Promoter DNA methylation analysis reveals a combined diagnosis of CpG-based biomarker for prostate cancer

Epigenome-wide differential methylation and differential variability as predictors of high-grade cervical intraepithelial neoplasia (CIN2+)

CpG site methylation patterns have potential to improve differentiation of high-grade screening-detected cervical abnormalities. We assessed CpG differential methylation (DM) and differential variability (DV) in high-grade (CIN2+) vs low-grade (≤ CIN1) lesions. In ≤ CIN1 (n = 117) and CIN2+ (n = 31) samples, cervical sample DNA underwent testing with Illumina HumanMethylation arrays. We assessed DM and DV of CpG methylation M-values among 9 cervical cancer-associated genes. We fit CpG-specific linear models and estimated empirical Bayes standard errors and false discovery rates (FDRs). An exploratory epigenome-wide association study (EWAS) aimed to detect novel DM and DV CpGs (FDR < 0.05) and Gene Ontology (GO) term enrichment. Compared to ≤ CIN1, CIN2+ exhibited greater methylation at CCNA1 cluster 1 (M-value difference 0.24; 95% CI, 0.04-0.43) and RARB cluster 2 (0.16; 95% CI, 0.05-0.28), and lower methylation at CDH1 cluster 1 (-0.15; 95% CI, -0.26 to -0.04). CIN2+ exhibited lower variability at CDH1 cluster 2 (variation difference -0.24; 95% CI, -0.41 to -0.05) and FHIT cluster 1 (-0.30; 95% CI, -0.50 to -0.09). EWAS detected 3534 DM and 270 DV CpGs. Forty-four GO terms were enriched with DM CpGs related to transcriptional, structural, developmental, and neuronal processes. Methylation patterns may help triage screening-detected cervical abnormalities and inform US screening algorithms. This article is part of a Special Collection on Gynecological Cancer.

Epigenetic signatures of asthma: a comprehensive study of DNA methylation and clinical markers

BACKGROUND

Asthma, a complex respiratory disease, presents with inflammatory symptoms in the lungs, blood, and other tissues. We investigated the relationship between DNA methylation and 35 clinical markers of asthma.

METHODS

The Illumina Infinium EPIC v1 methylation array was used to evaluate 742,442 CpGs in whole blood from 319 participants from 94 families. They were part of the Netherlands Twin Register from families with at least one member suffering from severe asthma. Repeat blood samples were taken after 10 years from 182 individuals. Principal component analysis on the clinical asthma markers yielded ten principal components (PCs) that explained 92.8% of the total variance. We performed epigenome-wide association studies (EWAS) for each of the ten PCs correcting for familial structure and other covariates.

RESULTS

221 unique CpGs reached genome-wide significance at timepoint 1 after Bonferroni correction. PC7, which correlated with loadings of eosinophil counts and immunoglobulin levels, accounted for the majority of associations (204). Enrichment analysis via the EWAS Atlas identified 190 of these CpGs to be previously identified in EWASs of asthma and asthma-related traits. Proximity assessment to previously identified SNPs associated with asthma identified 17 unique SNPs within 1 MB of two of the 221 CpGs. EWAS in 182 individuals with epigenetic data at a second timepoint identified 49 significant CpGs. EWAS Atlas enrichment analysis indicated that 4 of the 49 were previously associated with asthma or asthma-related traits. Comparing the estimates of all the significant associations identified across the two time points yielded a correlation of 0.81.

CONCLUSION

We identified 270 unique CpGs that were associated with PC scores generated from 35 clinical markers of asthma, either cross-sectionally or 10 years later. A strong correlation was present between effect sizes at the 2 timepoints. Most associations were identified for PC7, which captured blood eosinophil counts and immunoglobulin levels and many of these CpGs have previous associations in earlier studies of asthma and asthma-related traits. The results point to a robust DNA methylation profile as a new, stable biomarker for asthma.

Epigenetic Signatures of Asthma: A Comprehensive Study of DNA Methylation and Clinical Markers

Background

Asthma, a complex respiratory disease, presents with inflammatory symptoms in the lungs, blood, and other tissues. We investigated the relationship between DNA methylation and 35 clinical markers of asthma. The Illumina Infinium EPIC v1 methylation array was used to evaluate 742,442 CpGs in whole blood samples from 319 participants. They were part of the Netherlands Twin Register from families with at least one member suffering from severe asthma. Repeat blood samples were taken after 10 years from 182 of these individuals. Principal component analysis (PCA) on the clinical markers yielded ten principal components (PCs) that explained 92.8% of the total variance. We performed epigenome-wide association studies (EWAS) for each of the ten PCs correcting for familial structure and other covariates.

