DNA methylation and cancer incidence: lymphatic-hematopoietic versus solid cancers in the Strong Heart Study

Blood DNA methylation and liver cancer in American Indians: evidence from the Strong Heart Study

PURPOSE

Liver cancer incidence among American Indians/Alaska Natives has risen over the past 20 years. Peripheral blood DNA methylation may be associated with liver cancer and could be used as a biomarker for cancer risk. We evaluated the association of blood DNA methylation with risk of liver cancer.

METHODS

We conducted a prospective cohort study in 2324 American Indians, between age 45 and 75 years, from Arizona, Oklahoma, North Dakota and South Dakota who participated in the Strong Heart Study between 1989 and 1991. Liver cancer deaths (n = 21) were ascertained using death certificates obtained through 2017. The mean follow-up duration (SD) for non-cases was 25.1 (5.6) years and for cases, 11.0 (8.8) years. DNA methylation was assessed from blood samples collected at baseline using MethylationEPIC BeadChip 850 K arrays. We used Cox regression models adjusted for age, sex, center, body mass index, low-density lipoprotein cholesterol, smoking, alcohol consumption, and immune cell proportions to examine the associations.

RESULTS

We identified 9 CpG sites associated with liver cancer. cg16057201 annotated to MRFAP1) was hypermethylated among cases vs. non-cases (hazard ratio (HR) for one standard deviation increase in methylation was 1.25 (95% CI 1.14, 1.37). The other eight CpGs were hypomethylated and the corresponding HRs (95% CI) ranged from 0.58 (0.44, 0.75) for cg04967787 (annotated to PPRC1) to 0.77 (0.67, 0.88) for cg08550308. We also assessed 7 differentially methylated CpG sites associated with liver cancer in previous studies. The adjusted HR for cg15079934 (annotated to LPS1) was 1.93 (95% CI 1.10, 3.39).

CONCLUSIONS

Blood DNA methylation may be associated with liver cancer mortality and may be altered during the development of liver cancer.

Comprehensive Molecular Profiling of NPM1-Mutated Acute Myeloid Leukemia Using RNAseq Approach

Acute myeloid leukemia (AML) is a complex hematologic malignancy with high morbidity and mortality. Nucleophosmin 1 (NPM1) mutations occur in approximately 30% of AML cases, and NPM1-mutated AML is classified as a distinct entity. NPM1-mutated AML patients without additional genetic abnormalities have a favorable prognosis. Despite this, 30-50% of them experience relapse. This study aimed to investigate the potential of total RNAseq in improving the characterization of NPM1-mutated AML patients. We explored genetic variations independently of myeloid stratification, revealing a complex molecular scenario. We showed that total RNAseq enables the uncovering of different genetic alterations and clonal subtypes, allowing for a comprehensive evaluation of the real expression of exome transcripts in leukemic clones and the identification of aberrant fusion transcripts. This characterization may enhance understanding and guide improved treatment strategies for NPM1mut AML patients, contributing to better outcomes. Our findings underscore the complexity of NPM1-mutated AML, supporting the incorporation of advanced technologies for precise risk stratification and personalized therapeutic strategies. The study provides a foundation for future investigations into the clinical implications of identified genetic variations and highlights the importance of evolving diagnostic approaches in leukemia management.

Expression of small nucleolar RNA SNORA51 and its clinical significance in hepatocellular carcinoma

Small nucleolar RNA H/ACA Box 51 (SNORA51) is involved in progression of multiple cancers. However, its role in hepatocellular carcinoma (HCC) is still unclear. The aim of the present study was to analyze the expression of SNORA51 in HCC and its clinical significance. A total of 136 patients with HCC who underwent surgery from January 1, 2016 to December 31, 2018 were included. The expression of SNORA51 in cancer tissues and adjacent tissues was compared using reverse transcription-quantitative PCR and bioinformatics methods. Methylation of the SNORA51 promoter in cancer and adjacent tissues was compared using bioinformatics. The relationship between SNORA51 expression levels and clinicopathological characteristics of patients with HCC, in addition to prognosis, was analyzed. The expression of SNORA51 in HCC was significantly higher compared with that in adjacent tissues (P<0.05). starBase demonstrated that higher expression levels of SNORA51 were associated with a significantly worse prognosis of patients with HCC compared with those who had lower expression levels of SNORA51 (P<0.05). Bioinformatics analysis using The University of Alabama at Birmingham Cancer Data Analysis Portal demonstrated that methylation of the SNORA51 promoter region in HCC was significantly decreased compared with adjacent tissues (P<0.05). A high expression of SNORA51 was significantly associated with portal vein tumor thrombus, vascular invasion and TNM stage (P<0.05). The median survival time of patients with high SNORA51 expression was significantly lower compared with those who had low SNORA51 expression (P<0.05). Both uni- and multivariate Cox regression analysis demonstrated that SNORA51 expression was an independent risk factor that significantly worsened the prognosis of patients with HCC (P<0.05). The overexpression of SNORA51 in patients with HCC was significantly associated with a poor prognosis and may be related to the reduced methylation of the SNORA51 promoter region. Therefore, SNORA51 may be a promising biomarker for prediction of the prognosis of patients with HCC and may be a therapeutic target for the treatment of HCC in future.

