Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data: a multi‐phased study of prostate cancer

Abstract Background The integration of different layers of omics information is an opportunity to tackle the complexity of cardiovascular diseases (CVD) and to identify new predictive biomarkers and potential therapeutic targets. Our aim was to integrate DNA methylation and gene expression data in an effort to identify biomarkers related to cardiovascular disease risk in a community-based population. We accessed data from the Framingham Offspring Study, a cohort study with data on DNA methylation (Infinium HumanMethylation450 BeadChip; Illumina) and gene expression (Human Exon 1.0 ST Array; Affymetrix). Using the MOFA2 R package, we integrated these data to identify biomarkers related to the risk of presenting a cardiovascular event. Results Four independent latent factors (9, 19, 21—only in women—and 27), driven by DNA methylation, were associated with cardiovascular disease independently of classical risk factors and cell-type counts. In a sensitivity analysis, we also identified factor 21 as associated with CVD in women. Factors 9, 21 and 27 were also associated with coronary heart disease risk. Moreover, in a replication effort in an independent study three of the genes included in factor 27 were also present in a factor identified to be associated with myocardial infarction (CDC42BPB, MAN2A2 and RPTOR). Factor 9 was related to age and cell-type proportions; factor 19 was related to age and B cells count; factor 21 pointed to human immunodeficiency virus infection-related pathways and inflammation; and factor 27 was related to lifestyle factors such as alcohol consumption, smoking and body mass index. Inclusion of factor 21 (only in women) improved the discriminative and reclassification capacity of the Framingham classical risk function and factor 27 improved its discrimination. Conclusions Unsupervised multi-omics data integration methods have the potential to provide insights into the pathogenesis of cardiovascular diseases. We identified four independent factors (one only in women) pointing to inflammation, endothelium homeostasis, visceral fat, cardiac remodeling and lifestyles as key players in the determination of cardiovascular risk. Moreover, two of these factors improved the predictive capacity of a classical risk function.

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Abstract P098: An Epigenome-wide Study of Obesity in African American Youth and Young Adults: Novel Findings, Replication in Neutrophils and Relationship With Gene Expression

Circulation ◽

10.1161/circ.135.suppl_1.p098 ◽

2017 ◽

Vol 135 (suppl_1) ◽

Author(s):

Xiaoling Wang ◽

Yue Pan ◽

Haidong Zhu ◽

Guang Hao ◽

Xin Wang ◽

...

Keyword(s):

Gene Expression ◽

Older Adults ◽

Dna Methylation ◽

Young Adults ◽

Methylation Data ◽

Middle Aged ◽

Cpg Sites ◽

Genome Wide ◽

Obesity Status ◽

Youth And Young Adults

Background: Several large-scale epigenome wide association studies on obesity-related DNA methylation changes have been published and in total identified 46 CpG sites. These studies were conducted in middle-aged and older adults of Caucasians and African Americans (AAs) using leukocytes. To what extend these signals are independent of cell compositions as well as to what extend they may influence gene expression have not been systematically investigated. Furthermore, the high prevalence of obesity comorbidities in middle-aged or older population may hide or bias obesity itself related DNA methylation changes. Methods: In this study of healthy AA youth and young adults, genome wide DNA methylation data from leukocytes were obtained from three independent studies: EpiGO study (96 obese cases vs. 92 lean controls, aged 14-21, 50% females, test of interest is obesity status), LACHY study (284 participants from general population, aged 14-18, 50% females, test of interest is BMI), and Georgia Stress and Heart study (298 participants from general population, aged 18-38, 52% females, test of interest is BMI) using the Infinium HumanMethylation450 BeadChip. Genome wide DNA methylation data from purified neutrophils as well as genome wide gene expression data from leukocytes using Illumina HT12 V4 array were also obtained for the EpiGO samples. Results: The meta-analysis on the 3 cohorts identified 76 obesity related CpG sites in leukocytes with p<1х10 -7 . Out of the 46 previously identified CpG sites, 36 can be replicated in this AA youth and young adult sample with same direction and p<0.05. Out of the 107 CpG sites including the 36 replicated ones and the 71 newly identified ones, 71 CpG sites (66%) had their relationship with obesity replicated in purified neutrophils (p<0.05). The analysis on the cis regulation of the 107 CpG sites on gene expression showed that 59 CpG sites had at least one gene within 250kb having expression difference between obese cases and lean controls. Furthermore, out of the 59 CpG sites, 6 showed significantly negative correlations and 1 showed significantly positive correlation with the differentially expressed genes. These CpG sites located in SOCS3, CISH, ABCG1, PIM3 and PTGDS genes. Conclusion: In this study of AA youth and young adults, we identified novel CpG sites associated with obesity and replicated majority of the CpG sites previously identified in middle-aged and older adults. For the first time, we showed that majority of the obesity related CpG sites identified from leukocytes are not driven by cell compositions and provided the direct link between DNA methylation-gene expression-obesity status for 7 CpG sites in 5 genes.