Results

221 unique CpGs reached genome-wide significance at timepoint 1 (T1) after Bonferroni correction. PC7 accounted for the majority of associations (204), which correlated with loadings of eosinophil counts and immunoglobulin levels. Enrichment analysis via the EWAS Atlas identified 190 of these CpGs to be previously identified in EWASs of asthma and asthma-related traits. Proximity assessment to previously identified SNPs associated with asthma identified 17 unique SNPs within 1 MB of two of the 221 CpGs. EWAS in 182 individuals with epigenetic data at a second timepoint (T2) identified 49 significant CpGs. EWAS Atlas enrichment analysis indicated that 4 of the 49 were previously associated with asthma or asthma-related traits. Comparing the estimates of all the significant associations identified across the two time points (271 in total) yielded a correlation of 0.81.

Conclusion

We identified 270 unique CpGs that were associated with PC scores generated from 35 clinical markers of asthma, either cross-sectionally or 10 years later. A strong correlation was present between effect sizes at the 2 timepoints. Most associations were identified for PC7, which captured blood eosinophil counts and immunoglobulin levels and many of these CpGs have previous associations in earlier studies of asthma and asthma-related traits. The results point to using this robust DNA methylation profile as a new, stable biomarker for asthma.

Androgen receptor DNA methylation is an independent determinant of glucose metabolic disorders in women; testosterone plays a moderating effect

BACKGROUND

We have previously shown that serum testosterone was associated with impaired fasting glucose (IFG) and type 2 diabetes (T2D). Testosterone can be acting through binding the androgen receptor (AR). Therefore, we aimed to explore the independent associations of AR DNA methylation (ARm) with IFG and T2D and the moderation effects of serum testosterone on the associations.

METHODS

A case-control study with 1065 participants including 461 men and 604 women was performed. ARm in peripheral blood sample and serum testosterone were measured using pyrosequeuncing and liquid chromatography-tandem mass, respectively. Multivariable logistic regression was performed to estimate the associations of ARm (including 2 cytosine-phosphoguanine [CpG] islands and average methylation levels) with different glucose status. Serum testosterone was used as a moderator to estimate the moderation effect.

RESULTS

After multivariate adjustment, CpG 1, 2 and CpG average methylation were all significantly associated with IFG (CpG 1: Odds ratio (OR) = 4.80, 95% confidence interval (CI): 2.24-10.27; CpG 2: OR = 4.35, 95% CI: 2.50-7.58; CpG average: OR = 11.73, 95% CI: 5.36-25.67) in women. In addition, testosterone played negative moderation effects in above associations. Moreover, no significant independent associations of methylation levels with T2D was observed both in men and women.

CONCLUSION

Our findings demonstrate that ARm was positively associated with IFG in women and the associations would be weakened by testosterone. The individuals experiencing low testosterone and ARm levels reported a lower state of IFG than those who experienced high levels of testosterone and ARm in women.

MicroRNA-30a-5pme: a novel diagnostic and prognostic biomarker for clear cell renal cell carcinoma in tissue and urine samples

Background

The rising incidence of renal cell carcinomas (RCC) constitutes a significant challenge owing to risk of overtreatment. Because aberrant microRNA (miR) promoter methylation contributes to cancer development, we investigated whether altered miR-30a-5p expression associates with DNA promoter methylation and evaluated the usefulness as clear cell RCC (ccRCC) diagnostic and prognostic markers.

Methods

Genome-wide methylome and RNA sequencing data from a set of ccRCC and normal tissue samples from The Cancer Genome Atlas (TCGA) database were integrated to identify candidate CpG loci involved in cancer onset. MiR-30a-5p expression and promoter methylation were quantitatively assessed by PCR in a tissue set (Cohort #1) and urine sets (Cohorts #2 and 3) from IPOPorto and Homburg University Hospital. Non-parametric tests were used for comparing continuous variables. MiR-30a-5p promoter methylation (miR-30a-5p^me) performance as diagnostic (receiver operator characteristics [ROC] - validity estimates) and prognostic [metastasis-free (MFS) and disease-specific survival (DSS)] biomarker was further validated in urine samples from ccRCC patients by Kaplan Meier curves (with log rank) and both univariable and multivariable analysis.

Results

Two significant hypermethylated CpG loci in TCGA ccRCC samples, correlating with miR-30a-5p transcriptional downregulation, were disclosed. MiR-30a-5p^me in ccRCC tissues was confirmed in an independent patient’s cohort of IPOPorto and associated with shorter time to relapse. In urine samples, miR-30a-5p^me levels identified cancer both in testing and validation cohorts, with 83% sensitivity/53% specificity and 63% sensitivity/67% specificity, respectively. Moreover, higher miR-30a-5p^me levels independently predicted metastatic dissemination and survival.

Conclusion

To the best of our knowledge, this is the first study validating the diagnostic and prognostic potential of miR-30a-5p^me for ccRCC in urine samples, providing new insights for its clinical usefulness as non-invasive cancer biomarker.

