Crosstalk between microRNA expression and DNA methylation drives the hormone-dependent phenotype of breast cancer

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Application of the Key Characteristics of Carcinogens to Bisphenol A

The ten key characteristics (KCs) of carcinogens are based on characteristics of known human carcinogens and encompass many types of endpoints. We propose that an objective review of the large amount of cancer mechanistic evidence for the chemical bisphenol A (BPA) can be achieved through use of these KCs. A search on metabolic and mechanistic data relevant to the carcinogenicity of BPA was conducted and web-based software tools were used to screen and organize the results. We applied the KCs to systematically identify, organize, and summarize mechanistic information for BPA, and to bring relevant carcinogenic mechanisms into focus. For some KCs with very large data sets, we utilized reviews focused on specific endpoints. Over 3000 studies for BPA from various data streams (exposed humans, animals, in vitro and cell-free systems) were identified. Mechanistic data relevant to each of the ten KCs were identified, with receptor-mediated effects, epigenetic alterations, oxidative stress, and cell proliferation being especially data rich. Reactive and bioactive metabolites are also associated with a number of KCs. This review demonstrates how the KCs can be applied to evaluate mechanistic data, especially for data-rich chemicals. While individual entities may have different approaches for the incorporation of mechanistic data in cancer hazard identification, the KCs provide a practical framework for conducting an objective examination of the available mechanistic data without a priori assumptions on mode of action. This analysis of the mechanistic data available for BPA suggests multiple and inter-connected mechanisms through which this chemical can act.

Targeting methionine metabolism in cancer: opportunities and challenges

Reprogramming of methionine metabolism is a conserved hallmark of tumorigenesis. Recent studies have revealed mechanisms regulating methionine metabolism within the tumor microenvironment (TME) that drive both cancer development and antitumor immunity evasion. In this review article we summarize advancements in our understanding of tumor regulation of methionine metabolism and therapies in development that target tumor methionine metabolism. We also delineate the challenges of methionine blockade therapies in cancer and discuss emerging strategies to address them.

A micro-nano interface integrated SERS-based microfluidic sensor for miRNA detection using DNAzyme walker amplification

BACKGROUND

Precise and specific miRNA detection plays a vital role in exploring development mechanisms of cancer disease, thereby it can significantly improve relevant prevention and treatment strategies.

RESULTS

In this work, a surface-enhanced Raman spectroscopy (SERS)-based microfluidic chip has been devised with a microcone array SERS substrate (MCASS) for the miR-141 detection. This substrate excels in unique SERS activity and large surface area for DNA oligonucleotide modification. As the presence of miR-141, the DNAzyme walker induced cleavage reaction took place on the finely designed and prepared dual DNA conjugated SERS nanoprobes. The SERS nanoprobes can anchor on MCASS by the DNA hybridization that achieved an impressive detection limit in the femtomolar level.

SIGNIFICANCE

With this integrated SERS-based microfluidic chip, we provided a miRNA detection strategy using DNAzyme walker amplification technology. It is believed that this strategy could be a powerful tool for miRNA detection and related cancer screening test.

Associations Between Epigenetic Age Acceleration and microRNA Expression Among U.S. Firefighters

Epigenetic changes may be biomarkers of health. Epigenetic age acceleration (EAA), the discrepancy between epigenetic age measured via epigenetic clocks and chronological age, is associated with morbidity and mortality. However, the intersection of epigenetic clocks with microRNAs (miRNAs) and corresponding miRNA-based health implications have not been evaluated. We analyzed DNA methylation and miRNA profiles from blood sampled among 332 individuals enrolled across 2 U.S.-based firefighter occupational studies (2015-2018 and 2018-2020). We considered 7 measures of EAA in leukocytes (PhenoAge, GrimAge, Horvath, skin-blood, and Hannum epigenetic clocks, and extrinsic and intrinsic epigenetic age acceleration). We identified miRNAs associated with EAA using individual linear regression models, adjusted for sex, race/ethnicity, chronological age, and cell type estimates, and investigated downstream effects of associated miRNAs with miRNA enrichment analyses and genomic annotations. On average, participants were 38 years old, 88% male, and 75% non-Hispanic white. We identified 183 of 798 miRNAs associated with EAA (FDR q < 0.05); 126 with PhenoAge, 59 with GrimAge, 1 with Horvath, and 1 with the skin-blood clock. Among miRNAs associated with Horvath and GrimAge, there were 61 significantly enriched disease annotations including age-related metabolic and cardiovascular conditions and several cancers. Enriched pathways included those related to proteins and protein modification. We identified miRNAs associated with EAA of multiple epigenetic clocks. PhenoAge had more associations with individual miRNAs, but GrimAge and Horvath had greater implications for miRNA-associated pathways. Understanding the relationship between these epigenetic markers could contribute to our understanding of the molecular underpinnings of aging and aging-related diseases.

