Inferring regulatory element landscapes and transcription factor networks from cancer methylomes

Genome-wide DNA methylation regulation analysis provides novel insights on post-radiation breast cancer

Breast cancer (BC) is the most common malignancy with a poor prognosis. Radiotherapy is one of the leading traditional treatments for BC. However, radiotherapy-associated secondary diseases are severe issues for the treatment of BC. The present study integrated multi-omics data to investigate the molecular and epigenetic mechanisms involved in post-radiation BC. The differences in the expression of radiation-associated genes between post-radiation and pre-radiation BC samples were determined. Enrichment analysis revealed that these radiation-associated genes involved diverse biological functions and pathways in BC. Combining epigenetic data, we identified radiation-associated genes whose transcriptional changes might be associated with aberrant methylation. Then, we identified potential therapeutic targets and chemical drugs for post-radiation BC patient treatment by constructing a drug-target association network. Specifically, four radiation-associated genes (CD248, CCDC80, GADD45B, and MMP2) whose increased expression might be regulated by hypomethylation of the corresponding enhancer region were found to have excellent diagnostic effects and clinical prognostic value. Finally, we further used independent samples to verify CD248 expression and established a simple epigenetic regulatory model. In summary, this study provides novel insights for understanding the regulation of target genes mediated by DNA methylation and developing potential biomarkers for radiation-associated secondary diseases in BC.

Genome-wide association studies are enriched for interacting genes

BACKGROUND

With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.

RESULTS

We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs. matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.

CONCLUSIONS

Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

OCRClassifier: integrating statistical control chart into machine learning framework for better detecting open chromatin regions

Open chromatin regions (OCRs) play a crucial role in transcriptional regulation and gene expression. In recent years, there has been a growing interest in using plasma cell-free DNA (cfDNA) sequencing data to detect OCRs. By analyzing the characteristics of cfDNA fragments and their sequencing coverage, researchers can differentiate OCRs from non-OCRs. However, the presence of noise and variability in cfDNA-seq data poses challenges for the training data used in the noise-tolerance learning-based OCR estimation approach, as it contains numerous noisy labels that may impact the accuracy of the results. For current methods of detecting OCRs, they rely on statistical features derived from typical open and closed chromatin regions to determine whether a region is OCR or non-OCR. However, there are some atypical regions that exhibit statistical features that fall between the two categories, making it difficult to classify them definitively as either open or closed chromatin regions (CCRs). These regions should be considered as partially open chromatin regions (pOCRs). In this paper, we present OCRClassifier, a novel framework that combines control charts and machine learning to address the impact of high-proportion noisy labels in the training set and classify the chromatin open states into three classes accurately. Our method comprises two control charts. We first design a robust Hotelling T2 control chart and create new run rules to accurately identify reliable OCRs and CCRs within the initial training set. Then, we exclusively utilize the pure training set consisting of OCRs and CCRs to create and train a sensitized T2 control chart. This sensitized T2 control chart is specifically designed to accurately differentiate between the three categories of chromatin states: open, partially open, and closed. Experimental results demonstrate that under this framework, the model exhibits not only excellent performance in terms of three-class classification, but also higher accuracy and sensitivity in binary classification compared to the state-of-the-art models currently available.

Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC v1.0 BeadChip microarrays

DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC v1.0 arrays. We conducted a comprehensive assessment of the EPIC v1.0 array probe reliability using 69 blood DNA samples, each measured twice, generated by the Alzheimer's Disease Neuroimaging Initiative study. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliability information for probes on the EPIC v1.0 array, will serve as a valuable resource for future DNAm studies.

Genome wide association studies are enriched for interacting genes

Background

With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.

Results

We used genetic algorithms to measure fitness of gene-cell set proposals against a series of objective functions that capture data and annotations. The highest information objective function captured protein-protein interactions. We observed significantly greater fitness scores and subgraph sizes in foreground vs.matching sets of control variants. Furthermore, our model reliably identified known targets and ligand-receptor pairs, consistent with prior studies.

Conclusions

Our findings suggested that application of genetic algorithms to association studies can generate a coherent cellular model of risk from a set of susceptibility variants. Further, we showed, using breast cancer as an example, that such variants have a greater number of physical interactions than expected due to chance.

DNA methylation clocks for estimating biological age in Chinese cohorts

Epigenetic clocks are accurate predictors of human chronological age based on the analysis of DNA methylation (DNAm) at specific CpG sites. However, a systematic comparison between DNA methylation data and other omics datasets has not yet been performed. Moreover, available DNAm age predictors are based on datasets with limited ethnic representation. To address these knowledge gaps, we generated and analyzed DNA methylation datasets from two independent Chinese cohorts, revealing age-related DNAm changes. Additionally, a DNA methylation aging clock (iCAS-DNAmAge) and a group of DNAm-based multi-modal clocks for Chinese individuals were developed, with most of them demonstrating strong predictive capabilities for chronological age. The clocks were further employed to predict factors influencing aging rates. The DNAm aging clock, derived from multi-modal aging features (compositeAge-DNAmAge), exhibited a close association with multi-omics changes, lifestyles, and disease status, underscoring its robust potential for precise biological age assessment. Our findings offer novel insights into the regulatory mechanism of age-related DNAm changes and extend the application of the DNAm clock for measuring biological age and aging pace, providing the basis for evaluating aging intervention strategies.

MethNet: a robust approach to identify regulatory hubs and their distal targets from cancer data

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover cis regulatory elements (CREs) in a 1 Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as 'hubs', which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPR interference based single-cell RNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

Genome-wide profiling of WRKY genes involved in flavonoid biosynthesis in Erigeron breviscapus

The transcription factors of WRKY genes play essential roles in plant growth, stress responses, and metabolite biosynthesis. Erigeron breviscapus, a traditional Chinese herb, is abundant in flavonoids and has been used for centuries to treat cardiovascular and cerebrovascular diseases. However, the WRKY transcription factors that regulate flavonoid biosynthesis in E. breviscapus remain unknown. In this study, a total of 75 EbWRKY transcription factors were predicted through comprehensive genome-wide characterization of E. breviscapus and the chromosomal localization of each EbWRKY gene was investigated. RNA sequencing revealed transient responses of 74 predicted EbWRKY genes to exogenous abscisic acid (ABA), salicylic acid (SA), and gibberellin 3 (GA3) after 4 h of treatment. In contrast, the expression of key structural genes involved in flavonoid biosynthesis increased after 4 h in GA3 treatment. However, the content of flavonoid metabolites in leaves significantly increased at 12 h. The qRT-PCR results showed that the expression patterns of EbWRKY11, EbWRKY30, EbWRKY31, EbWRKY36, and EbWRKY44 transcription factors exhibited a high degree of similarity to the 11 structural genes involved in flavonoid biosynthesis. Protein-DNA interactions were performed between the key genes involved in scutellarin biosynthesis and candidate WRKYs. The result showed that F7GAT interacts with EbWRKY11, EbWRKY36, and EbWRKY44, while EbF6H has a self-activation function. This study provides comprehensive information on the regulatory control network of flavonoid accumulation mechanisms, offering valuable insights for breeding E. breviscapus varieties with enhanced scutellarin content.

