Competition between DNA Methylation, Nucleotide Synthesis, and Antioxidation in Cancer versus Normal Tissues

Metabolic heterogeneity in tumor microenvironment - A novel landmark for immunotherapy

The surrounding non-cancer cells and tumor cells that make up the tumor microenvironment (TME) have various metabolic rhythms. TME metabolic heterogeneity is influenced by the intricate network of metabolic control within and between cells. DNA, protein, transport, and microbial levels are important regulators of TME metabolic homeostasis. The effectiveness of immunotherapy is also closely correlated with alterations in TME metabolism. The response of a tumor patient to immunotherapy is influenced by a variety of variables, including intracellular metabolic reprogramming, metabolic interaction between cells, ecological changes within and between tumors, and general dietary preferences. Although immunotherapy and targeted therapy have made great strides, their use in the accurate identification and treatment of tumors still has several limitations. The function of TME metabolic heterogeneity in tumor immunotherapy is summarized in this article. It focuses on how metabolic heterogeneity develops and is regulated as a tumor progresses, the precise molecular mechanisms and potential clinical significance of imbalances in intracellular metabolic homeostasis and intercellular metabolic coupling and interaction, as well as the benefits and drawbacks of targeted metabolism used in conjunction with immunotherapy. This offers insightful knowledge and important implications for individualized tumor patient diagnosis and treatment plans in the future.

Brain metastases: It takes two factors for a primary cancer to metastasize to brain

Brain metastasis of a cancer is a malignant disease with high mortality, but the cause and the molecular mechanism remain largely unknown. Using the samples of primary tumors of 22 cancer types in the TCGA database, we have performed a computational study of their transcriptomic data to investigate the drivers of brain metastases at the basic physics and chemistry level. Our main discoveries are: (i) the physical characteristics, namely electric charge, molecular weight, and the hydrophobicity of the extracellular structures of the expressed transmembrane proteins largely affect a primary cancer cell’s ability to cross the blood-brain barrier; and (ii) brain metastasis may require specific functions provided by the activated enzymes in the metastasizing primary cancer cells for survival in the brain micro-environment. Both predictions are supported by published experimental studies. Based on these findings, we have built a classifier to predict if a given primary cancer may have brain metastasis, achieving the accuracy level at AUC = 0.92 on large test sets.

A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data

The metabolic heterogeneity and metabolic interplay between cells are known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single-cell metabolomics technology, we are yet to establish systematic understanding of the intra-tissue metabolic heterogeneity and cooperative mechanisms. To mitigate this knowledge gap, we developed a novel computational method, namely, single-cell flux estimation analysis (scFEA), to infer the cell-wise fluxome from single-cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a systematically reconstructed human metabolic map as a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network-based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multilayer neural networks to capitulate the nonlinear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq data set with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this data set showed the consistency between predicted flux and the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics data sets and identified context- and cell group-specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analyses including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

LINE-1 methylation mediates the inverse association between body mass index and breast cancer risk: A pilot study in the Lebanese population

INTRODUCTION

Lebanon is among the top countries worldwide in combined incidence and mortality of breast cancer, which raises concern about risk factors peculiar to this country. The underlying molecular mechanisms of breast cancer require elucidation, particularly epigenetics, which is recognized as a molecular sensor to environmental exposures.

PURPOSE

We aim to explore whether DNA methylation levels of AHRR (marker of cigarette smoking), SLC1A5 and TXLNA (markers of alcohol consumption), and LINE-1 (a genome-wide repetitive retrotransposon) can act as molecular mediators underlying putative associations between breast cancer risk and pertinent extrinsic (tobacco smoking and alcohol consumption) and intrinsic factors [age and body mass index (BMI)].

METHODS

This is a cross-sectional pilot study which includes breast cancer cases (N = 65) and controls (N = 54). DNA methylation levels were measured using bisulfite pyrosequencing on available peripheral blood samples (N = 119), and Multivariate Imputation by Chained Equations (MICE) was used to impute missing DNA methylation values in remaining samples. Multiple mediation analysis was performed to assess direct and indirect (via DNA methylation) effects of intrinsic and extrinsic factors on breast cancer risk.