AP003352.1/miR-141-3p axis enhances the proliferation of osteosarcoma by LPAR3

Osteosarcoma (OS) is a highly malignant tumor with a poor prognosis and a growing incidence. LncRNAs and microRNAs control the occurrence and development process of osteosarcoma through ceRNA patterns. The LPAR3 gene is important in cancer cell proliferation, apoptosis and disease development. However, the regulatory mechanism of the ceRNA network through which LPAR3 participates in osteosarcoma has not been clarified. Herein, our study demonstrated that the AP003352.1/miR-141-3p axis drives LPAR3 expression to induce the malignant progression of osteosarcoma. First, the expression of LPAR3 is regulated by the changes in AP003352.1 and miR-141-3p. Similar to the ceRNA of miR-141-3p, AP003352.1 regulates the expression of LPAR3 through this mechanism. In addition, the regulation of AP003352.1 in malignant osteosarcoma progression depends to a certain degree on miR-141-3p. Importantly, the AP003352.1/miR-141-3p/LPAR3 axis can better serve as a multi-gene diagnostic marker for osteosarcoma. In conclusion, our research reveals a new ceRNA regulatory network, which provides a novel potential target for the diagnosis and treatment of osteosarcoma.

Epigenetic effects of short-chain fatty acids from the large intestine on host cells

Adult humans harbor at least as many microbial cells as eukaryotic ones. The largest compartment of this diverse microbial population, the gut microbiota, encompasses the collection of bacteria, archaea, viruses, and eukaryotic organisms that populate the gastrointestinal tract, and represents a complex and dynamic ecosystem that has been increasingly implicated in health and disease. The gut microbiota carries ∼100-to-150-times more genes than the human genome and is intimately involved in development, homeostasis, and disease. Of the several microbial metabolites that have been studied, short-chain fatty acids emerge as a group of molecules that shape gene expression in several types of eukaryotic cells by multiple mechanisms, which include DNA methylation changes, histone post-translational modifications, and microRNA-mediated gene silencing. Butyric acid, one of the most extensively studied short-chain fatty acids, reaches higher concentrations in the colonic lumen, where it provides a source of energy for healthy colonocytes, and its concentrations decrease towards the bottom of the colonic crypts, where stem cells reside. The lower butyric acid concentration in the colonic crypts allows undifferentiated cells, such as stem cells, to progress through the cell cycle, pointing towards the importance of the crypts in providing them with a protective niche. In cancerous colonocytes, which metabolize relatively little butyric acid and mostly rely on glycolysis, butyric acid preferentially acts as a histone deacetylase inhibitor, leading to decreased cell proliferation and increased apoptosis. A better understanding of the interface between the gut microbiota metabolites and epigenetic changes in eukaryotic cells promises to unravel in more detail processes that occur physiologically and as part of disease, help develop novel biomarkers, and identify new therapeutic modalities.

The Mechanism of DNA Methylation and miRNA in Breast Cancer

Breast cancer is the most prevalent cancer in the world. Currently, the main treatments for breast cancer are radiotherapy, chemotherapy, targeted therapy and surgery. The treatment measures for breast cancer depend on the molecular subtype. Thus, the exploration of the underlying molecular mechanisms and therapeutic targets for breast cancer remains a hotspot in research. In breast cancer, a high level of expression of DNMTs is highly correlated with poor prognosis, that is, the abnormal methylation of tumor suppressor genes usually promotes tumorigenesis and progression. MiRNAs, as non-coding RNAs, have been identified to play key roles in breast cancer. The aberrant methylation of miRNAs could lead to drug resistance during the aforementioned treatment. Therefore, the regulation of miRNA methylation might serve as a therapeutic target in breast cancer. In this paper, we reviewed studies on the regulatory mechanisms of miRNA and DNA methylation in breast cancer from the last decade, focusing on the promoter region of tumor suppressor miRNAs methylated by DNMTs and the highly expressed oncogenic miRNAs inhibited by DNMTs or activating TETs.

