Do age‐associated DNA methylation changes increase the risk of malignant transformation?

Wagner, Wolfgang; Weidner, Carola I.; Lin, Qiong

doi:10.1002/bies.201400063

Cited by 22 publications

(19 citation statements)

References 44 publications

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“…A multivariate model with these 99 CpGs facilitated reliable age-predictions in the Hannum dataset of normal blood with a mean absolute deviation (MAD) of only 4.12 years (Pearson correlation R 2 = 0.87) [ 8 ]. In contrast, the same model did not reveal any correlation between chronological and predicted age of AML profiles ( Fig 1C ; R 2 < 0.001) [ 3 ]. In average the predicted age of AML patients was 21 years older than their chronological age, but there were also several AML patients which were predicted to be younger.…”

Section: Resultsmentioning

confidence: 99%

“…Somatic mutations are usually considered as tumor-initiating events [ 1 ]. However, aging is also accompanied by specific epigenetic modifications, which may also contribute to aberrant chromatin conformation and stability [ 2 , 3 ]. Such epigenetic modifications are particularly observed in DNA methylation (DNAm) changes that resemble addition or removal of methyl groups to cytosines in a CpG dinucleotide context.…”

Section: Introductionmentioning

confidence: 99%

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Epigenetic Aging Signatures Are Coherently Modified in Cancer

2015

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Aging is associated with highly reproducible DNA methylation (DNAm) changes, which may contribute to higher prevalence of malignant diseases in the elderly. In this study, we analyzed epigenetic aging signatures in 5,621 DNAm profiles of 25 cancer types from The Cancer Genome Atlas (TCGA). Overall, age-associated DNAm patterns hardly reflect chronological age of cancer patients, but they are coherently modified in a non-stochastic manner, particularly at CpGs that become hypermethylated upon aging in non-malignant tissues. This coordinated regulation in epigenetic aging signatures can therefore be used for aberrant epigenetic age-predictions, which facilitate disease stratification. For example, in acute myeloid leukemia (AML) higher epigenetic age-predictions are associated with increased incidence of mutations in RUNX1, WT1, and IDH2, whereas mutations in TET2, TP53, and PML-PARA translocation are more frequent in younger age-predictions. Furthermore, epigenetic aging signatures correlate with overall survival in several types of cancer (such as lower grade glioma, glioblastoma multiforme, esophageal carcinoma, chromophobe renal cell carcinoma, cutaneous melanoma, lung squamous cell carcinoma, and neuroendocrine neoplasms). In conclusion, age-associated DNAm patterns in cancer are not related to chronological age of the patient, but they are coordinately regulated, particularly at CpGs that become hypermethylated in normal aging. Furthermore, the apparent epigenetic age-predictions correlate with clinical parameters and overall survival in several types of cancer, indicating that regulation of DNAm patterns in age-associated CpGs is relevant for cancer development.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Epigenetic Aging Signatures Are Coherently Modified in Cancer

2015

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“…Scientists note the similarities between chromatin changes seen in senescent cells (no longer dividing) and those seen in cancer cells. DNA methylation age measures applied to cancer cells show that “tumors appear to have aged 40% more than matched normal tissue from the same individual” (Wagner, Weidner & Lin 2015, 22). Suppression or inappropriate activation of gene expression can explain changes in cell physiology that aren’t caused by a sequence mutation.…”

Section: Old Age: Epigenetic Drift and The Epigenetic Clockmentioning

confidence: 99%

How the genome got a life span

Lappé

Landecker

2015

New Genetics and Society

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In the space of little more than a decade, ideas of the human genome have shifted significantly, with the emergence of the notion that the genome an individual changes with development, age, disease, environmental inputs, and time. This paper examines the emergence of the genome with a life span, one that experiences drift, instability and mutability, and a host of other temporal changes. We argue that developments in chromatin biology have provided the basis for this genomic embodiment of experience and exposure. We analyze how time has come to matter for the genome through chromatin, providing analysis of examples in which the human life course is being explored as a set of material changes to chromatin. A genome with a lifespan aligns the molecular and the experiential in new ways, shifting ideas of life stages, their interrelation, and the temporality of health and disease.

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“… 12 , 13 , 17 , 24 , 25 This method has also been applied to the field of cancer biology, where changes in DNAm and age acceleration of premalignant tissue is associated with development of malignancy. 26 , 27 These previous reports add to the promise of measurement of DNAm age and age acceleration as a potential tool for candidate assessment and risk stratification after transplantation. Assessment of DNAm age of PBMC may be a better guide to adjustment of immunosuppression such as minimization of ATG or tacrolimus dosing, as has been suggested with varying degrees of success based on patient chronological age.…”

Section: Discussionmentioning

confidence: 91%

DNA Methylation Age Is More Closely Associated With Infection Risk Than Chronological Age in Kidney Transplant Recipients

Schaenman

Zhou

Guo

et al. 2020

Transplantation Direct

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Background. Older kidney transplant recipients demonstrate increased rates of infection but decreased rates of rejection compared with younger recipients, suggesting that older transplant patients are functionally overimmunosuppressed. We hypothesized that this is a consequence of reduction in immunological activity due to biological aging and that an immune biological age, as determined by DNA methylation (DNAm), would be associated more strongly with incidence of infection than chronological age. Methods. DNAm analysis was performed on peripheral blood mononuclear cell collected from 60 kidney transplant recipients representing older (≥age 60 y) and younger (aged 30–59 y) patients 3 months after transplantation. DNAm age was calculated based on methylation status of a panel of CpG sites, which have been previously identified as indicative of biological age. Results. Correlation was seen between chronological and DNAm age; however, there were many patients with significant differences (either acceleration or slowing) between DNAm age and chronological age. A statistically significant association was seen between increased DNAm age and incidence of infection in the first year after kidney transplantation, whereas no significant association was seen between chronological age and infection. Conclusions. Assessment of DNAm age holds promise as an approach for patient evaluation and individualization of immune suppression regimens. This analysis may provide insights into the immunological mechanism behind increased incidence of infection observed in older transplant patients. The ability to measure biological age would allow for patient risk stratification and individualization of immunosuppression, improving outcomes for the growing numbers of older patients undergoing kidney transplantation.

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Do age‐associated DNA methylation changes increase the risk of malignant transformation?

Cited by 22 publications

References 44 publications

Epigenetic Aging Signatures Are Coherently Modified in Cancer

Epigenetic Aging Signatures Are Coherently Modified in Cancer

How the genome got a life span

DNA Methylation Age Is More Closely Associated With Infection Risk Than Chronological Age in Kidney Transplant Recipients

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