Activation of Bivalent Gene POU4F1 Promotes and Maintains Basal-like Breast Cancer

Basal-like breast cancer (BLBC) is the most aggressive molecular subtype of breast cancer with worse prognosis and fewer treatment options. The underlying mechanisms upon BLBC transcriptional dysregulation and its upstream transcription factors (TFs) remain unclear. Here, among the hyperactive candidate TFs of BLBC identified by bioinformatic analysis, POU4F1 is uniquely upregulated in BLBC and is associated with poor prognosis. POU4F1 is necessary for the tumor growth and malignant phenotypes of BLBC through regulating G1/S transition by direct binding at the promoter of CDK2 and CCND1. More importantly, POU4F1 maintains BLBC identity by repressing ERα expression through CDK2-mediated EZH2 phosphorylation and subsequent H3K27me3 modification in ESR1 promoter. Knocking out POU4F1 in BLBC cells reactivates functional ERα expression, rendering BLBC sensitive to tamoxifen treatment. In-depth epigenetic analysis reveals that the subtype-specific re-configuration and activation of the bivalent chromatin in the POU4F1 promoter contributes to its unique expression in BLBC, which is maintained by DNA demethylase TET1. Together, these results reveal a subtype-specific epigenetically activated TF with critical role in promoting and maintaining BLBC, suggesting that POU4F1 is a potential therapeutic target for BLBC.

Epigenomic analyses identify FOXM1 as a key regulator of anti-tumor immune response in esophageal adenocarcinoma

Unlike most cancer types, the incidence of esophageal adenocarcinoma (EAC) has rapidly escalated in the western world over recent decades. Using whole genome bisulfite sequencing (WGBS), we identify the transcription factor (TF) FOXM1 as an important epigenetic regulator of EAC. FOXM1 plays a critical role in cellular proliferation and tumor growth in EAC patient-derived organoids and cell line models. We identify ERBB2 as an upstream regulator of the expression and transcriptional activity of FOXM1. Unexpectedly, gene set enrichment analysis (GSEA) unbiased screen reveals a prominent anti-correlation between FOXM1 and immune response pathways. Indeed, syngeneic mouse models show that FOXM1 inhibits the infiltration of CD8+ T cells into the tumor microenvironment. Consistently, FOXM1 suppresses CD8+ T cell chemotaxis in vitro and antigen-dependent CD8+ T cell killing. This study characterizes FOXM1 as a significant EAC-promoting TF and elucidates its novel function in regulating anti-tumor immune response.

Liquid biopsy in T-cell lymphoma: biomarker detection techniques and clinical application

T-cell lymphoma is a highly invasive tumor with significant heterogeneity. Invasive tissue biopsy is the gold standard for acquiring molecular data and categorizing lymphoma patients into genetic subtypes. However, surgical intervention is unfeasible for patients who are critically ill, have unresectable tumors, or demonstrate low compliance, making tissue biopsies inaccessible to these patients. A critical need for a minimally invasive approach in T-cell lymphoma is evident, particularly in the areas of early diagnosis, prognostic monitoring, treatment response, and drug resistance. Therefore, the clinical application of liquid biopsy techniques has gained significant attention in T-cell lymphoma. Moreover, liquid biopsy requires fewer samples, exhibits good reproducibility, and enables real-time monitoring at molecular levels, thereby facilitating personalized health care. In this review, we provide a comprehensive overview of the current liquid biopsy biomarkers used for T-cell lymphoma, focusing on circulating cell-free DNA (cfDNA), circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), Epstein-Barr virus (EBV) DNA, antibodies, and cytokines. Additionally, we discuss their clinical application, detection methodologies, ongoing clinical trials, and the challenges faced in the field of liquid biopsy.

A Feature Selection Method Based on Graph Theory for Cancer Classification

OBJECTIVE

Gene expression profile data is a good data source for people to study tumors, but gene expression data has the characteristics of high dimension and redundancy. Therefore, gene selection is a very important step in microarray data classification.

METHODS

In this paper, a feature selection method based on the maximum mutual information coefficient and graph theory is proposed. Each feature of gene expression data is treated as a vertex of the graph, and the maximum mutual information coefficient between genes is used to measure the relationship between the vertices to construct an undirected graph, and then the core and coritivity theory is used to determine the feature subset of gene data.

RESULTS

In this work, we used three different classification models and three different evaluation metrics such as accuracy, F1-Score, and AUC to evaluate the classification performance to avoid reliance on any one classifier or evaluation metric. The experimental results on six different types of genetic data show that our proposed algorithm has high accuracy and robustness compared to other advanced feature selection methods.

CONCLUSION

In this method, the importance and correlation of features are considered at the same time, and the problem of gene selection in microarray data classification is solved.

Integrative approaches based on genomic techniques in the functional studies on enhancers

With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1-6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and largescale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 elementgene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancerpromoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

Enhancer target prediction: state-of-the-art approaches and future prospects

Enhancers are genomic regions that regulate gene transcription and are located far away from the transcription start sites of their target genes. Enhancers are highly enriched in disease-associated variants and thus deciphering the interactions between enhancers and genes is crucial to understanding the molecular basis of genetic predispositions to diseases. Experimental validations of enhancer targets can be laborious. Computational methods have thus emerged as a valuable alternative for studying enhancer-gene interactions. A variety of computational methods have been developed to predict enhancer targets by incorporating genomic features (e.g. conservation, distance, and sequence), epigenomic features (e.g. histone marks and chromatin contacts) and activity measurements (e.g. covariations of enhancer activity and gene expression). With the recent advances in genome perturbation and chromatin conformation capture technologies, data on experimentally validated enhancer targets are becoming available for supervised training of these methods and evaluation of their performance. In this review, we categorize enhancer target prediction methods based on their rationales and approaches. Then we discuss their merits and limitations and highlight the future directions for enhancer targets prediction.

Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC BeadChip microarrays

DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC arrays. We conducted a comprehensive assessment of the EPIC array probe reliability using 138 duplicated blood DNAm samples generated by the Alzheimer's Disease Neuroimaging Initiative study. We introduced a novel statistical measure, the modified intraclass correlation, to better account for the disagreement in duplicate measurements. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliable information for probes on the EPIC array, will serve as a valuable resource for future DNAm studies.

Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC BeadChip microarrays

DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC arrays. We conducted a comprehensive assessment of the EPIC array probe reliability using 138 duplicated blood DNAm samples generated by the Alzheimer's Disease Neuroimaging Initiative study. We introduced a novel statistical measure, the modified intraclass correlation, to better account for the disagreement in duplicate measurements. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliable information for probes on the EPIC array, will serve as a valuable resource for future DNAm studies.

