA) and mRNA Profiles Reveals Reduced Correlation between MicroRNA and Target Gene in Cancer

BACKGROUND

Accumulating evidences demonstrated that microRNA-target gene pairs were closely related to tumorigenesis and development. However, the correlation between miRNA and target gene was insufficiently understood, especially its changes between tumor and normal tissues.

OBJECTIVES

The aim of this study was to evaluate the changes of correlation of miRNAs-target pairs between normal and tumor.

MATERIALS AND METHODS

5680 mRNA and 5740 miRNA expression profiles of 11 major human cancers were downloaded from the Cancer Genome Atlas (TCGA). The 11 cancer types were bladder urothelial carcinoma, breast invasive carcinoma, head and neck squamous cell carcinoma, kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, stomach adenocarcinoma, and thyroid carcinoma. For each cancer type, we firstly obtained differentially expressed miRNAs (DEMs) and genes (DEGs) in tumor and then acquired critical miRNA-target gene pairs by combining DEMs, DEGs and two experimentally validated miRNA-target interaction databases, miRTarBase and miRecords. We collected samples with both miRNA and mRNA expression values and performed a correlation analysis by Pearson method for miRNA-target pairs in normal and tumor, respectively.

RESULTS

We totally got 4743 critical miRNA-target pairs across 11 cancer types, and 4572 of them showed weaker correlation in tumor than in normal. The average correlation coefficients of miRNA-target pairs were different greatly between normal (-0.38 ~ -0.61) and tumor (-0.04 ~ -0.26) for 11 cancer type. The pan-cancer network, which consisted of 108 edges connecting 35 miRNAs and 89 target genes, showed the interactions of pairs appeared in multicancers.

CONCLUSIONS

This comprehensive analysis revealed that correlation between miRNAs and target genes was greatly reduced in tumor and these critical pairs we got were involved in cellular adhesion, proliferation, and migration. Our research could provide opportunities for investigating cancer molecular regulatory mechanism and seeking therapeutic targets.

Integrated analysis of transcription factors and targets co-expression profiles reveals reduced correlation between transcription factors and target genes in cancer

Transcription factors are recognized as the key regulators of gene expression. However, the changes in the correlation of transcription factors and their target genes between normal and tumor tissues are usually ignored. In this research, we used mRNA expression profile data from The Cancer Genome Atlas which included 5726 samples across 11 major human cancers to perform co-expression analysis by the Pearson correlation coefficients. Then, integrating 81,357 pairs of transcription factors and target genes from transcription factors databases to find out the changes in the co-expression correlation of these gene pairs from normal to tumor tissues. Based on the changes in the number of co-expressed TF-TG pairs and changes in the level of co-expression, we found the generally reduced correlation between transcription factors and their target genes in cancer. Additionally, we screened out universal and specific transcription factors-target genes pairs which may significant influence particular cancer. Then, we obtained 423 cancer cell line expression profiles from Broad Institute Cancer Cell Line Encyclopedia to verify our results. Some of these pairs like XRCC5-XRCC6 have been reported to involve in multiple cancers, while pairs like IRF1-PSMB9 without any previous articles related to tumor but involve in the biological processes of cancer, which are of great potential to be therapeutic targets. Our research may provide insights to better understand the tumor development mechanisms and find potential therapeutic targets.

Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data

In human health, a fundamental challenge is the identification of disease-related genes. Bladder cancer (BC) is a worldwide malignant tumor, which has resulted in 170,000 deaths in 2010 up from 114,000 in 1990. Moreover, with the emergence of multi-omics data, more comprehensive analysis of human diseases become possible. In this study, we propose a multi-step approach for the identification of BC-related genes by using integrative Heterogeneous Network Modeling of Multi-Omics data (iHNMMO). The heterogeneous network model properly and comprehensively reflects the multiple kinds of relationships between genes in the multi-omics data of BC, including general relationships, unique relationships under BC condition, correlational relationships within each omics and regulatory relationships between different omics. Besides, a network-based propagation algorithm with resistance is utilized to quantize the relationships between genes and BC precisely. The results of comprehensive performance evaluation suggest that iHNMMO significantly outperforms other approaches. Moreover, further analysis suggests that the top ranked genes may be functionally implicated in BC, which also confirms the superiority of iHNMMO. In summary, this study shows that disease-related genes can be better identified through reasonable integration of multi-omics data.

