Alcohol and Dietary Folate Intake and Promoter CpG Island Methylation in Clear-Cell Renal Cell Cancer

The association of healthy lifestyle index score and the risk of renal cell cancer in the Netherlands cohort study

BACKGROUND

Diet, alcohol, cigarette smoking, physical inactivity, and body mass index have been studied as risk factors for renal cell cancer (RCC). The joint effects of these lifestyle factors, captured as Healthy Lifestyle Index (HLI), were examined in one previous study. This study aims to investigate the association between HLI score and RCC risk in the prospective Netherlands Cohort Study (NLCS).

METHODS

A case-cohort analysis (3,767 subcohort members, 485 cases) was conducted using NLCS data (n = 120,852). Data on aforementioned risk factors was used to calculate HLI score, ranging 0-20, with higher scores reflecting healthier lifestyles. RCC occurrence was obtained by record linkage to cancer registries. Multivariable-adjusted proportional hazard models were used to calculate Hazard Ratios (HR) and 95% Confidence Intervals (95%CI).

RESULTS

Compared to participants in the unhealthiest HLI category, participants within the healthiest category had a lower RCC risk (HR = 0.79, 95%CI = 0.56-1.10, p for trend 0.045). A standard deviation (± 3-unit) increase in HLI score was not statistically significantly associated with a lower RCC risk (HR = 0.92, 95%CI = 0.83-1.01). This association was stronger after excluding diet or alcohol from the score, although confidence intervals overlap.

CONCLUSIONS

Adherence to a healthy lifestyle was weakly, though not statistically significantly, associated with a lower RCC risk.

Development of a prognostic risk model for clear cell renal cell carcinoma by systematic evaluation of DNA methylation markers

BACKGROUND

Current risk models for renal cell carcinoma (RCC) based on clinicopathological factors are sub-optimal in accurately identifying high-risk patients. Here, we perform a head-to-head comparison of previously published DNA methylation markers and propose a potential prognostic model for clear cell RCC (ccRCC).

PATIENTS AND METHODS

Promoter methylation of PCDH8, BNC1, SCUBE3, GREM1, LAD1, NEFH, RASSF1A, GATA5, SFRP1, CDO1, and NEURL was determined by nested methylation-specific PCR. To identify clinically relevant methylated regions, The Cancer Genome Atlas (TCGA) was used to guide primer design. Formalin-fixed paraffin-embedded (FFPE) tissue samples from 336 non-metastatic ccRCC patients from the prospective Netherlands Cohort Study (NLCS) were used to develop a Cox proportional hazards model using stepwise backward elimination and bootstrapping to correct for optimism. For validation purposes, FFPE ccRCC tissue of 64 patients from the University Hospitals Leuven and a series of 232 cases from The Cancer Genome Atlas (TCGA) were used.

RESULTS

Methylation of GREM1, GATA5, LAD1, NEFH, NEURL, and SFRP1 was associated with poor ccRCC-specific survival, independent of age, sex, tumor size, TNM stage or tumor grade. Moreover, the association between GREM1, NEFH, and NEURL methylation and outcome was shown to be dependent on the genomic region. A prognostic biomarker model containing GREM1, GATA5, LAD1, NEFH and NEURL methylation in combination with clinicopathological characteristics, performed better compared to the model with clinicopathological characteristics only (clinical model), in both the NLCS and the validation population with a c-statistic of 0.71 versus 0.65 and a c-statistic of 0.95 versus 0.86 consecutively. However, the biomarker model had limited added prognostic value in the TCGA series with a c-statistic of 0.76 versus 0.75 for the clinical model.

CONCLUSION

In this study we performed a head-to-head comparison of potential prognostic methylation markers for ccRCC using a novel approach to guide primers design which utilizes the optimal location for measuring DNA methylation. Using this approach, we identified five methylation markers that potentially show prognostic value in addition to currently known clinicopathological factors.

Association of Processed Meats and Alcohol Consumption with Renal Cell Carcinoma: A Worldwide Population-Based Study

The link between diet and renal cell carcinoma (RCC) is still unclear. The purpose of this study was to evaluate the association of diet with RCC's incidence and mortality rates worldwide. We conducted an ecological study including 170 countries, whose data on age-standardized (AS) incidence and mortality rates of RCC, dietary factors, and potentially confounding factors such as obesity, insufficient physical activity, tobacco smoking, hypertension, diabetes, and human development index (HDI) were collected and available on May 2020 from the Global Cancer Observatory, the Global Dietary Database, the Global Health Observatory data repository, the Diabetes Atlas 9th edition and the Human Development Report 2019. Univariable and multivariable linear regression analyses were performed to determine the association of dietary factors with incidence and mortality rates of RCC adjusted for the effects of population age and potentially confounding factors. Intake of processed meats and consumption of alcohol were both positively associated with AS incidence rates of RCC (β = 0.11, P < 0.001 and β = 0.1, P = 0.044, respectively). We suggest that high consumption of processed meats and/or alcohol is a risk factor for RCC. However, they were not associated with mortality. Further research is needed at an individual level.Supplemental data for this article is available online at https://doi.org/10.1080/01635581.2020.1856388.