Crosstalk between miRNAs and DNA Methylation in Cancer

miRNAs are some of the most well-characterized regulators of gene expression. Integral to several physiological processes, their aberrant expression often drives the pathogenesis of both benign and malignant diseases. Similarly, DNA methylation represents an epigenetic modification influencing transcription and playing a critical role in silencing numerous genes. The silencing of tumor suppressor genes through DNA methylation has been reported in many types of cancer and is associated with tumor development and progression. A growing body of literature has described the crosstalk between DNA methylation and miRNAs as an additional layer in the regulation of gene expression. Methylation in miRNA promoter regions inhibits its transcription, while miRNAs can target transcripts and subsequently regulate the proteins responsible for DNA methylation. Such relationships between miRNA and DNA methylation serve an important regulatory role in several tumor types and highlight a novel avenue for potential therapeutic targets. In this review, we discuss the crosstalk between DNA methylation and miRNA expression in the pathogenesis of cancer and describe how miRNAs influence DNA methylation and, conversely, how methylation impacts the expression of miRNAs. Finally, we address how these epigenetic modifications may be leveraged as biomarkers in cancer.

Large-scale integration of the non-coding RNAs with DNA methylation in human cancers

Characterizing influences of DNA methylation (DNAm) on non-coding RNAs (ncRNAs) is important to understand the mechanisms of gene regulation and cancer outcome. In our study, we describe the results of ncRNA quantitative trait methylation sites (ncQTM) analyses on 8,545 samples from The Cancer Genome Atlas (TCGA), 763 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and 516 samples from Genotype-Tissue Expression (GTEx) to identify the significant associations between DNAm sites and ncRNAs (miRNA, long non-coding RNA [lncRNA], small nuclear RNA [snRNA], small nucleolar RNA [snoRNA], and rRNA) across 32 cancer types. With more than 22 billion tests, we identify 302,764 cis-ncQTMs (6.28% of all tested) and 79,841,728 trans-ncQTMs (1.15% of all tested). Most DNAm sites (70.6% on average) are in trans association, while only 25.2% DNAm sites are in cis association. Further, we develop a subtype named ncm_cluster based on cancer-specific ncRNAs thatis associated with tumor microenvironment, clinical outcome, and biological pathways. To comprehensively describe the ncQTM patterns, we developed a database named Pancan-ncQTM (http://bigdata.njmu.edu.cn/Pancan-ncQTM/).

Aberrant promoter hypermethylation of miR-335 and miR-145 is involved in breast cancer PD-L1 overexpression

PD-L1 is one of the most important immune checkpoint molecules in breast cancer that plays an important role in suppressing the immune system when confronted with tumor cells and is regulated by various microRNAs. Among them, microRNA-335-3p and microRNA-145-5p, regulated by DNA methylation, have tumor suppressor activities. We studied the role of miR-335 and -145 on PD-L1 suppression in breast cancer. The expression of miR-355 and miR-145 was significantly downregulated in BC tissues and cell lines compared to their controls, and their downregulation was negatively correlated with PD-L1 overexpression. In-silico and luciferase reporter systems confirmed that miR-335 and -145 target PD-L1. In BC tissues and cell lines, cancer-specific methylation was found in CpG-rich areas upstream of miR-335 and-145, and up-regulation of PD-L1 expression was connected with hypermethylation (r = 0.4089, P = 0.0147, and r = 0.3373, P = 0.0475, respectively). The higher levels of miR-355 and -145 in BC cells induced apoptosis, arrested the cell cycle, and reduced proliferation significantly. In summary, we found that miR-335 and -145 are novel tumor suppressors inactivated in BC, and these miRs may serve as potential therapeutic targets for breast cancer treatment.