Dynamic and Modularized MicroRNA Regulation and Its Implication in Human Cancers

MicroRNA is responsible for the fine-tuning of fundamental cellular activities and human disease development. The altered availability of microRNAs, target mRNAs, and other types of endogenous RNAs competing for microRNA interactions reflects the dynamic and conditional property of microRNA-mediated gene regulation that remains under-investigated. Here we propose a new integrative method to study this dynamic process by considering both competing and cooperative mechanisms and identifying functional modules where different microRNAs co-regulate the same functional process. Specifically, a new pipeline was built based on a meta-Lasso regression model and the proof-of-concept study was performed using a large-scale genomic dataset from ~4,200 patients with 9 cancer types. In the analysis, 10,726 microRNA-mRNA interactions were identified to be associated with a specific stage and/or type of cancer, which demonstrated the dynamic and conditional miRNA regulation during cancer progression. On the other hands, we detected 4,134 regulatory modules that exhibit high fidelity of microRNA function through selective microRNA-mRNA binding and modulation. For example, miR-18a-3p, -320a, -193b-3p, and -92b-3p co-regulate the glycolysis/gluconeogenesis and focal adhesion in cancers of kidney, liver, lung, and uterus. Furthermore, several new insights into dynamic microRNA regulation in cancers have been discovered in this study.

Candidate miRNAs and pathogenesis investigation for hepatocellular carcinoma based on bioinformatics analysis

The present study aimed to explore the mechanisms behind the development and progression of hepatocellular carcinoma (HCC) and identify information regarding HCC-related microRNAs (miRNAs) or marker genes for the gene therapy of HCC. Gene expression profile of GSE67882, generated from 4 hepatitis B virus infected HCC tissue samples (HCC group) and 8 chronic hepatitis B tissue samples with no fibrosis (control group) were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs functional enrichment and pathway analyses of HCC were revealed, followed by transcription factor-miRNA interaction network construction and analyses. A total of 14 upregulated miRNAs and 16 downregulated miRNAs between HCC and control samples were obtained. Differentially expressed miRNAs were mainly involved in biological processes like the regulation of histone H3-K9 methylation, and the KEGG pathways in cancer map05200 demonstrates their involvement in cancer. A total of 3 outstanding regulatory networks of miRNAs: hsa-miR-15a, hsa-miR-125b and hsa-miR-122 were revealed. A total of 11 differentially expressed miRNAs including hsa-miR-146p-5b that regulated the marker genes of HCC were explored. miRNAs such as hsa-miR-15a, hsa-miR-125b, hsa-miR-122 and hsa-miR-146b-5p may be new biomarkers for the gene therapy of HCC. Furthermore, histone H3-K9 methylation and other pathways in cancer observed in the KEGG map05200 may be closely related with the development of HCC.

Studying Dynamic Features in Myocardial Infarction Progression by Integrating miRNA-Transcription Factor Co-Regulatory Networks and Time-Series RNA Expression Data from Peripheral Blood Mononuclear Cells

Myocardial infarction (MI) is a serious heart disease and a leading cause of mortality and morbidity worldwide. Although some molecules (genes, miRNAs and transcription factors (TFs)) associated with MI have been studied in a specific pathological context, their dynamic characteristics in gene expressions, biological functions and regulatory interactions in MI progression have not been fully elucidated to date. In the current study, we analyzed time-series RNA expression data from peripheral blood mononuclear cells. We observed that significantly differentially expressed genes were sharply up- or down-regulated in the acute phase of MI, and then changed slowly until the chronic phase. Biological functions involved at each stage of MI were identified. Additionally, dynamic miRNA–TF co-regulatory networks were constructed based on the significantly differentially expressed genes and miRNA–TF co-regulatory motifs, and the dynamic interplay of miRNAs, TFs and target genes were investigated. Finally, a new panel of candidate diagnostic biomarkers (STAT3 and ICAM1) was identified to have discriminatory capability for patients with or without MI, especially the patients with or without recurrent events. The results of the present study not only shed new light on the understanding underlying regulatory mechanisms involved in MI progression, but also contribute to the discovery of true diagnostic biomarkers for MI.

Transcription factor and miRNA co-regulatory network reveals shared and specific regulators in the development of B cell and T cell

The maturation process of lymphocyte was related to many blood diseases, such as lymphoma and lymphoid leukemia. Many TFs and miRNAs were separately studied in the development of B and T cells. In this study, we aim to discover the TF and miRNA co-regulation and identify key regulators in the B and T cells maturation. We obtained the candidate genes, miRNAs and TFs for each stage of their maturation, then constructed the TF-miRNA-gene feed-forward loops (FFLs) for each stage by our previous methods. Statistical test for FFLs indicated their enrichment and significance. TF-miRNA co-regulatory networks for each stage were constructed by combining their FFLs. Hub analysis revealed the key regulators in each stage, for example, MYC, STAT5A, PAX5 and miR-17 ~ 92 in the transition of pro-B cells into pre-B cells. We also identified a few common regulators and modules in two stages of B cell maturation (e.g. miR-146a/NFKB1/BCL11A) and two stages of T cell maturation (e.g. miR-20/CCND2/SORL1), as well as some shared regulators in the early stages of both B and T cell development. Our network will help to increase understanding of mature process of B and T cell, as well as the related blood diseases.