Super enhancer-driven core transcriptional regulatory circuitry crosstalk with cancer plasticity and patient mortality in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a clinically aggressive subtype of breast cancer. Core transcriptional regulatory circuitry (CRC) consists of autoregulated transcription factors (TFs) and their enhancers, which dominate gene expression programs and control cell fate. However, there is limited knowledge of CRC in TNBC. Herein, we systemically characterized the activated super-enhancers (SEs) and interrogated 14 CRCs in breast cancer. We found that CRCs could be broadly involved in DNA conformation change, metabolism process, and signaling response affecting the gene expression reprogramming. Furthermore, these CRC TFs are capable of coordinating with partner TFs bridging the enhancer-promoter loops. Notably, the CRC TF and partner pairs show remarkable specificity for molecular subtypes of breast cancer, especially in TNBC. USF1, SOX4, and MYBL2 were identified as the TNBC-specific CRC TFs. We further demonstrated that USF1 was a TNBC immunophenotype-related TF. Our findings that the rewiring of enhancer-driven CRCs was related to cancer immune and mortality, will facilitate the development of epigenetic anti-cancer treatment strategies.

CRISPR/dCAS9-mediated DNA demethylation screen identifies functional epigenetic determinants of colorectal cancer

BACKGROUND

Promoter hypermethylation of tumour suppressor genes is frequently observed during the malignant transformation of colorectal cancer (CRC). However, whether this epigenetic mechanism is functional in cancer or is a mere consequence of the carcinogenic process remains to be elucidated.

RESULTS

In this work, we performed an integrative multi-omic approach to identify gene candidates with strong correlations between DNA methylation and gene expression in human CRC samples and a set of 8 colon cancer cell lines. As a proof of concept, we combined recent CRISPR-Cas9 epigenome editing tools (dCas9-TET1, dCas9-TET-IM) with a customized arrayed gRNA library to modulate the DNA methylation status of 56 promoters previously linked with strong epigenetic repression in CRC, and we monitored the potential functional consequences of this DNA methylation loss by means of a high-content cell proliferation screen. Overall, the epigenetic modulation of most of these DNA methylated regions had a mild impact on the reactivation of gene expression and on the viability of cancer cells. Interestingly, we found that epigenetic reactivation of RSPO2 in the tumour context was associated with a significant impairment in cell proliferation in p53-/- cancer cell lines, and further validation with human samples demonstrated that the epigenetic silencing of RSPO2 is a mid-late event in the adenoma to carcinoma sequence.

CONCLUSIONS

These results highlight the potential role of DNA methylation as a driver mechanism of CRC and paves the way for the identification of novel therapeutic windows based on the epigenetic reactivation of certain tumour suppressor genes.

Transcription factor expression repertoire basis for epigenetic and transcriptional subtypes of colorectal cancers

Colorectal cancers (CRCs) form a heterogenous group classified into epigenetic and transcriptional subtypes. The basis for the epigenetic subtypes, exemplified by varying degrees of promoter DNA hypermethylation, and its relation to the transcriptional subtypes is not well understood. We link cancer-specific transcription factor (TF) expression alterations to methylation alterations near TF-binding sites at promoter and enhancer regions in CRCs and their premalignant precursor lesions to provide mechanistic insights into the origins and evolution of the CRC molecular subtypes. A gradient of TF expression changes forms a basis for the subtypes of abnormal DNA methylation, termed CpG-island promoter DNA methylation phenotypes (CIMPs), in CRCs and other cancers. CIMP is tightly correlated with cancer-specific hypermethylation at enhancers, which we term CpG-enhancer methylation phenotype (CEMP). Coordinated promoter and enhancer methylation appears to be driven by downregulation of TFs with common binding sites at the hypermethylated enhancers and promoters. The altered expression of TFs related to hypermethylator subtypes occurs early during CRC development, detectable in premalignant adenomas. TF-based profiling further identifies patients with worse overall survival. Importantly, altered expression of these TFs discriminates the transcriptome-based consensus molecular subtypes (CMS), thus providing a common basis for CIMP and CMS subtypes.

MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer

Aberrations in the capacity of DNA/chromatin modifiers and transcription factors to bind non-coding regions can lead to changes in gene regulation and impact disease phenotypes. However, identifying distal regulatory elements and connecting them with their target genes remains challenging. Here, we present MethNet, a pipeline that integrates large-scale DNA methylation and gene expression data across multiple cancers, to uncover novel cis regulatory elements (CREs) in a 1Mb region around every promoter in the genome. MethNet identifies clusters of highly ranked CREs, referred to as 'hubs', which contribute to the regulation of multiple genes and significantly affect patient survival. Promoter-capture Hi-C confirmed that highly ranked associations involve physical interactions between CREs and their gene targets, and CRISPRi based scRNA Perturb-seq validated the functional impact of CREs. Thus, MethNet-identified CREs represent a valuable resource for unraveling complex mechanisms underlying gene expression, and for prioritizing the verification of predicted non-coding disease hotspots.

Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma

Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.

EpiMix is an integrative tool for epigenomic subtyping using DNA methylation

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer and immunological and cardiovascular diseases. Recent technological advances have enabled genome-wide profiling of DNAme in large human cohorts. There is a need for analytical methods that can more sensitively detect differential methylation profiles present in subsets of individuals from these heterogeneous, population-level datasets. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared with existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and long non-coding RNAs (lncRNAs). Using cell-type-specific data from two separate studies, we discover epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven ncRNAs in non-small cell lung cancer.

Integrative Co-methylation Network Analysis Identifies Novel DNA Methylation Signatures and Their Target Genes in Alzheimer's Disease

BACKGROUND

DNA methylation is a key epigenetic marker, and its alternations may be involved in Alzheimer's disease (AD). CpGs sharing similar biological functions or pathways tend to be co-methylated.

METHODS

We performed an integrative network-based DNA methylation analysis on 2 independent cohorts (N = 941) using brain DNA methylation profiles and RNA-sequencing as well as AD pathology data.

RESULTS

Weighted co-methylation network analysis identified 6 modules as significantly associated with neuritic plaque burden. In total, 15 hub CpGs including 3 novel CpGs were identified and replicated as being significantly associated with AD pathology. Furthermore, we identified and replicated 4 target genes (ATP6V1G2, VCP, RAD52, and LST1) as significantly regulated by DNA methylation at hub CpGs. In particular, VCP gene expression was also associated with AD pathology in both cohorts.

CONCLUSIONS

This integrative network-based multiomics study provides compelling evidence for a potential role of DNA methylation alternations and their target genes in AD.

The non-coding genome in Autism Spectrum Disorders

Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests. While ASD have been proven to have a strong genetic component, current research largely focuses on coding regions of the genome. However, non-coding DNA, which makes up for ∼99% of the human genome, has recently been recognized as an important contributor to the high heritability of ASD, and novel sequencing technologies have been a milestone in opening up new directions for the study of the gene regulatory networks embedded within the non-coding regions. Here, we summarize current progress on the contribution of non-coding alterations to the pathogenesis of ASD and provide an overview of existing methods allowing for the study of their functional relevance, discussing potential ways of unraveling ASD's "missing heritability".