Endothelial-to-mesenchymal transition: An underappreciated mediator of diabetic complications

Diabetes and its complications represent a great burden on the global healthcare system. Diabetic complications are fundamentally diseases of the vasculature, with endothelial cells being the centerpiece of early hyperglycemia-induced changes. Endothelial-to-mesenchymal transition is a tightly regulated process that results in endothelial cells losing endothelial characteristics and developing mesenchymal traits. Although endothelial-to-mesenchymal transition has been found to occur within most of the major complications of diabetes, it has not been a major focus of study or a common target in the treatment or prevention of diabetic complications. In this review we summarize the importance of endothelial-to-mesenchymal transition in each major diabetic complication, examine specific mechanisms at play, and highlight potential mechanisms to prevent endothelial-to-mesenchymal transition in each of the major chronic complications of diabetes.

Crosstalk between Methylation and ncRNAs in Breast Cancer: Therapeutic and Diagnostic Implications

Breast cancer, as a highly heterogeneous malignant tumor, is one of the primary causes of death among females worldwide. The etiology of breast cancer involves aberrant epigenetic mechanisms and abnormal expression of certain non-coding RNA (ncRNAs). DNA methylation, N6-methyladenosine(m6A), and histone methylation are widely explored epigenetic regulation types in breast cancer. ncRNAs are a group of unique RNA transcripts, mainly including microRNA (miRNAs), long non-coding RNA (lncRNAs), circular RNA (circRNAs), small interfering RNA (siRNAs), piwi-interacting RNA (piRNAs), etc. Different types of methylation and ncRNAs mutually regulate and interact to form intricate networks to mediate precisely breast cancer genesis. In this review, we elaborate on the crosstalk between major methylation modifications and ncRNAs and discuss the role of their interaction in promoting breast cancer oncogenesis. This review can provide novel insights into establishing a new diagnostic marker system on methylation patterns of ncRNAs and therapeutic perspectives of combining ncRNA oligonucleotides and phytochemical drugs for breast cancer therapy.

Gene body methylation in cancer: molecular mechanisms and clinical applications

DNA methylation is an important epigenetic mechanism that regulates gene expression. To date, most DNA methylation studies have focussed on CpG islands in the gene promoter region, and the mechanism of methylation and the regulation of gene expression after methylation have been clearly elucidated. However, genome-wide methylation studies have shown that DNA methylation is widespread not only in promoters but also in gene bodies. Gene body methylation is widely involved in the expression regulation of many genes and is closely related to the occurrence and progression of malignant tumours. This review focusses on the formation of gene body methylation patterns, its regulation of transcription, and its relationship with tumours, providing clues to explore the mechanism of gene body methylation in regulating gene transcription and its significance and application in the field of oncology.

A vicious circle in breast cancer: The interplay between inflammation, reactive oxygen species, and microRNAs

Breast cancer (BC) is the most common cancer in women worldwide. This highly heterogeneous disease is molecularly stratified into luminal A, luminal B, HER2, triple-negative/basal-like, and normal-like subtypes. An important aspect in BC progression is the activation of inflammatory processes. The activation of CD8+/Th1, NK, and M1 tumor associated macrophages (TAMs), leads to tumor destruction. In contrast, an anti-inflammatory response mediated by CD4+/Th2 and M2 TAMs will favor tumor progression. Inflammation also stimulates the production of inflammatory mediators like reactive oxygen species (ROS). In chronic inflammation, ROS activates oxidative stress and endothelial dysfunction. In cancer, ROS plays a dual role with anti-tumorigenic and pro-tumorigenic effects in cell signaling pathways that control proliferation, survival, apoptosis, and inflammation. MicroRNAs (miRNAs), which are known to be involved in BC progression and inflammation, can be regulated by ROS. At the same time, miRNAs regulate the expression of genes modulating oxidative stress. In this review, we will discuss the interplay between inflammation, ROS, and miRNAs as anticancer and tumor promoter molecules in BC. A clear understanding of the role of miRNAs in the regulation of ROS production and inflammation, may lead to new opportunities for therapy in BC.

