The association between aging and cancer is complex. Recent studies have developed measures of biological aging based on DNA methylation and called them “age acceleration.” We aimed to assess the ...associations of age acceleration with risk of and survival from seven common cancers. Seven case–control studies of DNA methylation and colorectal, gastric, kidney, lung, prostate and urothelial cancer and B‐cell lymphoma nested in the Melbourne Collaborative Cohort Study were conducted. Cancer cases, vital status and cause of death were ascertained through linkage with cancer and death registries. Conditional logistic regression and Cox models were used to estimate odds ratios (OR) and hazard ratios (HR) and 95% confidence intervals (CI) for associations of five age acceleration measures derived from the Human Methylation 450 K Beadchip assay with cancer risk (N = 3,216 cases) and survival (N = 1,726 deaths), respectively. Epigenetic aging was associated with increased cancer risk, ranging from 4% to 9% per five‐year age acceleration for the 5 measures considered. Heterogeneity by study was observed, with stronger associations for risk of kidney cancer and B‐cell lymphoma. An associated increased risk of death following cancer diagnosis ranged from 2% to 6% per five‐year age acceleration, with no evidence of heterogeneity by cancer site. Cancer risk and mortality were increased by 15–30% for the fourth versus first quartile of age acceleration. DNA methylation‐based measures of biological aging are associated with increased cancer risk and shorter cancer survival, independently of major health risk factors.
What's new?
Aging is associated with profound changes in DNA methylation levels. These can be used to build accurate age predictors (“epigenetic clocks”) that deviate from chronological age by only a few years, a phenomenon named “age acceleration”. In this study of seven types of cancer, the authors found that age acceleration was associated with both increased cancer risk and decreased cancer survival, independently of major health risk factors. These results support the usefulness of methylation markers of biological aging as a tool to predict health outcomes and may provide valuable insight into the relationship between aging and cancer.
Recurrent tumor copy number variations (CNVs) in prostate cancer (PrCa) have predominantly been discovered in sporadic tumor cohorts. Here, we examined familial prostate tumors for novel CNVs as ...prior studies suggest these harbor distinct CNVs. Array comparative genomic hybridization of 12 tumors from an Australian PrCa family, PcTas9, highlighted multiple recurrent CNVs, including amplification of EEF2 (19p13.3) in 100% of tumors. The EEF2 CNV was examined in a further 26 familial and seven sporadic tumors from the Australian cohort and in 494 tumors unselected for family history from The Cancer Genome Atlas (TCGA). EEF2 overexpression was observed in seven PcTas9 tumors, in addition to seven other predominantly familial tumors (ntotal = 34%). EEF2 amplification was only observed in 1.4% of TCGA tumors, however 7.5% harbored an EEF2 deletion. Analysis of genes co‐expressed with EEF2 revealed significant upregulation of two genes, ZNF74 and ADSL, and downregulation of PLSCR1 in both EEF2 amplified familial tumors and EEF2 deleted TCGA tumors. Furthermore, in TCGA tumors, EEF2 amplification and deletion were significantly associated with a higher Gleason score. In summary, we identified a novel PrCa CNV that was predominantly amplified in familial tumors and deleted in unselected tumors. Our results provide further evidence that familial tumors harbor distinct CNVs, potentially due to an inherited predisposition, but also suggest that regardless of how EEF2 is dysregulated, a similar set of genes involved in key cancer pathways are impacted. Given the current lack of gene‐based biomarkers and clinical targets in PrCa, further investigation of EEF2 is warranted.
DNA methylation may be one of the mechanisms by which alcohol consumption is associated with the risk of disease. We conducted a large‐scale, cross‐sectional, genome‐wide DNA methylation association ...study of alcohol consumption and a longitudinal analysis of repeated measurements taken several years apart. Using the Illumina HumanMethylation450 BeadChip, DNA methylation was measured in blood samples from 5606 Melbourne Collaborative Cohort Study (MCCS) participants. For 1088 of them, these measures were repeated using blood samples collected a median of 11 years later. Associations between alcohol intake and blood DNA methylation were assessed using linear mixed‐effects regression models. Independent data from the London Life Sciences Prospective Population (LOLIPOP) (N = 4042) and Cooperative Health Research in the Augsburg Region (KORA) (N = 1662) cohorts were used to replicate associations discovered in the MCCS. Cross‐sectional analyses identified 1414 CpGs associated with alcohol intake at P < 10−7, 1243 of which had not been reported previously. Of these novel associations, 1078 were replicated (P < .05) using LOLIPOP and KORA data. Using the MCCS data, we also replicated 403 of 518 previously reported associations. Interaction analyses suggested that associations were stronger for women, non‐smokers, and participants genetically predisposed to consume less alcohol. Of the 1414 CpGs, 530 were differentially methylated (P < .05) in former compared with current drinkers. Longitudinal associations between the change in alcohol intake and the change in methylation were observed for 513 of the 1414 cross‐sectional associations. Our study indicates that alcohol intake is associated with widespread changes in DNA methylation across the genome. Longitudinal analyses showed that the methylation status of alcohol‐associated CpGs may change with alcohol consumption changes in adulthood.