Identifying Key Regulators of Keratinization in Lung Squamous Cell Cancer Using Integrated TCGA Analysis

Keratinization is one of lung squamous cell cancer’s (LUSC) hallmark histopathology features. Epithelial cells produce keratin to protect their integrity from external harmful substances. In addition to their roles as cell protectors, recent studies have shown that keratins have important roles in regulating either normal cell or tumor cell functions. The objective of this study is to identify the genes and microRNAs (miRNAs) that act as key regulators of the keratinization process in LUSC. To address this goal, we classified LUSC samples from GDC-TCGA databases based on their keratinization molecular signatures. Then, we performed differential analyses of genes, methylation, and miRNA expression between high keratinization and low keratinization samples. By reconstruction and analysis of the differentially expressed genes (DEGs) network, we found that TP63 and SOX2 were the hub genes that were highly connected to other genes and displayed significant correlations with several keratin genes. Methylation analysis showed that the P63, P73, and P53 DNA-binding motif sites were significantly enriched for differentially methylated probes. We identified SNAI2, GRHL3, TP63, ZNF750, and FOXE1 as the top transcription factors associated with these binding sites. Finally, we identified 12 miRNAs that influence the keratinization process by using miRNA–mRNA correlation analysis.

Enhancer/gene relationships: Need for more reliable genome-wide reference sets

Differences in cells' functions arise from differential activity of regulatory elements, including enhancers. Enhancers are cis-regulatory elements that cooperate with promoters through transcription factors to activate the expression of one or several genes by getting physically close to them in the 3D space of the nucleus. There is increasing evidence that genetic variants associated with common diseases are enriched in enhancers active in cell types relevant to these diseases. Identifying the enhancers associated with genes and conversely, the sets of genes activated by each enhancer (the so-called enhancer/gene or E/G relationships) across cell types, can help understanding the genetic mechanisms underlying human diseases. There are three broad approaches for the genome-wide identification of E/G relationships in a cell type: 1) genetic link methods or eQTL, 2) functional link methods based on 1D functional data such as open chromatin, histone mark or gene expression and 3) spatial link methods based on 3D data such as HiC. Since 1) and 3) are costly, the current strategy is to develop functional link methods and to use data from 1) and 3) as reference to evaluate them. However, there is still no consensus on the best functional link method to date, and method comparison remain seldom. Here, we compared the relative performances of three recent methods for the identification of enhancer-gene links, TargetFinder, Average-Rank, and the ABC model, using the three latest benchmarks from the field: a reference that combines 3D and eQTL data, called BENGI, and two genetic screening references, called CRiFF and CRiSPRi. Overall, none of the three methods performed best on the three references. CRiFF and CRISPRi reference sets are likely more reliable, but CRiFF is not genome-wide and CRiFF and CRISPRi are mostly available on the K562 cancer cell line. The BENGI reference set is genome-wide but likely contains many false positives. This study therefore calls for new reliable and genome-wide E/G reference data rather than new functional link E/G identification methods.

Integrated analysis reveals common DNA methylation patterns of alcohol-associated cancers: A pan-cancer analysis

Background: The role of alcohol in carcinogenesis has received increasing attention in recent years. Evidence shows its impacts on various aspects, including epigenetics alteration. The DNA methylation patterns underlying alcohol-associated cancers are not fully understood. Methods: We investigated the aberrant DNA methylation patterns in four alcohol-associated cancers based on the Illumina HumanMethylation450 BeadChip. Pearson coefficient correlations were identified between differential methylated CpG probes and annotated genes. Transcriptional factor motifs were enriched and clustered using MEME Suite, and a regulatory network was constructed. Results: In each cancer, differential methylated probes (DMPs) were identified, and 172 hypermethylated and 21 hypomethylated pan-cancer DMPs (PDMPs) were examined further. Annotated genes significantly regulated by PDMPs were investigated and enriched in transcriptional misregulation in cancers. The CpG island chr19:58220189-58220517 was hypermethylated in all four cancers and silenced in the transcription factor ZNF154. Various biological effects were exerted by 33 hypermethylated and seven hypomethylated transcriptional factor motifs grouped into five clusters. Eleven pan-cancer DMPs were identified to be associated with clinical outcomes in the four alcohol-associated cancers, which might provide a potential point of view for clinical outcome prediction. Conclusion: This study provides an integrated insight into DNA methylation patterns in alcohol-associated cancers and reveals the corresponding features, influences, and potential mechanisms.

EpiMix: an integrative tool for epigenomic subtyping using DNA methylation

DNA methylation (DNAme) is a major epigenetic factor influencing gene expression with alterations leading to cancer, immunological, and cardiovascular diseases. Recent technological advances enable genome-wide quantification of DNAme in large human cohorts. So far, existing methods have not been evaluated to identify differential DNAme present in large and heterogeneous patient cohorts. We developed an end-to-end analytical framework named "EpiMix" for population-level analysis of DNAme and gene expression. Compared to existing methods, EpiMix showed higher sensitivity in detecting abnormal DNAme that was present in only small patient subsets. We extended the model-based analyses of EpiMix to cis-regulatory elements within protein-coding genes, distal enhancers, and genes encoding microRNAs and lncRNAs. Using cell-type specific data from two separate studies, we discovered novel epigenetic mechanisms underlying childhood food allergy and survival-associated, methylation-driven non-coding RNAs in non-small cell lung cancer.

Inferring regulatory element landscapes and gene regulatory networks from integrated analysis in eight hulless barley varieties under abiotic stress

BACKGROUND

The cis-regulatory element became increasingly important for resistance breeding. There were many DNA variations identified by resequencing. To investigate the links between the DNA variations and cis-regulatory element was the fundamental work. DNA variations in cis-regulatory elements caused phenotype variations in general.

RESULTS

We used WGBS, ChIP-seq and RNA-seq technology to decipher the regulatory element landscape from eight hulless barley varieties under four kinds of abiotic stresses. We discovered 231,440 lowly methylated regions (LMRs) from the methylome data of eight varieties. The LMRs mainly distributed in the intergenic regions. A total of 97,909 enhancer-gene pairs were identified from the correlation analysis between methylation degree and expression level. A lot of enriched motifs were recognized from the tolerant-specific LMRs. The key transcription factors were screened out and the transcription factor regulatory network was inferred from the enhancer-gene pairs data for drought stress. The NAC transcription factor was predicted to target to TCP, bHLH, bZIP transcription factor genes. We concluded that the H3K27me3 modification regions overlapped with the LMRs more than the H3K4me3. The variation of single nucleotide polymorphism was more abundant in LMRs than the remain regions of the genome.

CONCLUSIONS

Epigenetic regulation is an important mechanism for organisms to adapt to complex environments. Through the study of DNA methylation and histone modification, we found that many changes had taken place in enhancers and transcription factors in the abiotic stress of hulless barley. For example, transcription factors including NAC may play an important role. This enriched the molecular basis of highland barley stress response.

CanMethdb: a database for genome-wide DNA methylation annotation in cancers

MOTIVATION

DNA methylation within gene body and promoters in cancer cells is well documented. An increasing number of studies showed that cytosine-phosphate-guanine (CpG) sites falling within other regulatory elements could also regulate target gene activation, mainly by affecting transcription factors (TFs) binding in human cancers. This led to the urgent need for comprehensively and effectively collecting distinct cis-regulatory elements and TF-binding sites (TFBS) to annotate DNA methylation regulation.