Genome-wide analysis of DNA methylation identifies two CpG sites for the early screening of colorectal cancer

Aim: To identify a panel of DNA methylation markers for the early diagnosis of colorectal cancer (CRC). Materials & methods: Using public omics data and our pyrosequencing data, we developed and validated a global methylation model and a CpG-methylation-based model for CRC screening. Results: Both of the models yielded high sensitivity and specificity for distinguishing CRC and its precursors (colorectal adenoma and colorectal laterally spreading tumor) from normal controls in eight independent datasets and our newly collected samples. More importantly, the two-CpG-based model showed high specificity in excluding inflammatory bowel diseases and other 13 cancer types. Conclusion: A diagnostic model based on two CpGs (cg09239744 and cg12587766) may be a powerful tool for CRC screening.

Diverse and dynamic DNA modifications in brain and diseases

DNA methylation is a class of epigenetic modification essential for coordinating gene expression timing and magnitude throughout normal brain development and for proper brain function following development. Aberrant methylation changes are associated with changes in chromatin architecture, transcriptional alterations and a host of neurological disorders and diseases. This review highlights recent advances in our understanding of the methylome's functionality and covers potential new roles for DNA methylation, their readers, writers, and erasers. Additionally, we examine novel insights into the relationship between the methylome, DNA-protein interactions, and their contribution to neurodegenerative diseases. Lastly, we outline the gaps in our knowledge that will likely be filled through the widespread use of newer technologies that provide greater resolution into how individual cell types are affected by disease and the contribution of each individual modification site to disease pathogenicity.

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94-0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05-0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article.

Phenotype-independent DNA methylation changes in prostate cancer

Background

Human prostate cancers display numerous DNA methylation changes compared to normal tissue samples. However, definitive identification of features related to the cells’ malignant status has been compromised by the predominance of cells with luminal features in prostate cancers.

Methods

We generated genome-wide DNA methylation profiles of cell subpopulations with basal or luminal features isolated from matched prostate cancer and normal tissue samples.

Results

Many frequent DNA methylation changes previously attributed to prostate cancers are here identified as differences between luminal and basal cells in both normal and cancer samples. We also identified changes unique to each of the two cancer subpopulations. Those specific to cancer luminal cells were associated with regulation of metabolic processes, cell proliferation and epithelial development. Within the prostate cancer TCGA dataset, these changes were able to distinguish not only cancers from normal samples, but also organ-confined cancers from those with extraprostatic extensions. Using changes present in both basal and luminal cancer cells, we derived a new 17-CpG prostate cancer signature with high predictive power in the TCGA dataset.

Conclusions

This study demonstrates the importance of comparing phenotypically matched prostate cell populations from normal and cancer tissues to unmask biologically and clinically relevant DNA methylation changes.

Gendermetrics of cancer research: results from a global analysis on prostate cancer

BACKGROUND

The present study aims to elucidate the success concerning gender equality in cancer research in the last decade (from 2008 to 2017) with prostate cancer as the target parameter.

RESULTS

31.7% of all authorships and 36.3% of the first, 32.5% of the co- and 22.6% of the last authorships were held by women. The corresponding female-to-male odds ratio is 1.26 (CI: 1.22-1.30) for first, 1.15 (CI: 1.12-1.18) for co- and 0.59 (CI: 0.57-0.62) for last authorships. The annual growth rates are 0.6% overall and 0.9% for first, 0.2% for co-authorships, and 2.8% for last authorships. Women are slightly underrepresented at prestigious authorships compared to men. The female underrepresentation accentuates in articles with many authors that attract the highest citation rates. Multi-author articles with male key authors are more frequently cited. Men publish more articles compared to women (61.8% male authors are responsible for 68.3% of the authorships) and are overrepresented at productivity levels of more than 1 article per author. Major regional differences were found with best female odds in Sweden, Brazil, and Austria. The prognosis for the next decade forecasts a harmonization of authorship odds.

CONCLUSION

Prostate cancer research is characterized by a career dichotomy with few women in academic leadership positions and many female early career researchers. This career dichotomy has been narrowed in the last decade and will likely be further reduced in the future.

METHODS

On the basis of the Gendermetrics Platform, a total of 26,234 articles related to prostate cancer research were analyzed.

Aberrant DNA methylation of GATA binding protein 3 (GATA3), interleukin-4 (IL-4), and transforming growth factor-β (TGF-β) promoters in Behcet's disease

The pathogenesis of Behcet's disease (BD) remains poorly understood. The purpose of this study was to investigate whether an aberrant DNA methylation of transcriptional and inflammatory factors, including TBX21, GATA3, RORγt, FOXP3, IFN-γ, IL-4, IL-17A and TGF-β, in CD4⁺T confers risk to BD. We found that the promoter methylation level of GATA3, IL-4 and TGF-β was significantly up-regulated in active BD patients and negatively correlated with the corresponding mRNA expression. The mRNA expression of GATA3 and TGF-β was markedly down-regulated in active BD patients compared to healthy individuals. Treatment with corticosteroids and cyclosporine (CsA) resulted in a decrease of the methylation level of GATA3 and TGF-β in inactive BD patients. Our results suggest that an aberrant DNA methylation of GATA3 and TGF-β is associated with their mRNA expression and participates in the pathogenesis of BD.