Elevated miR-29c-5p Expression in Nipple Aspirate Fluid Is Associated with Extremely High Mammographic Breast Density

Simple Summary

High mammographic density is a known risk factor for breast cancer. However, the underlying mechanisms of high mammographic density development and breast cancer are unknown. MicroRNAs are potential biomarkers indicative of carcinogenesis and can be assessed in nipple aspirate fluid. We used nipple aspirate fluid from women with very low and extremely high mammographic density to examine differences in expression of multiple miRNAs between both extremes in the spectrum of mammographic density. We found that hsa-miR-29c-5p was upregulated in an extremely high mammographic density context and potential targets were identified that might provide clues of the relationship between high mammographic density and breast cancer risk. Understanding the relationship between high mammographic density and breast cancer is of great value for early breast cancer diagnosis and treatment. With our research we provide new insight into this relationship and further research could determine the effects of dysregulated hsa-miR-29c-5p on the identified candidate targets.

Abstract

High mammographic density (MD) is associated with an increased risk of breast cancer, however the underlying mechanisms are largely unknown. This research aimed to identify microRNAs (miRNAs) that play a role in the development of extremely dense breast tissue. In the discovery phase, 754 human mature miRNAs were profiled in 21 extremely high MD- and 20 very low MD-derived nipple aspirate fluid (NAF) samples from healthy women. In the validation phase, candidate miRNAs were assessed in a cohort of 89 extremely high MD and 81 very low MD NAF samples from healthy women. Independent predictors of either extremely high MD or miRNA expression were identified by logistic regression and linear regression analysis, respectively. mRNA targets and pathways were identified through miRTarBase, TargetScan, and PANTHER pathway analysis. Statistical analysis identified four differentially expressed miRNAs during the discovery phase. During the validation, linear regression (p = 0.029; fold change = 2.10) and logistic regression (p = 0.048; odds ratio = 1.38) showed that hsa-miR-29c-5p was upregulated in extremely high MD-derived NAF. Identified candidate mRNA targets of hsa-miR-29c-5p are CFLAR, DNMT3A, and PTEN. Further validation and exploration of targets and downstream pathways of has-miR-29c-5p will provide better insight into the processes involved in the development of high MD and in the associated increased risk of breast cancer.

DNMT3A and DNMT3B in Breast Tumorigenesis and Potential Therapy

Breast cancer has become a leading cause of cancer-related deaths in women worldwide. DNA methylation has been revealed to play an enormously important role in the development and progression of breast cancer. DNA methylation is regulated by DNA methyltransferases (DNMTs), including DNMT1, DNMT2, and DNMT3. DNMT3 family has three members: DNMT3A, DNMT3B, and DNMT3L. The roles and functions of DNMT1 in breast cancer have been well reviewed. In this article, the roles of DNMT3A and DNMT3B in breast tumorigenesis and development are reviewed. We also discuss the SNP and mutations of DNMT3A and DNMT3B in breast cancer. In addition, we summarize how DNMT3A and DNMT3B are regulated by non-coding RNAs and signaling pathways in breast cancer, and targeting the expression levels of DNMT3A and DNMT3B may be a promising therapeutic approach for breast cancer. This review will provide reference for further studies on the biological functions and molecular mechanisms of DNMT3A and DNMT3B in breast cancer.

Implication of microRNAs in Carcinogenesis with Emphasis on Hematological Malignancies and Clinical Translation

MicroRNAs (miRNAs) are evolutionarily conserved small non-coding RNAs, that are involved in the multistep process of carcinogenesis, contributing to all established hallmarks of cancer. In this review, implications of miRNAs in hematological malignancies and their clinical utilization fields are discussed. As components of the complex regulatory network of gene expression, influenced by the tissue microenvironment and epigenetic modifiers, miRNAs are “micromanagers” of all physiological processes including the regulation of hematopoiesis and metabolic pathways. Dysregulated miRNA expression levels contribute to both the initiation and progression of acute leukemias, the metabolic reprogramming of malignantly transformed hematopoietic precursors, and to the development of chemoresistance. Since they are highly stable and can be easily quantified in body fluids and tissue specimens, miRNAs are promising biomarkers for the early detection of hematological malignancies. Besides novel opportunities for differential diagnosis, miRNAs can contribute to advanced chemoresistance prediction and prognostic stratification of acute leukemias. Synthetic oligonucleotides and delivery vehicles aim the therapeutic modulation of miRNA expression levels. However, major challenges such as efficient delivery to specific locations, differences of miRNA expression patterns between pediatric and adult hematological malignancies, and potential side effects of miRNA-based therapies should be considered.

cSurvival: a web resource for biomarker interactions in cancer outcomes and in cell lines

Survival analysis is a technique for identifying prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled &gt;11 000 adult and &gt;4000 pediatric tumor cases with clinical outcomes and multiomics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database and offers three main advances: (i) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (ii) survival analysis not only at the gene, but also the GS level; and (iii) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Altogether, these analyses demonstrate cSurvival’s ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.