RESULTS

We developed a database (CanMethdb, http://meth.liclab.net/CanMethdb/) that focused on the upstream and downstream annotations for CpG-genes in cancers. This included upstream cis-regulatory elements, especially those involving distal regions to genes, and TFBS annotations for the CpGs and downstream functional annotations for the target genes, computed through integrating abundant DNA methylation and gene expression profiles in diverse cancers. Users could inquire CpG-target gene pairs for a cancer type through inputting a genomic region, a CpG, a gene name, or select hypo/hypermethylated CpG sets. The current version of CanMethdb documented a total of 38 986 060 CpG-target gene pairs (with 6 769 130 unique pairs), involving 385 217 CpGs and 18 044 target genes, abundant cis-regulatory elements and TFs for 33 TCGA cancer types. CanMethdb might help biologists perform in-depth studies of target gene regulations based on DNA methylations in cancer.

AVAILABILITY AND IMPLEMENTATION

The main program is available at https://github.com/chunquanlipathway/CanMethdb.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA methylation and transcriptomic features are preserved throughout disease recurrence and chemoresistance in high grade serous ovarian cancers

Background

Little is known about the role of global DNA methylation in recurrence and chemoresistance of high grade serous ovarian cancer (HGSOC).

Methods

We performed whole genome bisulfite sequencing and transcriptome sequencing in 62 primary and recurrent tumors from 28 patients with stage III/IV HGSOC, of which 11 patients carried germline, pathogenic BRCA1 and/or BRCA2 mutations.

Results

Landscapes of genome-wide methylation (on average 24.2 million CpGs per tumor) and transcriptomes in primary and recurrent tumors showed extensive heterogeneity between patients but were highly preserved in tumors from the same patient. We identified significant differences in the burden of differentially methylated regions (DMRs) in tumors from BRCA1/2 compared to non-BRCA1/2 carriers (mean 659 DMRs and 388 DMRs in paired comparisons respectively). We identified overexpression of immune pathways in BRCA1/2 carriers compared to non-carriers, implicating an increased immune response in improved survival (P = 0.006) in these BRCA1/2 carriers.

Conclusion

These findings indicate methylome and gene expression programs established in the primary tumor are conserved throughout disease progression, even after extensive chemotherapy treatment, and that changes in methylation and gene expression are unlikely to serve as drivers for chemoresistance in HGSOC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02440-z.

Generation and multiomic profiling of a TP53/CDKN2A double-knockout gastroesophageal junction organoid model

Inactivation of the tumor suppressor genes tumor protein p53 (TP53) and cyclin-dependent kinase inhibitor 2A (CDKN2A) occurs early during gastroesophageal junction (GEJ) tumorigenesis. However, because of a paucity of GEJ-specific disease models, cancer-promoting consequences of TP53 and CDKN2A inactivation at the GEJ have not been characterized. Here, we report the development of a wild-type primary human GEJ organoid model and a CRISPR-edited transformed GEJ organoid model. CRISPR-Cas9-mediated TP53 and CDKN2A knockout (TP53/CDKN2AKO) in GEJ organoids induced morphologic dysplasia and proneoplastic features in vitro and tumor formation in vivo. Lipidomic profiling identified several platelet-activating factors (PTAFs) among the most up-regulated lipids in CRISPR-edited organoids. PTAF/PTAF receptor (PTAFR) abrogation by siRNA knockdown or a pharmacologic inhibitor (WEB2086) reduced proliferation and other proneoplastic features of TP53/CDKN2AKO GEJ organoids in vitro and tumor formation in vivo. In addition, murine xenografts of Eso26, an established human esophageal adenocarcinoma cell line, were suppressed by WEB2086. Mechanistically, TP53/CDKN2A dual inactivation disrupted both the transcriptome and the DNA methylome, likely mediated by key transcription factors, particularly forkhead box M1 (FOXM1). FOXM1 activated PTAFR transcription by binding to the PTAFR promoter, further amplifying the PTAF-PTAFR pathway. Together, these studies established a robust model system for investigating early GEJ neoplastic events, identified crucial metabolic and epigenomic changes occurring during GEJ model tumorigenesis, and revealed a potential cancer therapeutic strategy. This work provides insights into proneoplastic mechanisms associated with TP53/CDKN2A inactivation in early GEJ neoplasia, which may facilitate early diagnosis and prevention of GEJ neoplasms.

Profiling Analysis of Circular RNA and mRNA in Human Temporal Lobe Epilepsy with Hippocampal Sclerosis ILAE Type 1

Hippocampal sclerosis (HS) is the most common surgical pathology associated with temporal lobe epilepsy (TLE). However, the cause of TLE with or without HS remains unknown. Our current study aimed to illustrate the essential molecular mechanism that is potentially involved in the pathogenesis of TLE-HS and to shed light on the transcriptional changes associated with hippocampal sclerosis. Compared to no-HS group, 341 mRNA transcripts and 131 circRNA transcripts were differentially expressed in ILAE type 1 group. The raw sequencing data have been deposited into sequence-read archive (SRA) database under accession number PRJNA699348.Gene Ontology analysis demonstrated that the dysregulated genes were associated with the biological processes of vesicle-mediated transport. Enrichment analysis demonstrated that dysregulated genes were involved mainly in the MAPK signal pathway. Subsequently, A total of 441 known or predicted interactions were formed among DEGs, and the most important module was detected in the PPI network using the MCODE plug-in. There were mainly four functional modules enriched: ER to Golgi transport vesicle membrane, Basal transcription factors, GABA-gated chloride ion channel activity, CENP-A containing nucleosome assembly. A circRNA-mRNA co-expression network was constructed including 5 circRNAs(hsa_circ_0025349, hsa_circ_0002405, hsa_circ_0004805, hsa_circ_0032254, and hsa_circ_0032875) and three mRNAs (FYN, SELENBP1, and GRIPAP1) based on the normalized mRNA signal intensities. This is the first to report the circRNAs and mRNAs expression profile of surgically resected hippocampal tissues from TLE patients of ILAE-1 and no-HS, and these results may provide new insight into the transcriptional changes associated with this pathology.

Analyzing integrated network of methylation and gene expression profiles in lung squamous cell carcinoma

Gene expression, DNA methylation, and their organizational relationships are commonly altered in lung squamous cell carcinoma (LUSC). To elucidate these complex interactions, we reconstructed a differentially expressed gene network and a differentially methylated cytosine (DMC) network by partial information decomposition and an inverse correlation algorithm, respectively. Then, we performed graph union to integrate the networks. Community detection and enrichment analysis of the integrated network revealed close interactions between the cell cycle, keratinization, immune system, and xenobiotic metabolism gene sets in LUSC. DMC analysis showed that hypomethylation targeted the gene sets responsible for cell cycle, keratinization, and NRF2 pathways. On the other hand, hypermethylated genes affected circulatory system development, the immune system, extracellular matrix organization, and cilium organization. By centrality measurement, we identified NCAPG2, PSMG3, and FADD as hub genes that were highly connected to other nodes and might play important roles in LUSC gene dysregulation. We also found that the genes with high betweenness centrality are more likely to affect patients’ survival than those with low betweenness centrality. These results showed that the integrated network analysis enabled us to obtain a global view of the interactions and regulations in LUSC.