Epigenetics of type 2 diabetes and diabetes-related outcomes in the Strong Heart Study

BACKGROUND

The prevalence of type 2 diabetes has dramatically increased in the past years. Increasing evidence supports that blood DNA methylation, the best studied epigenetic mark, is related to diabetes risk. Few prospective studies, however, are available. We studied the association of blood DNA methylation with diabetes in the Strong Heart Study. We used limma, Iterative Sure Independence Screening and Cox regression to study the association of blood DNA methylation with fasting glucose, HOMA-IR and incident type 2 diabetes among 1312 American Indians from the Strong Heart Study. DNA methylation was measured using Illumina's MethylationEPIC beadchip. We also assessed the biological relevance of our findings using bioinformatics analyses.

RESULTS

Among the 358 differentially methylated positions (DMPs) that were cross-sectionally associated either with fasting glucose or HOMA-IR, 49 were prospectively associated with incident type 2 diabetes, although no DMPs remained significant after multiple comparisons correction. Multiple of the top DMPs were annotated to genes with relevant functions for diabetes including SREBF1, associated with obesity, type 2 diabetes and insulin sensitivity; ABCG1, involved in cholesterol and phospholipids transport; and HDAC1, of the HDAC family. (HDAC inhibitors have been proposed as an emerging treatment for diabetes and its complications.) CONCLUSIONS: Our results suggest that differences in peripheral blood DNA methylation are related to cross-sectional markers of glucose metabolism and insulin activity. While some of these DMPs were modestly associated with prospective incident type 2 diabetes, they did not survive multiple testing. Common DMPs with diabetes epigenome-wide association studies from other populations suggest a partially common epigenomic signature of glucose and insulin activity.

Epigenetic Biomarkers of Lead Exposure and Cardiovascular Disease: Prospective Evidence in the Strong Heart Study

Background Lead is a cardiotoxic metal with a variety of adverse health effects. In the absence of data on bone lead exposure, epigenetic biomarkers can serve as indicators of cumulative lead exposure and body burden. Herein, we leveraged novel epigenetic biomarkers of lead exposure to investigate their association with cardiovascular disease (CVD) incidence and mortality. Methods and Results Blood DNA methylation was measured using the Illumina MethylationEPIC BeadChip among 2231 participants of the Strong Heart Study (SHS) at baseline (1989-1991). Epigenetic biomarkers of lead levels in blood, patella, and tibia were estimated using previously identified cytosine-guanine dinucleotide (CpG) sites. CVD incidence and mortality data were available through 2017. Median concentrations of lead epigenetic biomarkers were 13.8 μg/g, 21.3 μg/g, and 2.9 μg/dL in tibia, patella, and blood, respectively. In adjusted models, the hazard ratio (HR) (95% CI) of CVD mortality per doubling increase in lead epigenetic biomarkers were 1.42 (1.07-1.87) for tibia lead, 1.22 (0.93-1.60) for patella lead, and 1.57 (1.16-2.11) for blood lead. The corresponding HRs for incident CVD were 0.99 (0.83-1.19), 1.07 (0.89-1.29), and 1.06 (0.87-1.30). The association between the tibia lead epigenetic biomarker and CVD mortality was modified by sex (interaction P value: 0.014), with men at increased risk (HR, 1.42 [95% CI, 1.17-1.72]) compared with women (HR, 1.04 [95% CI, 0.89-1.22]). Conclusions Tibia and blood epigenetic biomarkers were associated with increased risk of CVD mortality, potentially reflecting the cardiovascular impact of cumulative and recent lead exposures. These findings support that epigenetic biomarkers of lead exposure may capture some of the disease risk associated with lead exposure.

Multi-Omics Integration in a Twin Cohort and Predictive Modeling of Blood Pressure Values

Abnormal blood pressure is strongly associated with risk of high-prevalence diseases, making the study of blood pressure a major public health challenge. Although biological mechanisms underlying hypertension at the single omic level have been discovered, multi-omics integrative analyses using continuous variations in blood pressure values remain limited. We used a multi-omics regression-based method, called sparse multi-block partial least square, for integrative, explanatory, and predictive interests in study of systolic and diastolic blood pressure values. Various datasets were obtained from the Finnish Twin Cohort for up to 444 twins. Blocks of omics-including transcriptomic, methylation, metabolomic-data as well as polygenic risk scores and clinical data were integrated into the modeling and supported by cross-validation. The predictive contribution of each omics block when predicting blood pressure values was investigated using external participants from the Young Finns Study. In addition to revealing interesting inter-omics associations, we found that each block of omics heterogeneously improved the predictions of blood pressure values once the multi-omics data were integrated. The modeling revealed a plurality of clinical, transcriptomic, and metabolomic factors consistent with the literature and that play a leading role in explaining unit variations in blood pressure. These findings demonstrate (1) the robustness of our integrative method to harness results obtained by single omics discriminant analyses, and (2) the added value of predictive and exploratory gains of a multi-omics approach in studies of complex phenotypes such as blood pressure.

