An integrated network of microRNA and gene expression in ovarian cancer

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning

MOTIVATION

Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages.

RESULTS

In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems.

AVAILABILITY

http://sbbi-panda.unl.edu/pin/.

Gene-based mediation analysis in epigenetic studies

Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.

Transcriptome sequencing identifies genes associated with invasion of ovarian cancer

OBJECTIVE

To identify key genes in ovarian cancer using transcriptome sequencing in two cell lines: MCV152 (benign ovarian epithelial tumour) and SKOV-3 (ovarian serous carcinoma).

METHODS

Differentially expressed genes (DEGs) between SKOV-3 and MCV152 were identified. Candidate genes were assessed for enrichment in gene ontology function and Kyoto Encyclopaedia of Genes and Genomes pathway. Candidate gene expression in SKOV-3 and MCV152 cells was validated using Western blots.

RESULTS

A total of 2020 upregulated and 1673 downregulated DEGs between SKOV3 and MCV152 cells were identified that were significantly enriched in the cell adhesion function. Upregulated DEGs, such as angiopoietin 2 (ANGPT2), CD19 molecule (CD19), collagen type IV alpha 3 chain (COL4A3), fibroblast growth factor 18 (FGF18), integrin subunit beta 4 (ITGB4), integrin subunit beta 8 (ITGB8), laminin subunit alpha 3 (LAMA3), laminin subunit gamma 2 (LAMC2), protein phosphatase 2 regulatory subunit Bgamma (PPP2R2C) and spleen associated tyrosine kinase (SYK) were significantly involved in the extracellular matrix-receptor interaction pathway. Downregulated DEGs, such as AKT serine/threonine kinase 3 (AKT3), collagen type VI alpha 1 chain (COL6A1), colony stimulating factor 3 (CSF3), fibroblast growth factor 1 (FGF1), integrin subunit alpha 2 (ITGA2), integrin subunit alpha 11 (ITGA11), MYB proto-oncogene, transcription factor (MYB), phosphoenolpyruvate carboxykinase 2, mitochondrial (PCK2), placental growth factor (PGF), phosphoinositide-3-kinase adaptor protein 1 (PIK3AP1), serum/glucocorticoid regulated kinase 1 (SGK1), toll like receptor 4 (TLR4) and tumour protein p53 (TP53) were involved in PI3K-Akt signalling. Expression of these DEGs was confirmed by Western blot analyses.

CONCLUSION

Candidate genes enriched in cell adhesion, extracellular matrix-receptor interaction and PI3K-Akt signalling pathways were identified that may be closely associated with ovarian cancer invasion and potential targets for ovarian cancer treatment.

Circulating microRNA trafficking and regulation: computational principles and practice

Rapid advances in genomics discovery tools and a growing realization of microRNA's implication in intercellular communication have led to a proliferation of studies of circulating microRNA sorting and regulation across cells and different species. Although sometimes, reaching controversial scientific discoveries and conclusions, these studies have yielded new insights in the functional roles of circulating microRNA and a plethora of analytical methods and tools. Here, we consider this body of work in light of key computational principles underpinning discovery of circulating microRNAs in terms of their sorting and targeting, with the goal of providing practical guidance for applications that is focused on the design and analysis of circulating microRNAs and their context-dependent regulation. We survey a broad range of informatics methods and tools that are available to the researcher, discuss their key features, applications and various unsolved problems and close this review with prospects and broader implication of this field.

A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models

MOTIVATION

Epistasis reflects the distortion on a particular trait or phenotype resulting from the combinatorial effect of two or more genes or genetic variants. Epistasis is an important genetic foundation underlying quantitative traits in many organisms as well as in complex human diseases. However, there are two major barriers in identifying epistasis using large genomic datasets. One is that epistasis analysis will induce over-fitting of an over-saturated model with the high-dimensionality of a genomic dataset. Therefore, the problem of identifying epistasis demands efficient statistical methods. The second barrier comes from the intensive computing time for epistasis analysis, even when the appropriate model and data are specified.

RESULTS

In this study, we combine statistical techniques and computational techniques to scale up epistasis analysis using Empirical Bayesian Elastic Net (EBEN) models. Specifically, we first apply a matrix manipulation strategy for pre-computing the correlation matrix and pre-filter to narrow down the search space for epistasis analysis. We then develop a parallelized approach to further accelerate the modeling process. Our experiments on synthetic and empirical genomic data demonstrate that our parallelized methods offer tens of fold speed up in comparison with the classical EBEN method which runs in a sequential manner. We applied our parallelized approach to a yeast dataset, and we were able to identify both main and epistatic effects of genetic variants associated with traits such as fitness.