Integration of Multimodal Data from Disparate Sources for Identifying Disease Subtypes

Simple Summary

The diagnostic and treatment strategies of cancer remain generally suboptimal resulting in over-diagnosis or under-treatment. Though many attempts on optimizing treatment decisions by early prediction of disease progression have been undertaken, these efforts yielded only modest success so far due to the heterogeneity of cancer with multifactorial etiology. Here, we propose a deep-learning based data integration model capable of predicting disease progression by integrating collective information available through multiple studies with different cohorts and heterogeneous data types. The results have shown that the proposed data integration pipeline is able to identify disease progression with higher accuracy and robustness compared to using a single cohort, by offering a more complete picture of the specific disease on patients with brain, blood, and pancreatic cancers.

Abstract

Studies over the past decade have generated a wealth of molecular data that can be leveraged to better understand cancer risk, progression, and outcomes. However, understanding the progression risk and differentiating long- and short-term survivors cannot be achieved by analyzing data from a single modality due to the heterogeneity of disease. Using a scientifically developed and tested deep-learning approach that leverages aggregate information collected from multiple repositories with multiple modalities (e.g., mRNA, DNA Methylation, miRNA) could lead to a more accurate and robust prediction of disease progression. Here, we propose an autoencoder based multimodal data fusion system, in which a fusion encoder flexibly integrates collective information available through multiple studies with partially coupled data. Our results on a fully controlled simulation-based study have shown that inferring the missing data through the proposed data fusion pipeline allows a predictor that is superior to other baseline predictors with missing modalities. Results have further shown that short- and long-term survivors of glioblastoma multiforme, acute myeloid leukemia, and pancreatic adenocarcinoma can be successfully differentiated with an AUC of 0.94, 0.75, and 0.96, respectively.

Exploration of the Effect on Genome-Wide DNA Methylation by miR-143 Knock-Out in Mice Liver

MiR-143 play an important role in hepatocellular carcinoma and liver fibrosis via inhibiting hepatoma cell proliferation. DNA methyltransferase 3 alpha (DNMT3a), as a target of miR-143, regulates the development of primary organic solid tumors through DNA methylation mechanisms. However, the effect of miR-143 on DNA methylation profiles in liver is unclear. In this study, we used Whole-Genome Bisulfite Sequencing (WGBS) to detect the differentially methylated regions (DMRs), and investigated DMR-related genes and their enriched pathways by miR-143. We found that methylated cytosines increased 0.19% in the miR-143 knock-out (KO) liver fed with high-fat diet (HFD), compared with the wild type (WT). Furthermore, compared with the WT group, the CG methylation patterns of the KO group showed lower CG methylation levels in CG islands (CGIs), promoters and hypermethylation in CGI shores, 5'UTRs, exons, introns, 3'UTRs, and repeat regions. A total of 984 DMRs were identified between the WT and KO groups consisting of 559 hypermethylation and 425 hypomethylation DMRs. Furthermore, DMR-related genes were enriched in metabolism pathways such as carbon metabolism (serine hydroxymethyltransferase 2 (Shmt2), acyl-Coenzyme A dehydrogenase medium chain (Acadm)), arginine and proline metabolism (spermine synthase (Sms), proline dehydrogenase (Prodh2)) and purine metabolism (phosphoribosyl pyrophosphate synthetase 2 (Prps2)). In summary, we are the first to report the change in whole-genome methylation levels by miR-143-null through WGBS in mice liver, and provide an experimental basis for clinical diagnosis and treatment in liver diseases, indicating that miR-143 may be a potential therapeutic target and biomarker for liver damage-associated diseases and hepatocellular carcinoma.