Crosstalk Between Inflammatory Signaling and Methylation in Cancer

Inflammation is an intricate immune response against infection and tissue damage. While the initial immune response is important for preventing tumorigenesis, chronic inflammation is implicated in cancer pathogenesis. It has been linked to various stages of tumor development including transformation, proliferation, angiogenesis, and metastasis. Immune cells, through the production of inflammatory mediators such as cytokines, chemokines, transforming growth factors, and adhesion molecules contribute to the survival, growth, and progression of the tumor in its microenvironment. The aberrant expression and secretion of pro-inflammatory and growth factors by the tumor cells result in the recruitment of immune cells, thus creating a mutual crosstalk. The reciprocal signaling between the tumor cells and the immune cells creates and maintains a successful tumor niche. Many inflammatory factors are regulated by epigenetic mechanisms including DNA methylation and histone modifications. In particular, DNA and histone methylation are crucial forms of transcriptional regulation and aberrant methylation has been associated with deregulated gene expression in oncogenesis. Such deregulations have been reported in both solid tumors and hematological malignancies. With technological advancements to study genome-wide epigenetic landscapes, it is now possible to identify molecular mechanisms underlying altered inflammatory profiles in cancer. In this review, we discuss the role of DNA and histone methylation in regulation of inflammatory pathways in human cancers and review the merits and challenges of targeting inflammatory mediators as well as epigenetic regulators in cancer.

Genome-wide methylation patterns in Marfan syndrome

Background

Marfan syndrome (MFS) is a connective tissue disorder caused by mutations in the Fibrillin-1 gene (FBN1). Here, we undertook the first epigenome-wide association study (EWAS) in patients with MFS aiming at identifying DNA methylation loci associated with MFS phenotypes that may shed light on the disease process.

Methods

The Illumina 450 k DNA-methylation array was used on stored peripheral whole-blood samples of 190 patients with MFS originally included in the COMPARE trial. An unbiased genome-wide approach was used, and methylation of CpG-sites across the entire genome was evaluated. Additionally, we investigated CpG-sites across the FBN1-locus (15q21.1) more closely, since this is the gene defective in MFS. Differentially Methylated Positions (DMPs) and Differentially Methylated Regions (DMRs) were identified through regression analysis. Associations between methylation levels and aortic diameters and presence or absence of 21 clinical features of MFS at baseline were analyzed. Moreover, associations between aortic diameter change, and the occurrence of clinical events (death any cause, type-A or -B dissection/rupture, or aortic surgery) and methylation levels were analyzed.

Results

We identified 28 DMPs that are significantly associated with aortic diameters in patients with MFS. Seven of these DMPs (25%) could be allocated to a gene that was previously associated with cardiovascular diseases (HDAC4, IGF2BP3, CASZ1, SDK1, PCDHGA1, DIO3, PTPRN2). Moreover, we identified seven DMPs that were significantly associated with aortic diameter change and five DMP’s that associated with clinical events. No significant associations at p < 10^–8 or p < 10^–6 were found with any of the non-cardiovascular phenotypic MFS features. Investigating DMRs, clusters were seen mostly on X- and Y, and chromosome 18–22. The remaining DMRs indicated involvement of a large family of protocadherins on chromosome 5, which were not reported in MFS before.

Conclusion

This EWAS in patients with MFS has identified a number of methylation loci significantly associated with aortic diameters, aortic dilatation rate and aortic events. Our findings add to the slowly growing literature on the regulation of gene expression in MFS patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01204-4.

Epigenome-wide association studies: current knowledge, strategies and recommendations

The aetiology and pathophysiology of complex diseases are driven by the interaction between genetic and environmental factors. The variability in risk and outcomes in these diseases are incompletely explained by genetics or environmental risk factors individually. Therefore, researchers are now exploring the epigenome, a biological interface at which genetics and the environment can interact. There is a growing body of evidence supporting the role of epigenetic mechanisms in complex disease pathophysiology. Epigenome-wide association studies (EWASes) investigate the association between a phenotype and epigenetic variants, most commonly DNA methylation. The decreasing cost of measuring epigenome-wide methylation and the increasing accessibility of bioinformatic pipelines have contributed to the rise in EWASes published in recent years. Here, we review the current literature on these EWASes and provide further recommendations and strategies for successfully conducting them. We have constrained our review to studies using methylation data as this is the most studied epigenetic mechanism; microarray-based data as whole-genome bisulphite sequencing remains prohibitively expensive for most laboratories; and blood-based studies due to the non-invasiveness of peripheral blood collection and availability of archived DNA, as well as the accessibility of publicly available blood-cell-based methylation data. Further, we address multiple novel areas of EWAS analysis that have not been covered in previous reviews: (1) longitudinal study designs, (2) the chip analysis methylation pipeline (ChAMP), (3) differentially methylated region (DMR) identification paradigms, (4) methylation quantitative trait loci (methQTL) analysis, (5) methylation age analysis and (6) identifying cell-specific differential methylation from mixed cell data using statistical deconvolution.