RESULTS

In relation to exposure, AHRR hypo-methylation was associated with cigarette but not waterpipe smoking, suggesting potentially different biomarkers of these two forms of tobacco use; SLC1A5 and TXLNA methylation were not associated with alcohol consumption; LINE-1 methylation was inversely associated with BMI (β-value [95% confidence interval (CI)] = -0.04 [-0.07, -0.02]), which remained significant after adjustment for age, smoking and alcohol consumption. In relation to breast cancer, there was no detectable association between AHRR, SLC1A5 or TXLNA methylation and cancer risk, but LINE-1 methylation was significantly higher in breast cancer cases when compared to controls (mean ± SD: 72.00 ± 0.66 versus 70.89 ± 0.73, P = 4.67 × 10-14). This difference remained significant after adjustment for confounders (odds ratio (OR) [95% CI] = 9.75[3.74, 25.39]). Moreover, LINE-1 hypo-methylation mediated 83% of the inverse effect of BMI on breast cancer risk.

CONCLUSION

This pilot study demonstrates that alterations in blood LINE-1 methylation mediate the inverse effect of BMI on breast cancer risk. This warrants large scale studies and stratification based on clinic-pathological types of breast cancer.

Highly recurrent CBS epimutations in gastric cancer CpG island methylator phenotypes and inflammation

Background

CIMP (CpG island methylator phenotype) is an epigenetic molecular subtype, observed in multiple malignancies and associated with the epigenetic silencing of tumor suppressors. Currently, for most cancers including gastric cancer (GC), mechanisms underlying CIMP remain poorly understood. We sought to discover molecular contributors to CIMP in GC, by performing global DNA methylation, gene expression, and proteomics profiling across 14 gastric cell lines, followed by similar integrative analysis in 50 GC cell lines and 467 primary GCs.

Results

We identify the cystathionine beta-synthase enzyme (CBS) as a highly recurrent target of epigenetic silencing in CIMP GC. Likewise, we show that CBS epimutations are significantly associated with CIMP in various other cancers, occurring even in premalignant gastroesophageal conditions and longitudinally linked to clinical persistence. Of note, CRISPR deletion of CBS in normal gastric epithelial cells induces widespread DNA methylation changes that overlap with primary GC CIMP patterns. Reflecting its metabolic role as a gatekeeper interlinking the methionine and homocysteine cycles, CBS loss in vitro also causes reductions in the anti-inflammatory gasotransmitter hydrogen sulfide (H₂S), with concomitant increase in NF-κB activity. In a murine genetic model of CBS deficiency, preliminary data indicate upregulated immune-mediated transcriptional signatures in the stomach.

Conclusions

Our results implicate CBS as a bi-faceted modifier of aberrant DNA methylation and inflammation in GC and highlights H₂S donors as a potential new therapy for CBS-silenced lesions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-021-02375-2.

Inhibition of DNMT1 and ERRα crosstalk suppresses breast cancer via derepression of IRF4

DNA methylation is implicated in the acquisition of malignant phenotypes, and the use of epigenetic modulating drugs is a promising anti-cancer therapeutic strategy. 5-aza-2'deoxycytidine (decitabine, 5-azadC) is an FDA-approved DNA methyltransferase (DNMT) inhibitor with proven effectiveness against hematological malignancies and more recently triple-negative breast cancer (BC). Herein, genetic or pharmacological studies uncovered a hitherto unknown feedforward molecular link between DNMT1 and the estrogen related receptor α (ERRα), a key transcriptional regulator of cellular metabolism. Mechanistically, DNMT1 promotes ERRα stability which in turn couples DNMT1 transcription with that of the methionine cycle and S-adenosylmethionine synthesis to drive DNA methylation. In vitro and in vivo investigation using a pre-clinical mouse model of BC demonstrated a clear therapeutic advantage for combined administration of the ERRα inhibitor C29 with 5-azadC. A large-scale bisulfite genomic sequencing analysis revealed specific methylation perturbations fostering the discovery that reversal of promoter hypermethylation and consequently derepression of the tumor suppressor gene, IRF4, is a factor underlying the observed BC suppressive effects. This work thus uncovers a critical role of ERRα in the crosstalk between transcriptional control of metabolism and epigenetics and illustrates the potential for targeting ERRα in combination with DNMT inhibitors for BC treatment and other epigenetics-driven malignancies.