AVAILABILITY AND IMPLEMENTATION

The software is available at github.com/shilab/parEBEN.

miR-1224-5p inhibits the proliferation and invasion of ovarian cancer via targeting SND1

Emerging evidences have indicated that abnormal expression of microRNAs (miRNAs) contributed to carcinogenesis of ovarian cancer. However, the molecular mechanism of many aberrant expressed miRNAs was not known. Here, we discovered that miR-1224-5p was a downregulated miRNA in ovarian cancer via bioinformatic analysis and RT-qPCR. It was found that upregulation of miR-1224-5p inhibited cell proliferation and invasion ability of ovarian cancer cells. SND1, a well-characterized oncogene, was predicted as a target gene of miR-1224-5p. The western blotting, dual luciferase reporter assay, RNA-binding protein immunoprecipitation assay, and RT-qPCR demonstrated SND1 as a target gene of miR-1224-5p in ovarian cancer. MiR-1224-5p inhibited the expression of mesenchymal markers and increased the expression of epithelial markers in ovarian cancer cells via targeting SND1, indicating miR-1224-5p was involved in epithelial mesenchymal transition. The rescue assay manifested that miR-1224-5p-regulated cell proliferation and invasion mainly rely on downregulation of SND1 in ovarian cancer cells. In conclusion, our study revealed a direct regulatory association between miR-1224-5p and SND1 and their involvement in ovarian carcinogenesis.

Exploring drivers of gene expression in the Cancer Genome Atlas

Motivation

The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells.

Results

To more thoroughly investigate the behavior of gene expression in TCGA tumor samples, we introduce a statistical framework for partitioning the variation in gene expression due to a variety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation and germ-line genetic variation. As proof-of-principle, we identify and validate specific TFs that influence the expression of PTPN14 in breast cancer cells.

Availability and implementation

We provide a freely available, user-friendly, browseable interactive web-based application for exploring the results of our transcriptome-wide analyses across 17 different cancers in TCGA at http://ls-shiny-prod.uwm.edu/edge_in_tcga. All TCGA Open Access tier data are available at the Broad Institute GDAC Firehose and were downloaded using the TCGA2STAT R package. TCGA Controlled Access tier data are available via controlled access through the Genomic Data Commons (GDC). R scripts used to download, format and analyze the data and produce the interactive R/Shiny web app have been made available on GitHub at https://github.com/andreamrau/EDGE-in-TCGA.

Integration of miRNA and mRNA Expression Data for Understanding Etiology of Gynecologic Cancers

Dysregulation of miRNA-mRNA regulatory networks is very common phenomenon in any diseases including cancer. Altered expression of biomarkers leads to these gynecologic cancers. Therefore, understanding the underlying biological mechanisms may help in developing a robust diagnostic as well as a prognostic tool. It has been demonstrated in various studies that the pathways associated with gynecologic cancer have dysregulated miRNA as well as mRNA expression. Identification of miRNA-mRNA regulatory modules may help in understanding the mechanism of altered gynecologic cancer pathways. In this regard, an existing robust mutual information-based Maximum-Relevance Maximum-Significance algorithm has been used for identification of miRNA-mRNA regulatory modules in gynecologic cancer. A set of miRNA-mRNA modules are identified first than their association with gynecologic cancer are studied exhaustively. The effectiveness of the proposed approach is compared with the existing methods. The proposed approach is found to generate more robust integrated networks of miRNA-mRNA in gynecologic cancer.

Prediction of microRNA and gene target from an integrated network in chronic obstructive pulmonary disease based on canonical correlation analysis

BACKGROUND:

Chronic obstructive pulmonary disease (COPD) is a complex disorder with a high mortality. The pathophysiology of COPD has not been characterized till date.

OBJECTIVE:

To identify COPD-related biomarkers by a bioinformatics analysis.

METHODS:

Here, we conducted the canonical correlation analysis to extract the potential COPD-related miRNAs and mRNAs based on the miRNA-mRNA dual expression profiling data. After identifying miRNAs and mRNAs related to COPD, we constructed an interaction network by integrating three validated miRNA-target sources. Then we expanded the network by adding miRNA-mRNA pairs, which were identified by Spearman rank correlation test. For miRNAs involved in the network, we further performed the Gene Ontology (GO) functional enrichment analysis of their targets. To validate COPD-related mRNAs involved in the network, we performed receiver operating characteristic (ROC) curve analysis and Support Vector Machine (SVM) classification on only those mRNAs that overlapped with COPD-related mRNAs of Online Mendelian Inheritance in Man (OMIM) database.