We conducted a large‐scale epigenome‐wide association study of alcohol consumption. Cross‐sectional analyses identified 1414 CpG sites at which blood DNA methylation was associated with alcohol drinking. The majority of these associations had not been reported previously and were replicated using data from independent samples. Methylation changes appeared more pronounced in women, non‐smokers, and participants genetically predisposed to consume less alcohol; comparison of current, former, and never drinkers and longitudinal analyses showed that these changes are at least partially reversible. We conducted a large‐scale epigenome‐wide association study of alcohol consumption.
DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre‐diagnostic, peripheral blood ...samples is associated with lung cancer risk. We used a case‐control study nested within the EPIC‐Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case‐control pairs). We validated the top signals in 429 case‐control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p‐valuepooled = 4 × 10−17), cg03636183 in the F2RL3 gene (p‐valuepooled = 2 × 10 − 13), cg21566642 and cg05951221 in 2q37.1 (p‐valuepooled = 7 × 10−16 and 1 × 10−11 respectively), cg06126421 in 6p21.33 (p‐valuepooled = 2 × 10−15) and cg23387569 in 12q14.1 (p‐valuepooled = 5 × 10−7). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p‐valuesheterogeneity ≤ 1.8 x10 − 7), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p‐values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack‐years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.
What's new?
It is well known that smoking can cause lung cancer but the concept that it might do so by changing DNA methylation is only emerging. Here the authors identify six sites of methylation (CpGs), where methylation levels were associated with lung cancer risk after adjusting for smoking, current or former. Methylation of five of the CpGs was lowest in current smokers and increased in former smokers with time since quitting, supporting the growing evidence that smoking may lead to DNA methylation changes measurable in peripheral blood and useful as predictive markers for lung cancer risk, especially in former smokers.
Methylation marks of exposure to health risk factors may be useful markers of cancer risk as they might better capture current and past exposures than questionnaires, and reflect different individual ...responses to exposure. We used data from seven case-control studies nested within the Melbourne Collaborative Cohort Study of blood DNA methylation and risk of colorectal, gastric, kidney, lung, prostate and urothelial cancer, and B-cell lymphoma (N cases = 3123). Methylation scores (MS) for smoking, body mass index (BMI), and alcohol consumption were calculated based on published data as weighted averages of methylation values. Rate ratios (RR) and 95% confidence intervals for association with cancer risk were estimated using conditional logistic regression and expressed per SD increase of the MS, with and without adjustment for health-related confounders. The contribution of MS to discriminate cases from controls was evaluated using the area under the curve (AUC). After confounder adjustment, we observed: large associations (RR = 1.5-1.7) with lung cancer risk for smoking MS; moderate associations (RR = 1.2-1.3) with urothelial cancer risk for smoking MS and with mature B-cell neoplasm risk for BMI and alcohol MS; moderate to small associations (RR = 1.1-1.2) for BMI and alcohol MS with several cancer types and cancer overall. Generally small AUC increases were observed after inclusion of several MS in the same model (colorectal, gastric, kidney, urothelial cancers: +3%; lung cancer: +7%; B-cell neoplasms: +8%). Methylation scores for smoking, BMI and alcohol consumption show independent associations with cancer risk, and may provide some improvements in risk prediction.
The question of whether it is appropriate to attribute authorship to deceased individuals of original studies in the biomedical literature is contentious. Authorship guidelines utilized by journals ...do not provide a clear consensus framework that is binding on those in the field. To guide and inform the implementation of authorship frameworks it would be useful to understand the extent of the practice in the scientific literature, but studies that have systematically quantified the prevalence of this phenomenon in the biomedical literature have not been performed to date. To address this issue, we quantified the prevalence of publications by deceased authors in the biomedical literature from the period 1990–2020. We screened 2,601,457 peer-reviewed papers from the full text Europe PubMed Central database. We applied natural language processing, stringent filtering and manual curation to identify a final set of 1,439 deceased authors. We then determined these authors published a total of 38,907 papers over their careers with 5,477 published after death. The number of deceased publications has been growing rapidly, a 146-fold increase since the year 2000. This rate of increase was still significant when accounting for the growing total number of publications and pool of authors. We found that more than 50% of deceased author papers were first submitted after the death of the author and that over 60% of these papers failed to acknowledge the deceased authors status. Most deceased authors published less than 10 papers after death but a small pool of 30 authors published significantly more. A pool of 266 authors published more than 90% of their total publications after death. Our analysis indicates that the attribution of deceased authorship in the literature is not an occasional occurrence but a burgeoning trend. A consensus framework to address authorship by deceased scientists is warranted.