Pan-cancer landscape of the RUNX protein family reveals their potential as carcinogenic biomarkers and the mechanisms underlying their action

Background

The RUNX family of transcription factors plays an important regulatory role in tumor development. Although the importance of RUNX in certain cancer types is well known, the pan-cancer landscape remains unclear.

Materials and Methods

Data from The Cancer Genome Atlas (TCGA) provides a pan-cancer overview of the RUNX genes. Hence, herein, we performed a pan-cancer analysis of abnormal RUNX expression and deciphered the potential regulatory mechanism. Specifically, we used TCGA multi-omics data combined with multiple online tools to analyze transcripts, genetic alterations, DNA methylation, clinical prognoses, miRNA networks, and potential target genes.

Results

RUNX genes are consistently overexpressed in esophageal, gastric, pancreatic, and pan-renal cancers. The total protein expression of RUNX1 in lung adenocarcinoma, kidney renal clear cell carcinoma (KIRC), and uterine corpus endometrial carcinoma (UCEC) is consistent with the mRNA expression results. Moreover, increased phosphorylation on the T14 and T18 residues of RUNX1 may represent potential pathogenic factors. The RUNX genes are significantly associated with survival in pan-renal cancer, brain lower-grade glioma, and uveal melanoma. Meanwhile, various mutations and posttranscriptional changes, including the RUNX1 D96 mutation in invasive breast carcinoma, the co-occurrence of RUNX gene mutations in UCEC, and methylation changes in the RUNX2 promoter in KIRC, may be associated with cancer development. Finally, analysis of epigenetic regulator co-expression, miRNA networks, and target genes revealed the carcinogenicity, abnormal expression, and direct regulation of RUNX genes.

Conclusions

We successfully analyzed the pan-cancer abnormal expression and prognostic value of RUNX genes, thereby providing potential biomarkers for various cancers. Further, mutations revealed via genetic alteration analysis may serve as a basis for personalized patient therapies.

Structural and Functional Insights into CP2c Transcription Factor Complexes

CP2c, also known as TFCP2, α-CP2, LSF, and LBP-1c, is a prototypic member of the transcription factor (TF) CP2 subfamily involved in diverse ubiquitous and tissue/stage-specific cellular processes and in human malignancies including cancer. Despite its importance, many fundamental regulatory mechanisms of CP2c are still unclear. Here, we uncover unprecedented structural and functional aspects of CP2c using DSP crosslinking and Western blot in addition to conventional methods. We found that a monomeric form of a CP2c homotetramer (tCP2c; [C4]) binds to the known CP2c-binding DNA motif (CNRG-N(5~6)-CNRG), whereas a dimeric form of a CP2c, CP2b, and PIAS1 heterohexamer ([C2B2P2]₂) binds to the three consecutive CP2c half-sites or two staggered CP2c binding motifs, where the [C4] exerts a pioneering function for recruiting the [C2B2P2]₂ to the target. All CP2c exists as a [C4], or as a [C2B2P2]₂ or [C2B2P2]₄ in the nucleus. Importantly, one additional cytosolic heterotetrameric CP2c and CP2a complex, ([C2A2]), exerts some homeostatic regulation of the nuclear complexes. These data indicate that these findings are essential for the transcriptional regulation of CP2c in cells within relevant timescales, providing clues not only for the transcriptional regulation mechanism by CP2c but also for future therapeutics targeting CP2c function.

Enhancer methylation dynamics drive core transcriptional regulatory circuitry in pan-cancer

Accumulating evidence has demonstrated that enhancer methylation has strong and dynamic regulatory effects on gene expression. Some transcription factors (TFs) can auto- and cross-regulate in a feed-forward manner, and cooperate with their enhancers to form core transcriptional regulatory circuitries (CRCs). However, the elaborated regulatory mechanism between enhancer methylation and CRC remains the tip of the iceberg. Here, we revealed that DNA methylation could drive the tissue-specific enhancer basal transcription and target gene expression in human cancers. By integrating methylome, transcriptome, and 3D genomic data, we identified enhancer methylation triplets (enhancer methylation-enhancer transcription-target gene expression) and dissected potential regulatory patterns within them. Moreover, we observed that cancer-specific core TFs regulated by enhancers were able to shape their enhancer methylation forming the enhancer methylation-driven CRCs (emCRCs). Further parsing of clinical implications showed rewired emCRCs could serve as druggable targets and prognostic risk markers. In summary, the integrative analysis of enhancer methylation regulome would facilitate portraying the cancer epigenomics landscape and developing the epigenetic anti-cancer approaches.

Flexible copula model for integrating correlated multi-omics data from single-cell experiments

With recent advances in technologies to profile multi-omics data at the single-cell level, integrative multi-omics data analysis has been increasingly popular. It is increasingly common that information such as methylation changes, chromatin accessibility, and gene expression are jointly collected in a single-cell experiment. In biomedical studies, it is often of interest to study the associations between various data types and to examine how these associations might change according to other factors such as cell types and gene regulatory components. However, since each data type usually has a distinct marginal distribution, joint analysis of these changes of associations using multi-omics data is statistically challenging. In this paper, we propose a flexible copula-based framework to model covariate-dependent correlation structures independent of their marginals. In addition, the proposed approach could jointly combine a wide variety of univariate marginal distributions, either discrete or continuous, including the class of zero-inflated distributions. The performance of the proposed framework is demonstrated through a series of simulation studies. Finally, it is applied to a set of experimental data to investigate the dynamic relationship between single-cell RNA-sequencing, chromatin accessibility, and DNA methylation at different germ layers during mouse gastrulation. This article is protected by copyright. All rights reserved.

MethReg: estimating the regulatory potential of DNA methylation in gene transcription

Epigenome-wide association studies often detect many differentially methylated sites, and many are located in distal regulatory regions. To further prioritize these significant sites, there is a critical need to better understand the functional impact of CpG methylation. Recent studies demonstrated that CpG methylation-dependent transcriptional regulation is a widespread phenomenon. Here, we present MethReg, an R/Bioconductor package that analyzes matched DNA methylation and gene expression data, along with external transcription factor (TF) binding information, to evaluate, prioritize and annotate CpG sites with high regulatory potential. At these CpG sites, TF-target gene associations are often only present in a subset of samples with high (or low) methylation levels, so they can be missed by analyses that use all samples. Using colorectal cancer and Alzheimer's disease datasets, we show MethReg significantly enhances our understanding of the regulatory roles of DNA methylation in complex diseases.

Comprehensive enhancer-target gene assignments improve gene set level interpretation of genome-wide regulatory data

BACKGROUND

Revealing the gene targets of distal regulatory elements is challenging yet critical for interpreting regulome data. Experiment-derived enhancer-gene links are restricted to a small set of enhancers and/or cell types, while the accuracy of genome-wide approaches remains elusive due to the lack of a systematic evaluation. We combined multiple spatial and in silico approaches for defining enhancer locations and linking them to their target genes aggregated across >500 cell types, generating 1860 human genome-wide distal enhancer-to-target gene definitions (EnTDefs). To evaluate performance, we used gene set enrichment (GSE) testing on 87 independent ENCODE ChIP-seq datasets of 34 transcription factors (TFs) and assessed concordance of results with known TF Gene Ontology annotations, and other benchmarks.