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The Expression Level of mRNA, Protein, and DNA Methylation Status of FOSL2 of Uyghur in XinJiang in Type 2 Diabetes

Journal of Diabetes Research ◽

10.1155/2016/5957404 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Jun Li ◽

Siyuan Li ◽

Ying Hu ◽

Guolei Cao ◽

Siyao Wang ◽

...

Keyword(s):

Gene Expression ◽

Type 2 Diabetes ◽

Dna Methylation ◽

Methylation Status ◽

Methylation Data ◽

Biochemical Indicators ◽

Expression Levels ◽

Protein Levels ◽

Cpg Sites

Objective. We investigated the expression levels of both FOSL2 mRNA and protein as well as evaluating DNA methylation in the blood of type 2 diabetes mellitus (T2DM) Uyghur patients from Xinjiang. This study also evaluated whether FOSL2 gene expression had demonstrated any associations with clinical and biochemical indicators of T2DM. Methods. One hundred Uyghur subjects where divided into two groups, T2DM and nonimpaired glucose tolerance (NGT) groups. DNA methylation of FOSL2 was also analyzed by MassARRAY Spectrometry and methylation data of individual units were generated by the EpiTyper v1.0.5 software. The expression levels of FOS-like antigen 2 (FOSL2) and the protein expression levels were analyzed. Results. Significant differences were observed in mRNA and protein levels when compared with the NGT group, while methylation rates of eight CpG units within the FOSL2 gene were higher in the T2DM group. Methylation of CpG sites was found to inversely correlate with expression of other markers. Conclusions. Results show that a correlation between mRNA, protein, and DNA methylation of FOSL2 gene exists among T2DM patients from Uyghur. FOSL2 protein and mRNA were downregulated and the DNA became hypermethylated, all of which may be involved in T2DM pathogenesis in this population.

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DNA hypermethylation associated with upregulated gene expression in prostate cancer demonstrates the diversity of epigenetic regulation

BMC Medical Genomics ◽

10.1186/s12920-020-0657-6 ◽

2020 ◽

Vol 13 (1) ◽

Cited By ~ 2

Author(s):

Ieva Rauluseviciute ◽

Finn Drabløs ◽

Morten Beck Rye

Keyword(s):

Gene Expression ◽

Prostate Cancer ◽

Dna Methylation ◽

Epigenetic Regulation ◽

Normal Tissue ◽

Expression Profiles ◽

Expression Data ◽

Dna Hypermethylation ◽

Genome Wide ◽

A Genome

Abstract Background Prostate cancer (PCa) has the highest incidence rates of cancers in men in western countries. Unlike several other types of cancer, PCa has few genetic drivers, which has led researchers to look for additional epigenetic and transcriptomic contributors to PCa development and progression. Especially datasets on DNA methylation, the most commonly studied epigenetic marker, have recently been measured and analysed in several PCa patient cohorts. DNA methylation is most commonly associated with downregulation of gene expression. However, positive associations of DNA methylation to gene expression have also been reported, suggesting a more diverse mechanism of epigenetic regulation. Such additional complexity could have important implications for understanding prostate cancer development but has not been studied at a genome-wide scale. Results In this study, we have compared three sets of genome-wide single-site DNA methylation data from 870 PCa and normal tissue samples with multi-cohort gene expression data from 1117 samples, including 532 samples where DNA methylation and gene expression have been measured on the exact same samples. Genes were classified according to their corresponding methylation and expression profiles. A large group of hypermethylated genes was robustly associated with increased gene expression (UPUP group) in all three methylation datasets. These genes demonstrated distinct patterns of correlation between DNA methylation and gene expression compared to the genes showing the canonical negative association between methylation and expression (UPDOWN group). This indicates a more diversified role of DNA methylation in regulating gene expression than previously appreciated. Moreover, UPUP and UPDOWN genes were associated with different compartments — UPUP genes were related to the structures in nucleus, while UPDOWN genes were linked to extracellular features. Conclusion We identified a robust association between hypermethylation and upregulation of gene expression when comparing samples from prostate cancer and normal tissue. These results challenge the classical view where DNA methylation is always associated with suppression of gene expression, which underlines the importance of considering corresponding expression data when assessing the downstream regulatory effect of DNA methylation.