We conducted a genome-wide association study of blood DNA methylation and smoking, attempted replication of previously discovered associations, and assessed the reversibility of smoking-associated ...methylation changes. DNA methylation was measured in baseline peripheral blood samples for 5,044 participants in the Melbourne Collaborative Cohort Study. For 1,032 participants, these measures were repeated using blood samples collected at follow-up, a median of 11 years later. A cross-sectional analysis of the association between smoking and DNA methylation and a longitudinal analysis of changes in smoking status and changes in DNA methylation were conducted. We used our cross-sectional analysis to replicate previously reported associations for current (N = 3,327) and former (N = 172) smoking. A comprehensive smoking index accounting for the biological half-life of smoking compounds and several aspects of smoking history was constructed to assess the reversibility of smoking-induced methylation changes. This measure of lifetime exposure to smoking allowed us to detect more associations than comparing current with never smokers. We identified 4,496 cross-sectional associations at P < 10
−7
, including 3,296 annotated to 1,326 genes that were not previously implicated in smoking-associated DNA methylation changes at this significance threshold. We replicated the majority of previously reported associations (P < 10
−7
) for current and former smokers. In our data, we observed for former smokers a substantial degree of return to the methylation levels of never smokers, compared with current smokers (median: 74%, IQR = 63-86%), corresponding to small values (median: 2.75, IQR = 1.5-5.25) for the half-life parameter of the comprehensive smoking index. Longitudinal analyses identified 368 sites at which methylation changed upon smoking cessation. Our study demonstrates the usefulness of the comprehensive smoking index to detect associations between smoking and DNA methylation at CpGs across the genome, replicates the vast majority of previously reported associations, and quantifies the reversibility of smoking-induced methylation changes.
Flowering is an important agronomic trait that determines crop yield. Soybean is a major oilseed legume crop used for human and animal feed. Legumes have unique vegetative and floral complexities. ...Our understanding of the molecular basis of flower initiation and development in legumes is limited. Here, we address this by using a computational approach to examine flowering regulatory genes in the soybean genome in comparison to the most studied model plant, Arabidopsis. For this comparison, a genome-wide analysis of orthologue groups was performed, followed by an in silico gene expression analysis of the identified soybean flowering genes. Phylogenetic analyses of the gene families highlighted the evolutionary relationships among these candidates. Our study identified key flowering genes in soybean and indicates that the vernalisation and the ambient-temperature pathways seem to be the most variant in soybean. A comparison of the orthologue groups containing flowering genes indicated that, on average, each Arabidopsis flowering gene has 2-3 orthologous copies in soybean. Our analysis highlighted that the CDF3, VRN1, SVP, AP3 and PIF3 genes are paralogue-rich genes in soybean. Furthermore, the genome mapping of the soybean flowering genes showed that these genes are scattered randomly across the genome. A paralogue comparison indicated that the soybean genes comprising the largest orthologue group are clustered in a 1.4 Mb region on chromosome 16 of soybean. Furthermore, a comparison with the undomesticated soybean (Glycine soja) revealed that there are hundreds of SNPs that are associated with putative soybean flowering genes and that there are structural variants that may affect the genes of the light-signalling and ambient-temperature pathways in soybean. Our study provides a framework for the soybean flowering pathway and insights into the relationship and evolution of flowering genes between a short-day soybean and the long-day plant, Arabidopsis.
Genetic variant effect prediction algorithms are used extensively in clinical genomics and research to determine the likely consequences of amino acid substitutions on protein function. It is vital ...that we better understand their accuracies and limitations because published performance metrics are confounded by serious problems of circularity and error propagation. Here, we derive three independent, functionally determined human mutation datasets, UniFun, BRCA1-DMS and TP53-TA, and employ them, alongside previously described datasets, to assess the pre-eminent variant effect prediction tools.
Apparent accuracies of variant effect prediction tools were influenced significantly by the benchmarking dataset. Benchmarking with the assay-determined datasets UniFun and BRCA1-DMS yielded areas under the receiver operating characteristic curves in the modest ranges of 0.52 to 0.63 and 0.54 to 0.75, respectively, considerably lower than observed for other, potentially more conflicted datasets.
These results raise concerns about how such algorithms should be employed, particularly in a clinical setting. Contemporary variant effect prediction tools are unlikely to be as accurate at the general prediction of functional impacts on proteins as reported prior. Use of functional assay-based datasets that avoid prior dependencies promises to be valuable for the ongoing development and accurate benchmarking of such tools.
PDF