RESULTS

The top ranked 741 (40%) EnTDefs significantly outperform the common, naïve approach of linking distal regions to the nearest genes, and the top 10 EnTDefs perform well when applied to ChIP-seq data of other cell types. The GSE-based ranking of EnTDefs is highly concordant with ranking based on overlap with curated benchmarks of enhancer-gene interactions. Both our top general EnTDef and cell-type-specific EnTDefs significantly outperform seven independent computational and experiment-based enhancer-gene pair datasets. We show that using our top EnTDefs for GSE with either genome-wide DNA methylation or ATAC-seq data is able to better recapitulate the biological processes changed in gene expression data performed in parallel for the same experiment than our lower-ranked EnTDefs.

CONCLUSIONS

Our findings illustrate the power of our approach to provide genome-wide interpretation regardless of cell type.

Pioneer transcription factors are associated with the modulation of DNA methylation patterns across cancers

Methylation of cytosines on DNA is a prominent modification associated with gene expression regulation. Aberrant DNA methylation patterns have recurrently been linked to dysregulation of the regulatory program in cancer cells. To shed light on the underlying molecular mechanism driving this process, we hypothesised that aberrant methylation patterns could be controlled by the binding of specific transcription factors (TFs) across cancer types. By combining DNA methylation arrays and gene expression data with TF binding sites (TFBSs), we explored the interplay between TF binding and DNA methylation in 19 cancer types. We performed emQTL (expression-methylation quantitative trait loci) analyses independently in each cancer type and identified 13 TFs whose expression levels are correlated with local DNA methylation patterns around their binding sites in at least 2 cancer types. The 13 TFs are mainly associated with local demethylation and are enriched for pioneer function, suggesting a specific role for these TFs in modulating chromatin structure and transcription in cancer patients. Furthermore, we confirmed that de novo methylation is precluded across cancers at CpGs lying in genomic regions enriched for TF binding signatures associated with SP1, CTCF, NRF1, GABPA, KLF9, and/or YY1. The modulation of DNA methylation associated with TF binding was observed at cis-regulatory regions controlling immune- and cancer-associated pathways, corroborating that the emQTL signals were derived from both cancer and tumor-infiltrating cells. As a case example, we experimentally confirmed that FOXA1 knock-down is associated with higher methylation in regions bound by FOXA1 in breast cancer MCF-7 cells. Finally, we reported physical interactions between FOXA1 with TET1 and TET2 both in an in vitro setup and in vivo at physiological levels in MCF-7 cells, adding further support for FOXA1 attracting TET1 and TET2 to induce local demethylation in cancer cells.

Pan-cancer predictions of transcription factors mediating aberrant DNA methylation

BACKGROUND

Aberrant DNA methylation is a hallmark of cancer cells. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors initially thought to be repressed from binding by DNA methylation, have recently emerged as being able to shape DNA methylation patterns.

RESULTS

Here, we integrated the massive amount of data available from The Cancer Genome Atlas to predict transcription factors driving aberrant DNA methylation in 13 cancer types. We identified differentially methylated regions between cancer and matching healthy samples, searched for transcription factor motifs enriched in those regions and selected transcription factors with corresponding changes in gene expression. We predict transcription factors known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict transcription factors that lead to hyper-methylated regions upon transcription factor loss such as EGR1 in several cancer types. Finally, we validate that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells.

CONCLUSION

Our work highlights the importance of some transcription factors as upstream regulators shaping DNA methylation patterns in cancer.

Clinicopathological implications of lncRNAs, immunotherapy and DNA methylation in lung squamous cell carcinoma: a narrative review

Objective

To explore the clinicopathological impact of lncRNAs, immunotherapy, and DNA methylation in lung squamous cell carcinoma (LUSC), emphasizing their exact roles in carcinogenesis and modes of action.

Background

LUSC is the second most prevalent form, accounting for around 30% of non-small cell lung cancer (NSCLC). To date, molecular-targeted treatments have significantly improved overall survival in lung adenocarcinoma patients but have had little effect on LUSC therapy. As a result, there is an urgent need to discover new treatments for LUSC that are based on existing genomic methods.

Methods

In this review, we summarized and analyzed recent research on the biological activities and processes of lncRNA, immunotherapy, and DNA methylation in the formation of LUSC. The relevant studies were retrieved using a thorough search of Pubmed, Web of Science, Science Direct, Google Scholar, and the university's online library, among other sources.

Conclusions

LncRNAs are the primary components of the mammalian transcriptome and are emerging as master regulators of a number of cellular processes, including the cell cycle, differentiation, apoptosis, and growth, and are implicated in the pathogenesis of a variety of cancers, including LUSC. Understanding their role in LUSC in detail may help develop innovative treatment methods and tactics for LUSC. Meanwhile, immunotherapy has transformed the LUSC treatment and is now considered the new standard of care. To get a better knowledge of LUSC biology, it is critical to develop superior modeling systems. Preclinical models, particularly those that resemble human illness by preserving the tumor immune environment, are essential for studying cancer progression and evaluating novel treatment targets. DNA methylation, similarly, is a component of epigenetic alterations that regulate cellular function and contribute to cancer development. By methylating the promoter regions of tumor suppressor genes, abnormal DNA methylation silences their expression. DNA methylation indicators are critical in the early detection of lung cancer, predicting therapy efficacy, and tracking treatment resistance. As such, this review seeks to explore the clinicopathological impact of lncRNAs, immunotherapy, and DNA methylation in LUSC, emphasizing their exact roles in carcinogenesis and modes of action.

Comprehensive Genomic Discovery of Non-Coding Transcriptional Enhancers in the African Malaria Vector Anopheles coluzzii

Almost all regulation of gene expression in eukaryotic genomes is mediated by the action of distant non-coding transcriptional enhancers upon proximal gene promoters. Enhancer locations cannot be accurately predicted bioinformatically because of the absence of a defined sequence code, and thus functional assays are required for their direct detection. Here we used a massively parallel reporter assay, Self-Transcribing Active Regulatory Region sequencing (STARR-seq), to generate the first comprehensive genome-wide map of enhancers in Anopheles coluzzii, a major African malaria vector in the Gambiae species complex. The screen was carried out by transfecting reporter libraries created from the genomic DNA of 60 wild A. coluzzii from Burkina Faso into A. coluzzii 4a3A cells, in order to functionally query enhancer activity of the natural population within the homologous cellular context. We report a catalog of 3,288 active genomic enhancers that were significant across three biological replicates, 74% of them located in intergenic and intronic regions. The STARR-seq enhancer screen is chromatin-free and thus detects inherent activity of a comprehensive catalog of enhancers that may be restricted in vivo to specific cell types or developmental stages. Testing of a validation panel of enhancer candidates using manual luciferase assays confirmed enhancer function in 26 of 28 (93%) of the candidates over a wide dynamic range of activity from two to at least 16-fold activity above baseline. The enhancers occupy only 0.7% of the genome, and display distinct composition features. The enhancer compartment is significantly enriched for 15 transcription factor binding site signatures, and displays divergence for specific dinucleotide repeats, as compared to matched non-enhancer genomic controls. The genome-wide catalog of A. coluzzii enhancers is publicly available in a simple searchable graphic format. This enhancer catalogue will be valuable in linking genetic and phenotypic variation, in identifying regulatory elements that could be employed in vector manipulation, and in better targeting of chromosome editing to minimize extraneous regulation influences on the introduced sequences.

