Blood‐derived DNA methylation predictors of mortality discriminate tumor and healthy tissue in multiple organs

Zhang, Yan; Bewerunge-Hudler, Melanie; Schick, Matthias; Burwinkel, Barbara; Herpel, Esther; Hoffmeister, Michael; Brenner, Hermann

doi:10.1002/1878-0261.12738

Cited by 8 publications

(7 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, it remains an open question in the field as to whether our blood-derived methylation results can be transferred to other cell types relevant to cancer development. A recent paper by Zhang et al suggests that blood-derived DNA methylation markers could mirror changes in difficult-to-access tissues relevant to cancer development [59]. While we were not able to study this particular question given our study methods, our results provide support for investigating these questions more directly in future studies.…”

Section: Discussionmentioning

confidence: 47%

The Effects of Exercise Duration and Intensity on Breast Cancer-Related DNA Methylation: A Randomized Controlled Trial

Gillman

Helmuth

Koljack

et al. 2021

Cancers

View full text Add to dashboard Cite

Emerging research suggests that one mechanism through which physical activity may decrease cancer risk is through its influence on the methylation of genes associated with cancer. The purpose of the current study was to prospectively test, using a rigorous experimental design, whether aerobic exercise affects DNA methylation in genes associated with breast cancer, as well as whether quantity of exercise completed affects change in DNA methylation in a dose–response manner. 276 women (M age = 37.25, SD = 4.64) were recruited from the Denver metro area for a randomized controlled trial in which participants were assigned to a supervised aerobic exercise program varying in a fully crossed design by intensity (55–65% versus 75–85% of VO2max) and duration (40 versus 20 min per session). DNA methylation was assessed via blood samples provided at baseline, after completing a 16-week supervised exercise intervention, and six months after the intervention. 137 participants completed the intervention, and 81 had viable pre-post methylation data. Contrary to our hypotheses, total exercise volume completed in kcal/kg/week was not associated with methylation from baseline to post-intervention for any of the genes of interest. An increase in VO2max over the course of the intervention, however, was associated with decreased post-intervention methylation of BRCA1, p = 0.01. Higher levels of self-reported exercise during the follow-up period were associated with lower levels of GALNT9 methylation at the six-month follow-up. This study provides hypothesis-generating evidence that increased exercise behavior and or increased fitness might affect methylation of some genes associated with breast cancer to reduce risk.

show abstract

Section: Discussionmentioning

confidence: 47%

The Effects of Exercise Duration and Intensity on Breast Cancer-Related DNA Methylation: A Randomized Controlled Trial

Gillman

Helmuth

Koljack

et al. 2021

Cancers

View full text Add to dashboard Cite

show abstract

“…Because genome-wide DNA methylation studies show shared epigenomic features between aging and cancer, providing explanations for their possible molecular links [ 12 , 13 , 14 ], the role for DNAm age in carcinogenesis has been recently explored. The obtained results indeed indicate that DNAmaa increases cancer risk, promote cancer initiation or progression, and predict poor patient outcomes in different types of cancer [ 10 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. However, in some other cancers, DNAmad is associated with aggressive diseases and shorter survival.…”

Section: Introductionmentioning

confidence: 67%

“…Indeed, recent genome-wide analyses of DNA methylation have shown particular features shared between aging and cancer [ 1 , 4 , 13 , 14 ]. Importantly, the accelerated DNAm age in normal cells derived from healthy individuals is associated with increased cancer risk and cancer-related mortality [ 8 , 9 , 11 , 17 , 18 , 21 , 28 , 29 , 30 , 31 ]. Devall et al observed that higher DNAm age rates contributed to young age onset of colon cancer in African Americans [ 10 ].…”

Section: Discussionmentioning

confidence: 99%

DNA Methylation Age Drift Is Associated with Poor Outcomes and De-Differentiation in Papillary and Follicular Thyroid Carcinomas