MicroRNA and transcription factor mediated regulatory network analysis reveals critical regulators and regulatory modules in myocardial infarction

Myocardial infarction (MI) is a severe coronary artery disease and a leading cause of mortality and morbidity worldwide. However, the molecular mechanisms of MI have yet to be fully elucidated. In this study, we compiled MI-related genes, MI-related microRNAs (miRNAs) and known human transcription factors (TFs), and we then identified 1,232 feed-forward loops (FFLs) among these miRNAs, TFs and their co-regulated target genes through integrating target prediction. By merging these FFLs, the first miRNA and TF mediated regulatory network for MI was constructed, from which four regulators (SP1, ESR1, miR-21-5p and miR-155-5p) and three regulatory modules that might play crucial roles in MI were then identified. Furthermore, based on the miRNA and TF mediated regulatory network and literature survey, we proposed a pathway model for miR-21-5p, the miR-29 family and SP1 to demonstrate their potential co-regulatory mechanisms in cardiac fibrosis, apoptosis and angiogenesis. The majority of the regulatory relations in the model were confirmed by previous studies, which demonstrated the reliability and validity of this miRNA and TF mediated regulatory network. Our study will aid in deciphering the complex regulatory mechanisms involved in MI and provide putative therapeutic targets for MI.

Discovering key regulatory mechanisms from single-factor and multi-factor regulations in glioblastoma utilizing multi-dimensional data

Glioblastoma (GBM) is the most common malignant brain cancer in adults. Investigating the regulatory mechanisms underlying GBM is effective for the in-depth study of GBM. The Cancer Genome Atlas (TCGA) project is producing large-scale data and makes the comprehensive study of the diverse regulatory mechanisms underlying GBM possible. Although there have been research studies on GBM with large-scale data, distinguishing different regulatory mechanisms and identifying the key regulation types remain challenging. In this study, we integrated multi-dimensional data of differentially expressed genes in GBM: copy number variation (CNV), gene expression, miRNA expression and methylation, by performing partial correlation analysis with the Lasso technique. Our results showed that there were single-factor and multi-factor regulatory mechanisms in GBM. In further analysis of the regulation subtypes, we discovered that single-factor and multi-factor regulations are potentially distinct in functionality. Moreover, multi-factor regulations especially the key regulation subtypes may be more relevant to GBM and affect many GBM-related genes such as ERBB2 and MAPK1. This study not only verifies the utility of multi-dimensional data integration into GBM research but also distinguishes the key multi-factor regulatory subtypes that may drive pathogenesis of GBM from various regulatory mechanisms.

Suppression of endothelial nitric oxide synthase expression and endothelial cell proliferation by an intronic 27-ntmiRNA and it's a novel link to AP-1

This study aims to investigate the role of activator protein 1 (AP-1) in the effects of 27nt-miRNA on expression of endothelial nitric oxide synthase (eNOS) gene and proliferation of endothelial cells. Cell proliferation was analyzed by cell number counting, colony formation assay and MTT assay. Cell migration and invasion was detected by transwell assay and invasion assay. Expression of eNOS and AP-1 was measured by real-time RT-PCR (mRNA level) and Western blotting (protein level). Luciferase reporter assay was performed to detect the binding of 27nt-miRNA to AP-1. Overexpression of 27nt-miRNA significantly inhibited endothelial cells proliferation, invasion and migration in vitro. And, eNOS and AP-1 expression at mRNA and protein levels were down-regulated by overexpression of 27nt-miRNA. Interestingly, overexpression of AP-1 protein partially restored eNOS expression and endothelial cell proliferation. Furthermore, the luciferase reporter assay demonstrated that AP-1 was a direct target of 27nt-miRNA. These data demonstrate that overexpression of 27nt-miRNA inhibits endothelial cell proliferation, invasion, migration, eNOS expression and AP-1 expression. Moreover, AP-1, a direct target of 27nt-miRNA, reverses the inhibitory effects of 27nt-miRNA. Thus, the effects of 27nt-miRNA might be acted through targeting AP-1.

Prediction of protein kinase-specific phosphorylation sites in hierarchical structure using functional information and random forest

Reversible protein phosphorylation is one of the most important post-translational modifications, which regulates various biological cellular processes. Identification of the kinase-specific phosphorylation sites is helpful for understanding the phosphorylation mechanism and regulation processes. Although a number of computational approaches have been developed, currently few studies are concerned about hierarchical structures of kinases, and most of the existing tools use only local sequence information to construct predictive models. In this work, we conduct a systematic and hierarchy-specific investigation of protein phosphorylation site prediction in which protein kinases are clustered into hierarchical structures with four levels including kinase, subfamily, family and group. To enhance phosphorylation site prediction at all hierarchical levels, functional information of proteins, including gene ontology (GO) and protein-protein interaction (PPI), is adopted in addition to primary sequence to construct prediction models based on random forest. Analysis of selected GO and PPI features shows that functional information is critical in determining protein phosphorylation sites for every hierarchical level. Furthermore, the prediction results of Phospho.ELM and additional testing dataset demonstrate that the proposed method remarkably outperforms existing phosphorylation prediction methods at all hierarchical levels. The proposed method is freely available at http://bioinformatics.ustc.edu.cn/phos_pred/.