Components of one-carbon metabolism and renal cell carcinoma: a systematic review and meta-analysis

PURPOSE

Little is known about the aetiology of renal cell carcinoma (RCC). Components of one-carbon (1C) metabolism, which are required for nucleotide synthesis and methylation reactions, may be related to risk of RCC but existing evidence is inconclusive. We conducted a systematic review and independent exposure-specific meta-analyses of dietary intake and circulating biomarkers of 1C metabolites and RCC risk.

METHODS

Medline and Embase databases were searched for observational studies investigating RCC or kidney cancer incidence or mortality in relation to components of 1C metabolism and 12 eligible articles were included in the meta-analyses. We used Bayesian meta-analyses to estimate summary relative risks (RRs) and 95% credible intervals (CrIs) comparing the highest versus lowest categories as well as the between-study heterogeneity.

RESULTS

We did not find convincing evidence of an association between any exposure (riboflavin, vitamin B6, folate, vitamin B12, methionine, homocysteine, choline, or betaine) and RCC risk. However, vitamin B6 biomarker status did have a protective (RR = 0.62) but imprecise (95% CrI 0.39-1.14) effect estimate and folate intake had a notable association as well (RR = 0.85, 95% CrI 0.71-1.01).

CONCLUSION

There was a lack of precision due largely to the low number of studies. Further investigation is warranted, especially for folate and vitamin B6, which had consistent suggestive evidence of a protective effect for both dietary intake and biomarker status. A unique strength of this review is the use of Bayesian meta-analyses which allowed for robust estimation of between-study heterogeneity.

Germline polymorphisms in the Von Hippel-Lindau and Hypoxia-inducible factor 1-alpha genes, gene-environment and gene-gene interactions and renal cell cancer

We investigated the relationship between germline single nucleotide polymorphisms (SNPs) in Von Hippel-Lindau (VHL) and Hypoxia-inducible factor 1-alpha (HIF1A), and their gene-environment and gene-gene interactions, and clear-cell RCC (ccRCC) risk. Furthermore, we assessed the relationship between VHL SNPs and VHL promoter methylation. Three VHL polymorphisms and one HIF1A polymorphism were genotyped in the Netherlands Cohort Study. In 1986, 120,852 participants aged 55–69 completed a self-administered questionnaire on diet and lifestyle and toenail clippings were collected. Toenail DNA was genotyped using the Sequenom MassARRAY platform. After 20.3 years, 3004 subcohort members and 406 RCC cases, of which 263 ccRCC cases, were eligible for multivariate case-cohort analyses. VHL_rs779805 was associated with RCC (Hazard Ratio (HR) 1.53; 95% Confidence Interval (CI) 1.07–2.17) and ccRCC risk (HR 1.88; 95% CI 1.25–2.81). No associations were found for other SNPs. Potential gene-environment interactions were found between alcohol consumption and selected SNPs. However, none remained statistically significant after multiple comparison correction. No gene-gene interactions were observed between VHL and HIF1A. VHL promoter methylation was not associated with VHL SNPs. VHL SNPs may increase (cc)RCC susceptibility. No associations were found between gene-environment and gene-gene interactions and (cc)RCC risk and between VHL promoter methylation and VHL SNPs.

ALDH1L1 and ALDH1L2 Folate Regulatory Enzymes in Cancer

Epidemiological studies implicate excess ethanol ingestion as a risk factor for several cancers and support the concept of a synergistic effect of chronic alcohol consumption and folate deficiency on carcinogenesis. Alcohol consumption affects folate-related genes and enzymes including two major folate-metabolizing enzymes, ALDH1L1 and ALDH1L2. ALDH1L1 (cytosolic 10-formyltetrahydrofolate dehydrogenase) is a regulatory enzyme in folate metabolism that controls the overall flux of one-carbon groups in folate-dependent biosynthetic pathways. It is strongly and ubiquitously down-regulated in malignant tumors via promoter methylation, and recent studies underscored this enzyme as a candidate tumor suppressor and potential marker of aggressive cancers. A related enzyme, ALDH1L2, is the mitochondrial homolog of ALDH1L1 encoded by a separate gene. In contrast to its cytosolic counterpart, ALDH1L2 is expressed in malignant tumors and cancer cell lines and was implicated in metastasis regulation. This review discusses the link between folate and cancer, modifying effects of alcohol consumption on folate-associated carcinogenesis, and putative roles of ALDH1L1 and ALDH1L2 in this process.