Liu

Wang

Xiu

et al. 2021

Cancers

View full text Add to dashboard Cite

Alterations in global DNA methylation play a critical role in both aging and cancer, and DNA methylation (DNAm) age drift has been implicated in cancer risk and pathogenesis. In the present study, we analyzed the TCGA cohort of papillary and follicular thyroid carcinoma (PTC and FTC) for their DNAm age and association with clinic-pathological features. In 54 noncancerous thyroid (NT) samples, DNAm age was highly correlated with patient chronological age (R2 = 0.928, p = 2.6 × 10−31), but drifted to younger than chronological age in most specimens, especially those from patients >50 years old. DNAm age in 502 tumors was also correlated with patient chronological age, but to a much lesser extent (R2 = 0.403). Highly drifted DNAm age (HDDA) was identified in 161 tumors, among which were 101 with DNAm age acceleration while 60 with DNAm age deceleration. Tumors with HDDA were characterized by the robust aberrations in metabolic activities, extracellular microenvironment components and inflammation/immunology responses, and dedifferentiation. Importantly, HDDA in tumors independently predicted shorter disease-free survival of patients. Collectively, NT thyroids from TC patients have younger DNAm age, while HDDA frequently occurs in TCs, and contributes to the TC progression and poor patient outcomes. HDDA may serve as a new prognostic factor for TCs.

show abstract

“…Finally, it is noteworthy to highlight that current research shows evidence that some DNA methylation markers derived from blood can mimic DNA methylation signatures found in internal tissues from primary cancer sources [ 34 ]. Therefore, this research can potentially help identify early-stage cancer by testing blood-derived samples using the proposed classification model.…”

Section: Resultsmentioning

confidence: 99%

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

2022

View full text Add to dashboard Cite

Background DNA Methylation is one of the most important epigenetic processes that are crucial to regulating the functioning of the human genome without altering the DNA sequence. DNA Methylation data for cancer patients are becoming more accessible than ever, which is attributed to newer DNA sequencing technologies, notably, the relatively low-cost DNA microarray technology by Illumina Infinium. This technology makes it possible to study DNA methylation at hundreds of thousands of different loci. Currently, most of the research found in the literature focuses on the discovery of DNA methylation markers for specific cancer types. A relatively small number of studies have attempted to find unified DNA methylation biomarkers that can diagnose different types of cancer (pan-cancer classification). Results In this study, the aim is to conduct a pan-classification of cancer disease. We retrieved individual data for different types of cancer patients from The Cancer Genome Atlas (TCGA) portal. We selected data for many cancer types: Breast Cancer (BRCA), Ovary Cancer (OV), Stomach Cancer (STOMACH), Colon Cancer (COAD), Kidney Cancer (KIRC), Liver Cancer (LIHC), Lung Cancer (LUSC), Prostate Cancer (PRAD) and Thyroid cancer (THCA). The data was pre-processed and later used to build the required dataset. The system that we developed consists of two main stages. The purpose of the first stage is to perform feature selection and, therefore, decrease the dimensionality of the DNA methylation loci (features). This is accomplished using an unsupervised metaheuristic technique. As for the second stage, we used supervised machine learning and developed deep neural network (DNN) models to help classify the samples’ malignancy status and cancer type. Experimental results showed that compared to recently published methods, our proposed system achieved better classification results in terms of recall, and similar and higher results in terms of precision and accuracy. The proposed system also achieved an excellent receiver operating characteristic area under the curve (ROC AUC) values varying from 0.85 to 0.89. Conclusions This research presented an effective new approach to classify different cancer types based on DNA methylation data retrieved from TCGA. The performance of the proposed system was compared to recently published works, using different performance metrics. It provided better results, confirming the effectiveness of the proposed method for classifying different cancer types based on DNA methylation data.

show abstract

Blood‐derived DNA methylation predictors of mortality discriminate tumor and healthy tissue in multiple organs

Cited by 8 publications

References 49 publications

The Effects of Exercise Duration and Intensity on Breast Cancer-Related DNA Methylation: A Randomized Controlled Trial

The Effects of Exercise Duration and Intensity on Breast Cancer-Related DNA Methylation: A Randomized Controlled Trial

DNA Methylation Age Drift Is Associated with Poor Outcomes and De-Differentiation in Papillary and Follicular Thyroid Carcinomas

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation

Contact Info

Product

Resources

About