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Uncovering Driver DNA Methylation Events in Nonsmoking Early Stage Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2016/2090286 ◽

2016 ◽

Vol 2016 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Xindong Zhang ◽

Lin Gao ◽

Zhi-Ping Liu ◽

Songwei Jia ◽

Luonan Chen

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Lung Adenocarcinoma ◽

Early Stage ◽

Methylation Data ◽

Driver Genes ◽

Differential Network Analysis ◽

Differential Network ◽

Aberrant Dna Methylation ◽

Never Smokers

As smoking rates decrease, proportionally more cases with lung adenocarcinoma occur in never-smokers, while aberrant DNA methylation has been suggested to contribute to the tumorigenesis of lung adenocarcinoma. It is extremely difficult to distinguish which genes play key roles in tumorigenic processes via DNA methylation-mediated gene silencing from a large number of differentially methylated genes. By integrating gene expression and DNA methylation data, a pipeline combined with the differential network analysis is designed to uncover driver methylation genes and responsive modules, which demonstrate distinctive expressions and network topology in tumors with aberrant DNA methylation. Totally, 135 genes are recognized as candidate driver genes in early stage lung adenocarcinoma and top ranked 30 genes are recognized as driver methylation genes. Functional annotation and the differential network analysis indicate the roles of identified driver genes in tumorigenesis, while literature study reveals significant correlations of the top 30 genes with early stage lung adenocarcinoma in never-smokers. The analysis pipeline can also be employed in identification of driver epigenetic events for other cancers characterized by matched gene expression data and DNA methylation data.

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Comprehensive analysis of gene expression and DNA methylation data identifies potential biomarkers and functional epigenetic modules for lung adenocarcinoma

Genetics and Molecular Biology ◽

10.1590/1678-4685-gmb-2019-0164 ◽

2020 ◽

Vol 43 (3) ◽

Author(s):

XiaoCong Wang ◽

YanMei Li ◽

HuiHua Hu ◽

FangZheng Zhou ◽

Jie Chen ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Lung Adenocarcinoma ◽

Comprehensive Analysis ◽

Methylation Data ◽

Potential Biomarkers

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SNPs in Sites for DNA Methylation, Transcription Factor Binding, and miRNA Targets Leading to Allele-Specific Gene Expression and Contributing to Complex Disease Risk: A Systematic Review

Public Health Genomics ◽

10.1159/000510253 ◽

2020 ◽

pp. 1-16

Author(s):

Manik Vohra ◽

Anu Radha Sharma ◽

Navya Prabhu B ◽

Padmalatha S. Rai

Keyword(s):

Gene Expression ◽

Systematic Review ◽

Dna Methylation ◽

Transcription Factor ◽

Complex Disease ◽

Disease Risk ◽

Specific Gene ◽

Factor Binding ◽

Specific Gene Expression ◽

Allele Specific

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Integration of DNA Methylation Data and Gene Expression Data for Prostate Adenocarcinoma: A Proof of Concept

Current Bioinformatics ◽

10.2174/1574893612666170328171106 ◽

2017 ◽

Vol 12 (5) ◽

Cited By ~ 1

Author(s):

Arpit Singh ◽

Razia Rahman ◽

Yasha Hasija

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Gene Expression Data ◽

Prostate Adenocarcinoma ◽

Methylation Data ◽

Expression Data ◽

Proof Of Concept

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DNA Methylation Module Network-Based Prognosis and Molecular Typing of Cancer

Genes ◽

10.3390/genes10080571 ◽

2019 ◽

Vol 10 (8) ◽

pp. 571 ◽

Cited By ~ 4

Author(s):

Ze-Jia Cui ◽

Xiong-Hui Zhou ◽

Hong-Yu Zhang

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Molecular Typing ◽

Core Gene ◽

Cancer Prognosis ◽

Methylation Data ◽

Expression Data ◽

Module Network ◽

The Core ◽

Gene Modules

Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.

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