RESULTS:

The results indicate that some identified miRNAs and their targets in the constructed network might be potential biomarkers of COPD.

CONCLUSIONS:

Our study helps us to predict the potential risk biomarkers of COPD, and it can certainly help in further elucidating the genetic etiology of COPD.

Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method

Background

Colon cancer is a leading cause of worldwide cancer death. It has become clear that microRNAs (miRNAs) play a role in the progress of colon cancer and understanding the effect of miRNAs on tumorigenesis could lead to better prognosis and improved treatment. However, most studies have focused on studying differentially expressed miRNAs between tumor and non-tumor samples or between stages in tumor tissue. Limited work has conducted to study the interactions or epistasis between miRNAs and how the epistasis brings about effect on tumor progression. In this study, we investigate the main and pair-wise epistatic effects of miRNAs on the pathological stages of colon cancer using datasets from The Cancer Genome Atlas.

Results

We develop a workflow composed of multiple steps for feature selection based on the Empirical Bayesian Elastic Net (EBEN) method. First, we identify the main effects using a model with only main effect on the phenotype. Second, a corrected phenotype is calculated by removing the significant main effect from the original phenotype. Third, we select features with epistatic effect on the corrected phenotype. Finally, we run the full model with main and epistatic effects on the previously selected main and epistatic features. Using the multi-step workflow, we identify a set of miRNAs with main and epistatic effect on the pathological stages of colon cancer. Many of miRNAs with main effect on colon cancer have been previously reported to be associated with colon cancer, and the majority of the epistatic miRNAs share common target genes that could explain their epistasis effect on the pathological stages of colon cancer. We also find many of the target genes of detected miRNAs are associated with colon cancer. Go Ontology Enrichment Analysis of the experimentally validates targets of main and epistatic miRNAs, shows that these target genes are enriched for biological processes associated with cancer progression.

Conclusion

Our results provide a set of candidate miRNAs associated with colon cancer progression that could have potential translational and therapeutic utility. Our analysis workflow offers a new opportunity to efficiently explore epistatic interactions among genetic and epigenetic factors that could be associated with human diseases. Furthermore, our workflow is flexible and can be applied to analyze the main and epistatic effect of various genetic and epigenetic factors on a wide range of phenotypes.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4130-7) contains supplementary material, which is available to authorized users.

Identifying miRNA regulatory mechanisms in preeclampsia by systems biology approaches

BACKGROUND

Preeclampsia (PE) is the major cause of maternal and fetal morbidity and mortality, affecting 3-8% of all pregnancies around the globe. miRNAs are small, noncoding RNA molecules, which negatively regulate gene expression. Abnormally expressed miRNAs contribute to pregnancy complications such as PE. The aim of our study was to find possible regulatory mechanisms by system biology approaches, which are connected to the pathogenesis of PE.

METHODS

We integrated publicly available miRNA and gene expression profiles and created a network from the significant miRNA-mRNA pairs with the help of MAGIA and Cytoscape softwares. Two subnetworks were expanded by adding protein-protein interactions. Differentially expressed miRNAs were identified for the evaluation of their regulatory effect. We analyzed the miRNAs and their targets using different bioinformatics tools and through literature research.

RESULTS

Altogether, 52,603 miRNA-mRNA interactions were generated by the MAGIA web tool. The top 250 interactions were visualized and pairs with q < 0.0001 were analyzed, which included 85 nodes and 80 edges signalizing the connections between 52 regulated genes and 33 miRNAs. A total of 11 of the regulated genes are PE related and 9 of them were targeted by multiple miRNAs. A total of 8 miRNAs were associated with PE before, and 13 miRNAs regulated more than 1 mRNA. Hsa-mir-210 was the highest degree node in the network and its role in PE is well established.

CONCLUSIONS

We identified several miRNA-mRNA regulatory mechanisms which may contribute to the pathogenesis of PE. Further investigations are needed to validate these miRNA-mRNA interactions and to enlighten the possibilities of developing potential therapeutic targets.

Dissecting the regulation rules of cancer-related miRNAs based on network analysis

miRNAs (microRNAs) are a set of endogenous and small non-coding RNAs which specifically induce degradation of target mRNAs or inhibit protein translation to control gene expression. Obviously, aberrant miRNA expression in human cells will lead to a serious of changes in protein-protein interaction network (PPIN), thus to activate or inactivate some pathways related to various diseases, especially carcinogenesis. In this study, we systematically constructed the miRNA-regulated co-expressed protein-protein interaction network (CePPIN) for 17 cancers firstly. We investigated the topological parameters and functional annotation for the proteins in CePPIN, especially for those miRNA targets. We found that targets regulated by more miRNAs tend to play a more important role in the forming process of cancers. We further elucidated the miRNA regulation rules in PPIN from a more systematical perspective. By GO and KEGG pathway analysis, miRNA targets are involved in various cellular processes mostly related to cell cycle, such as cell proliferation, growth, differentiation, etc. Through the Pfam classification, we found that miRNAs belonging to the same family tend to have targets from the same family which displays the synergistic function of these miRNAs. Finally, the case study on miR-519d and miR-21-regulated sub-network was performed to support our findings.