Epigenetics in Food Allergy and Immunomodulation

Food allergy (FA) is an increasing problem worldwide and, over recent years, its prevalence is rising in developed countries. Nowadays, the immunological and cellular processes that occur in the allergic reactions are not fully understood, which hampers the development of in vitro diagnostic tools and further treatment options. Moreover, allergic diseases could be reinforced by environmental exposure and genetic modifications. Gene expression can be controlled by different epigenetic mechanisms like DNA methylation, histone modifications, and microRNAs. In addition, several environmental factors such as dietary components (vitamin D, butyrate, folic acid) are able to regulate this epigenetic mechanism. All these factors produce modifications in immune genes that could alter the development and function of immune cells, and therefore the etiology of the disease. Furthermore, these epigenetic mechanisms have also an influence on immunomodulation, which could explain sustained responsiveness or unresponsiveness during immunotherapy due to epigenetic modifications in key genes that induce tolerance in several FA. Thus, in this review we focus on the different epigenetic mechanisms that occur in FA and on the influence of several dietary components in these gene modifications.

Development and performance evaluation of TaqMan real-time fluorescence quantitative methylation specific PCR for detecting methylation level of PER2

BACKGROUND

PER2 gene methylation is closely related to the occurrence and progress of some cancers, but there is no method to quantitatively detect PER2 methylation in conventional laboratories. So, we established a TaqMan real-time fluorescence quantitative methylation specific PCR (TaqMan real-time FQ-MSP) assay and use it for quantitative detection of PER2 methylation in leukemia patients.

METHODS

According to the PER2 sequence searched by GenBank, a CpG sequence enrichment region of the PER2 gene promoter was selected, and the methylated and unmethylated target sequences were designed according to the law of bisulfite conversion of DNA to construct PER2 methylation positive and negative reference materials. Specific primers and probe were designed. The reference materials were continuously diluted into gradient samples by tenfold ratio to evaluate the analytical sensitivity, specificity, accuracy and reproducibility of the method, and the analytical sensitivity of TaqMan real-time FQ-MSP assay was compared with that of the conventional MSP assay. At the same time, the new-established TaqMan real-time FQ-MSP assay and the conventional MSP assay were used to detect the PER2 methylation level of 81 patients with leukemia, and the samples with inconsistent detection results of the two assays were sent to pyromethylation sequencing to evaluate the clinical detection performance.

RESULTS

The minimum detection limit of TaqMan real-time FQ-MSP assay for detecting PER2 methylation level established in this study was 6 copies/uL, and the coefficient of variation(CV) of intra-assay and inter-assay was less than 3%. Compared with the conventional MSP assay, it has higher analytical sensitivity. For the samples with inconsistent detection results, the results of pyrosequencing and TaqMan real-time FQ-MSP assay are consistent.

CONCLUSION

TaqMan real-time FQ-MSP assay of PER2 methylation established in this study has high detection performance and can be used for the detection of clinical samples.

OmiEmbed: A Unified Multi-Task Deep Learning Framework for Multi-Omics Data

High-dimensional omics data contain intrinsic biomedical information that is crucial for personalised medicine. Nevertheless, it is challenging to capture them from the genome-wide data, due to the large number of molecular features and small number of available samples, which is also called "the curse of dimensionality" in machine learning. To tackle this problem and pave the way for machine learning-aided precision medicine, we proposed a unified multi-task deep learning framework named OmiEmbed to capture biomedical information from high-dimensional omics data with the deep embedding and downstream task modules. The deep embedding module learnt an omics embedding that mapped multiple omics data types into a latent space with lower dimensionality. Based on the new representation of multi-omics data, different downstream task modules were trained simultaneously and efficiently with the multi-task strategy to predict the comprehensive phenotype profile of each sample. OmiEmbed supports multiple tasks for omics data including dimensionality reduction, tumour type classification, multi-omics integration, demographic and clinical feature reconstruction, and survival prediction. The framework outperformed other methods on all three types of downstream tasks and achieved better performance with the multi-task strategy compared to training them individually. OmiEmbed is a powerful and unified framework that can be widely adapted to various applications of high-dimensional omics data and has great potential to facilitate more accurate and personalised clinical decision making.