Folate deficiency promotes differentiation of vascular smooth muscle cells without affecting the methylation status of regulated genes

Elevated serum homocysteine, an intermediate of cellular one-carbon metabolism, is an independent risk factor for cardiovascular disease (CVD). Folate deficiency increases serum homocysteine and may contribute to CVD progression. Vascular smooth muscle cells (VSMCs) regulate vascular contractility, but also contribute to repair processes in response to vascular injury. Nutritional deficiencies, like folate deficiency, are thought to impact on this phenotypic plasticity, possibly by epigenetic mechanisms. We have investigated the effect of folate deficiency on VSMCs in two cell culture systems representing early and late stages of smooth muscle cells differentiation. We find that folate deficiency promotes differentiation towards a more contractile phenotype as indicated by increased expression of respective marker genes. However, microarray analysis identified markers of striated muscle as the predominant gene expression change elicited by folate deficiency. These changes are not merely a reflection of cell cycle arrest, as foetal calf serum restriction or iron deficiency do not replicate the gene expression changes observed in response to folate deficiency. Folate deficiency only has a marginal effect on global DNA methylation. DNA methylation of CpG islands associated with genes regulated by folate deficiency remains unaffected. This supports our earlier findings in a mouse model system which also did not show any changes in global DNA methylation in response to folate and vitamin B6/B12 deficiency. These data suggest that folate deficiency enhances the expression of smooth muscle marker gene expression, promotes a shift towards a skeletal muscle phenotype, and does not regulate gene expression via DNA methylation.

The Health Burden of Cancer Attributable to Obesity in Korea: A Population-Based Cohort Study

PURPOSE

Considering the health impact of obesity and cancer, it is important to estimate the burden of cancer attributable to high body mass index (BMI). Therefore, the present study attempts to measure the health burden of cancer attributable to excess BMI, according to cancer sites.

MATERIALS AND METHODS

The present study used nationwide medical check-up sample cohort data (2002-2015). The study subjects were 496,390 individuals (268,944 men and 227,446 women). We first calculated hazard ratio (HR) in order to evaluate the effect of excess BMI on cancer incidence and mortality. Then, the adjusted HR values and the prevalence of excess BMI were used to calculate the population attributable risk. This study also used the Global Burden of Disease method, to examine the health burden of obesity-related cancers attributable to obesity.

RESULTS

The highest disability-adjusted life year (DALY) values attributable to overweight and obesity in men were shown in liver cancer, colorectal cancer, and gallbladder cancer. Among women, colorectal, ovarian, and breast (postmenopausal) cancers had the highest DALYs values attributable to overweight and obesity. Approximately 8.0% and 12.5% of cancer health burden (as measured by DALY values) among obesity-related cancers in men and women, respectively, can be prevented.

CONCLUSIONS

Obesity has added to the health burden of cancer. By measuring the proportion of cancer burden attributable to excess BMI, the current findings provide support for the importance of properly allocating healthcare resources and for developing cancer prevention strategies to reduce the future burden of cancer.

Diagnosis of Breast Hyperplasia and Evaluation of RuXian-I Based on Metabolomics Deep Belief Networks

Breast cancer is estimated to be the leading cancer type among new cases in American women. Core biopsy data have shown a close association between breast hyperplasia and breast cancer. The early diagnosis and treatment of breast hyperplasia are extremely important to prevent breast cancer. The Mongolian medicine RuXian-I is a traditional drug that has achieved a high level of efficacy and a low incidence of side effects in its clinical use. However, for detecting the efficacy of RuXian-I, a rapid and accurate evaluation method based on metabolomic data is still lacking. Therefore, we proposed a framework, named the metabolomics deep belief network (MDBN), to analyze breast hyperplasia metabolomic data. We obtained 168 samples of metabolomic data from an animal model experiment of RuXian-I, which were averaged from control groups, treatment groups, and model groups. In the process of training, unlabelled data were used to pretrain the Deep Belief Networks models, and then labelled data were used to complete fine-tuning based on a limited-memory Broyden Fletcher Goldfarb Shanno (L-BFGS) algorithm. To prevent overfitting, a dropout method was added to the pretraining and fine-tuning procedures. The experimental results showed that the proposed model is superior to other classical classification methods that are based on positive and negative spectra data. Further, the proposed model can be used as an extension of the classification method for metabolomic data. For the high accuracy of classification of the three groups, the model indicates obvious differences and boundaries between the three groups. It can be inferred that the animal model of RuXian-I is well established, which can lay a foundation for subsequent related experiments. This also shows that metabolomic data can be used as a means to verify the effectiveness of RuXian-I in the treatment of breast hyperplasia.