Extracting coordinated patterns of DNA methylation and gene expression in ovarian cancer

FOXD3 may be a new cellular target biomarker as a hypermethylation gene in human ovarian cancer

Background

FOXD3 is aberrantly regulated in several tumors, but its underlying mechanisms in ovarian cancer (OC) remains largely unknown. The present study aimed to explore the role and associated mechanisms of FOXD3 in OC.

Methods

Microarray data from GEO was used to analyze differential CpG sites and differentially methylated regions (DMR) in tumor tissues and Illumina 450 genome-wide methylation data was employed. The FOXD3 expression level was determined through qRT-PCR and western blot analysis. Wound healing test, colony formation and flow cytometry assay were utilized to analyze cell migration, proliferation abilities, cell cycle and cell apoptosis, respectively. Finally, the effect of FOXD3 on tumor growth was investigated through in vivo xenograft experiments.

Results

GEO data analysis showed that FOXD3 was hypermethylated in OC tissues. Also, qRT-PCR revealed that FOXD3 was low expressed and methylation-specific PCR (MSP) confirmed that the methylation level of FOXD3 was hypermethylated. Combined treatment of 5-aza-2′-deoxycytidine (5-Aza-dC) could synergistically restored FOXD3 expression. Finally, in vitro and in vivo experiments showed that demethylated FOXD3 decreased cell proliferation and migration abilities, and increased the cell apoptosis. In vivo experiment detected that demethylated FOXD3 restrained tumor growth.

Conclusions

FOXD3 could act as a tumor suppressor to inhibit cell proliferation, migration and promote cell apoptosis in OC cells.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0755-8) contains supplementary material, which is available to authorized users.

Identifying subtype-specific associations between gene expression and DNA methylation profiles in breast cancer

BACKGROUND

Breast cancer is a complex disease in which different genomic patterns exists depending on different subtypes. Recent researches present that multiple subtypes of breast cancer occur at different rates, and play a crucial role in planning treatment. To better understand underlying biological mechanisms on breast cancer subtypes, investigating the specific gene regulatory system via different subtypes is desirable.

METHODS

Gene expression, as an intermediate phenotype, is estimated based on methylation profiles to identify the impact of epigenomic features on transcriptomic changes in breast cancer. We propose a kernel weighted l1-regularized regression model to incorporate tumor subtype information and further reveal gene regulations affected by different breast cancer subtypes. For the proper control of subtype-specific estimation, samples from different breast cancer subtype are learned at different rate based on target estimates. Kolmogorov Smirnov test is conducted to determine learning rate of each sample from different subtype.

RESULTS

It is observed that genes that might be sensitive to breast cancer subtype show prediction improvement when estimated using our proposed method. Comparing to a standard method, overall performance is also enhanced by incorporating tumor subtypes. In addition, we identified subtype-specific network structures based on the associations between gene expression and DNA methylation.

CONCLUSIONS

In this study, kernel weighted lasso model is proposed for identifying subtype-specific associations between gene expressions and DNA methylation profiles. Identification of subtype-specific gene expression associated with epigenomic changes might be helpful for better planning treatment and developing new therapies.

Identifying epigenetically dysregulated pathways from pathway-pathway interaction networks

BACKGROUND

Identification of pathways that show significant difference in activity between disease and control samples have been an interesting topic of research for over a decade. Pathways so identified serve as potential indicators of aberrations in phenotype or a disease condition. Recently, epigenetic mechanisms such as DNA methylation are known to play an important role in altering the regulatory mechanism of biological pathways. It is reasonable to think that a set of genes that show significant difference in expression and methylation interact together to form a network of pathways. Existing pathway identification methods fail to capture the complex interplay between interacting pathways.

RESULTS

This paper proposes a novel framework to identify biological pathways that are dysregulated by epigenetic mechanisms. Experiments on four benchmark cancer datasets and comparison with state-of-the-art pathway identification methods reveal the effectiveness of the proposed approach.

CONCLUSION

The proposed framework incorporates both topology and biological relationships of pathways. Comparison with state-of-the-art techniques reveals promising results. Epigenetic signatures identified from pathway interaction networks can help to advance Molecular Pathological Epidemiology (MPE) research efforts by predicting tumor molecular changes.

Network-guided regression for detecting associations between DNA methylation and gene expression

MOTIVATION

High-throughput profiling in biological research has resulted in the availability of a wealth of data cataloguing the genetic, epigenetic and transcriptional states of cells. These data could yield discoveries that may lead to breakthroughs in the diagnosis and treatment of human disease, but require statistical methods designed to find the most relevant patterns from millions of potential interactions. Aberrant DNA methylation is often a feature of cancer, and has been proposed as a therapeutic target. However, the relationship between DNA methylation and gene expression remains poorly understood.

RESULTS

We propose Network-sparse Reduced-Rank Regression (NsRRR), a multivariate regression framework capable of using prior biological knowledge expressed as gene interaction networks to guide the search for associations between gene expression and DNA methylation signatures. We use simulations to show the advantage of our proposed model in terms of variable selection accuracy over alternative models that do not use prior network information. We discuss an application of NsRRR to The Cancer Genome Atlas datasets on primary ovarian tumours.

AVAILABILITY AND IMPLEMENTATION

R code implementing the NsRRR model is available at http://www2.imperial.ac.uk/∼gmontana

CONTACT

giovanni.montana@kcl.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptional and epigenetic regulation of KIF14 overexpression in ovarian cancer

KIF14 (kinesin family member 14) is a mitotic kinesin and an important oncogene in several cancers. Tumor KIF14 expression levels are independently predictive of poor outcome, and in cancer cells KIF14 can modulate metastatic behavior by maintaining appropriate levels of cell adhesion and migration proteins at the cell membrane. Thus KIF14 is an exciting potential therapeutic target. Understanding KIF14's regulation in cancer cells is crucial to the development of effective and selective therapies to block its tumorigenic function(s). We previously determined that close to 30% of serous ovarian cancers (OvCa tumors) exhibit low-level genomic gain, indicating one mechanism of KIF14 overexpression in tumors. We now report on transcriptional and epigenetic regulation of KIF14. Through promoter deletion analyses, we identified one cis-regulatory region containing binding sites for Sp1, HSF1 and YY1. siRNA-mediated knockdown of these transcription factors demonstrated endogenous regulation of KIF14 overexpression by Sp1 and YY1, but not HSF1. ChIP experiments confirmed an enrichment of both Sp1 and YY1 binding to the endogenous KIF14 promoter in OvCa cell lines with high KIF14 expression. A strong correlation was seen in primary serous OvCa tumors between Sp1, YY1 and KIF14 expression, further evidence that these transcription factors are important players in KIF14 overexpression. Hypomethylation patterns were observed in primary serous OvCa tumors, suggesting a minor role for promoter methylation in the control of KIF14 gene expression. miRNA expression analysis determined that miR-93, miR-144 and miR-382 had significantly lower levels of expression in primary serous OvCa tumors than normal tissues; treatment of an OvCa cell line with miRNA mimics and inhibitors specifically modulated KIF14 mRNA levels, pointing to potential novel mechanisms of KIF14 overexpression in primary tumors. Our findings reveal multiple mechanisms of KIF14 upregulation in cancer cells, offering new targets for therapeutic interventions to reduce KIF14 in tumors, aiming at improved prognosis.