Importance: Understanding the role of the non-coding regulatory genome in complex disease phenotypes is essential, but even in well-characterized model organisms, identification of regulatory regions within the vast non-coding genome remains a challenge. We used a large-scale assay to generate a genome wide map of transcriptional enhancers. Such a catalogue for the important malaria vector, Anopheles coluzzii, will be an important research tool as the role of non-coding regulatory variation in differential susceptibility to malaria infection is explored and as a public resource for research on this important insect vector of disease.

Ferroptosis-Related Long Non-Coding RNA Signature Contributes to the Prediction of Prognosis Outcomes in Head and Neck Squamous Cell Carcinomas

Background: Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor, which makes the prognosis prediction challenging. Ferroptosis is an iron-dependent form of non-apoptotic regulated cell death, which could affect cancer development. However, the prognostic value of ferroptosis-related long non-coding RNA (lncRNA) in HNSCC is still limited.

Methods: In the current study, we employed the DESeq2 method to characterize the differentially expressed ferroptosis-related genes (FEGs) between cancer and normal samples. Next, the FEG-related lncRNAs (FElncRNAs) were identified using Spearman’s correlation analysis and multiple permutation hypotheses. Subsequently, LASSO and stepwise multivariate Cox regression analyses were undertaken to recognize the prognosis-related FElncRNA signature (PFLS) and risk scores.

Results: Herein, we first identified 60 dysregulated FEGs and their co-expressed FElncRNAs in HNSCC. Then, we recognized a set of six FElncRNAs PFLS (SLCO4A1-AS1, C1RL-AS1, PCED1B-AS1, HOXB-AS3, MIR9-3HG, and SFTA1P) for predicting patients’ prognostic risks and survival outcomes. We also assessed the efficiency of PFLS in the test set and an external validation cohort. Further parsing of the tumor immune microenvironment showed the PFLS was closely associated with immune cell infiltration abundances. Notably, the low-risk group of the PFLS showed a higher MHC score and cytolytic activity (CYT) score than the high-risk group, implying the low-risk group may have greater tumor surveillance and killing ability. In addition, we observed that the expression levels of two immune checkpoints (ICPs), i.e., programmed cell death protein 1 (PD-1) and programmed cell death 1 ligand 1 (PD-L1), showed significant associations with patients’ risk score, prompting the role of the PFLS in ICP blockade therapy. Finally, we also constructed a drug–PFLS network to reinforce the clinical utilities of the PFLS.

Conclusion: In summary, our study indicated that FElncRNAs played an important role in HNSCC survival prediction. Identification of PFLS will contribute to the development of novel anticancer therapeutic strategies.

Comprehensive analysis reveals common DNA methylation patterns of tobacco-associated cancers: A pan-cancer analysis

The role of tobacco in carcinogenesis has received increasing attention across a number of disciplines in recent years. Accumulating evidences reveal that tobacco consumption affects various epigenetic modifications, especially DNA methylation. However, the genetic modifications of methylation patterns involved in tobacco-attributable cancers remain poorly understood. In this manuscript, aberrant DNA methylation patterns were investigated in 9 tobacco-attributable cancers. Differential methylated probes (DMPs) were identified in each cancer type and a total of 2,392 hyper- and 736 hypomethylated pan-cancer DMPs (PDMPs) were screened out for further analysis. PDMP-associated genes were mostly enriched in metabolism-associated pathways, suggesting the potential roles of methylation alternation in reprogramming cancer cell metabolism. Hypomethylated PDMPs cg12422154, cg02772121 and cg06051311 constituted an enhancer region, significantly downregulating TRIM15, TRIM26 and RPP21, which serve as epigenetically therapeutic biomarkers. Forty-three hypermethylated and 13 hypomethylated transcription factor motifs were clustered into 6 groups, and exhibited various biological functions. Forty-nine PDMPs were reported to be associated with prognosis, providing effective tools to predict clinical outcomes. In summary, our studies revealed the characteristics, influences and potential mechanisms of DNA methylation patterns of tobacco-attributable cancer.

Physical exercise shapes the mouse brain epigenome

OBJECTIVE

To analyze the genome-wide epigenomic and transcriptomic changes induced by long term resistance or endurance training in the hippocampus of wild-type mice.

METHODS

We performed whole-genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq) of mice hippocampus after 4 weeks of specific training. In addition, we used a novel object recognition test before and after the intervention to determine whether the exercise led to an improvement in cognitive function.

RESULTS

Although the majority of DNA methylation changes identified in this study were training-model specific, most were associated with hypomethylation and were enriched in similar histone marks, chromatin states, and transcription factor biding sites. It is worth highlighting the significant association found between the loss of DNA methylation in Tet1 binding sites and gene expression changes, indicating the importance of these epigenomic changes in transcriptional regulation. However, endurance and resistance training activate different gene pathways, those being associated with neuroplasticity in the case of endurance exercise, and interferon response pathways in the case of resistance exercise, which also appears to be associated with improved learning and memory functions.

CONCLUSIONS

Our results help both understand the molecular mechanisms by which different exercise models exert beneficial effects for brain health and provide new potential therapeutic targets for future research.

Epigenetic DNA Modifications Upregulate SPRY2 in Human Colorectal Cancers

Conventional wisdom is that Sprouty2 (SPRY2), a suppressor of Receptor Tyrosine Kinase (RTK) signaling, functions as a tumor suppressor and is downregulated in many solid tumors. We reported, for the first time, that increased expression of SPRY2 augments cancer phenotype and Epithelial-Mesenchymal-Transition (EMT) in colorectal cancer (CRC). In this report, we assessed epigenetic DNA modifications that regulate SPRY2 expression in CRC. A total of 4 loci within SPRY2 were evaluated for 5mC using Combined Bisulfite Restriction Analysis (COBRA). Previously sequenced 5hmC nano-hmC seal data within SPRY2 promoter and gene body were evaluated in CRC. Combined bioinformatics analyses of SPRY2 CRC transcripts by RNA-seq/microarray and 450K methyl-array data archived in The Cancer Genome Atlas (TCGA) and GEO database were performed. SPRY2 protein in CRC tumors and cells was measured by Western blotting. Increased SPRY2 mRNA was observed across several CRC datasets and increased protein expression was observed among CRC patient samples. For the first time, SPRY2 hypomethylation was identified in adenocarcinomas in the promoter and gene body. We also revealed, for the first time, increases of 5hmC deposition in the promoter region of SPRY2 in CRC. SPRY2 promoter hypomethylation and increased 5hmC may play an influential role in upregulating SPRY2 in CRC.