Differential Regulatory Analysis Based on Coexpression Network in Cancer Research

With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.

Cyclin Y regulates the proliferation, migration, and invasion of ovarian cancer cells via Wnt signaling pathway

This study is designated to investigate the roles of cyclin Y (CCNY) and Wnt signaling pathway in regulating ovarian cancer (OC) cell proliferation, migration, and invasion. Quantitative real-time PCR (qRT-PCR), Western blot, MTT assay, cell scratch, and transwell test were used in our study, and transplanted tumor model was constructed on nude mice. C-Myc, cyclin D1, PFTK1, ki67, OGT, and β-catenin protein expressions in tumor tissues were detected. CCNY was significantly upregulated in OC cell lines and tissues (both P < 0.05); significant association was observed between CCNY expression and clinicopathological stage, lymph node metastasis (LNM) (P < 0.05); and the CCNY expression in stages III to IV was higher than that in stages I to II, and patients with LNM had higher CCNY expression when compared with those in patients without LNM (P < 0.05); expressions of c-Myc, cyclin D, PFTK1, ki67, and OGT were upregulated in OC tissues compared with ovarian benign tissues, suggesting that these expressions were significantly different between the two groups (P < 0.05); CCNY significantly exacerbated proliferation, migration, and invasion of A2780 cells; c-Myc and cyclin D1 protein expressions increased as the expression of CCNY increased (P < 0.001); β-catenin expressions in A2780 cells with over-expression of CCNY were significantly increased in the nucleus, but significantly decreased in the cytoplasm (both P < 0.05); high expressions of CCNY exacerbated the proliferation of A2780 cells in nude mice and significantly increased c-Myc, cyclin D1, PFTK1, ki67, and OGT protein expressions in tumor tissues which were transplanted into nude mice (P < 0.01). CCNY might exacerbate the proliferation, migration, and invasion of OC cells via activating the Wnt signaling pathway. Thus, this study provides a theoretical foundation for the development of therapeutic drugs that are able to cure OC by targeting CCNY.

Epigenetic regulation of IQGAP2 promotes ovarian cancer progression via activating Wnt/β-catenin signaling

Ovarian cancer is the most lethal gynecologic malignancy and most cases are diagnosed at an advanced stage with metastases; however, the molecular events supporting ovarian cancer development and progression remain poorly understood. In this study, by analysis of the genome-scale DNA methylation profiles of 8 healthy ovaries, 89 ovarian cancers and the corresponding 4 normal ovaries from The Cancer Genome Atlas, we unveiled the abnormalities in gene methylation of ovarian cancers, and found that IQGAP2 one of the most frequently altered genes, was significantly hypermethylated in ovarian cancer. There was an inverse correlation between IQGAP2 DNA methylation and mRNA expression, and IQGAP2 expression was downregulated in ovarian cancer. Further survival analysis indicated that decreased IQGAP2 was associated with a worse progression-free survival of patient with ovarian cancer, and biological function studies demonstrated that IQGAP2 inhibited ovarian cancer cell epithelial-mesenchymal transition, migration and invasion via suppression of Wnt-induced β-catenin nuclear translocation and transcriptional activity. Thus, these data identified IQGAP2 as a novel tumor suppressor for ovarian cancer to inhibit cell invasion through regulating Wnt/β-catenin signaling, and provided a new biomarker and potential therapeutic strategy for this disease.

Genome-wide analysis of microRNA and mRNA expression signatures in cancer

Cancer is an extremely diverse and complex disease that results from various genetic and epigenetic changes such as DNA copy-number variations, mutations, and aberrant mRNA and/or protein expression caused by abnormal transcriptional regulation. The expression profiles of certain microRNAs (miRNAs) and messenger RNAs (mRNAs) are closely related to cancer progression stages. In the past few decades, DNA microarray and next-generation sequencing techniques have been widely applied to identify miRNA and mRNA signatures for cancers on a genome-wide scale and have provided meaningful insights into cancer diagnosis, prognosis and personalized medicine. In this review, we summarize the progress in genome-wide analysis of miRNAs and mRNAs as cancer biomarkers, highlighting their diagnostic and prognostic roles.