Dependency of the Cancer-Specific Transcriptional Regulation Circuitry on the Promoter DNA Methylome

Methylation and transcriptome analyses construct a prognostic model and reveal the suppressor role of VMO1 in lung adenocarcinoma

BACKGROUND

DNA methylation is an important epigenetic mechanism of gene regulation. The aberrant DNA methylation has been found to play an important role in the initiation and progression of tumors.

RESULTS

Transcriptome and DNA methylation data of lung adenocarcinoma (LUAD) patients were co-analyzed and 95 methylation-driven genes (MDGs) was found in relation to LUAD. A prognostic model based on 3 MDGs (GMNN, SPINK2 and VMO1) was constructed by Univariate and Multivariate cox regression analyses. The risk score generated from the prognostic model could be used to classify LUAD patients into high and low risk groups. Furthermore, it was found that the risk score was associated with tumor microenvironment (TME) and clinical characteristics (survival status and T stage) of patients. Interestingly, we identified and validated that the patients in the low-risk group responded better to immunotherapy treatment. Then, a nomogram model based on the risk score and clinical characteristics was established which showed significant prediction value. The down-regulation and hypermethylation levels of vitelline membrane outer layer protein 1 homolog (VMO1) were verified in paired LUAD tumor and non-tumor tissues by pyrosequencing assay and RT-qPCR. Furthermore, MTT, migration and wound healing assays were performed with lentivector-mediated ectopic over-expression and 5-Aza-dC demethylation followed by siRNA rescue experiments to investigate the role of VMO1 in LUAD cells. Our results indicated that VMO1 could inhibit proliferation and migration of A549 and NCI-H1299 cells.

CONCLUSIONS

In summary, our experiments constructed a prognostic model with high capacity for risk prediction in LUAD patients. VMO1 had a malignant suppressor role in LUAD cells. The correlation between risk score and TME might elucidate a potential mechanism of oncogenesis and provide an avenue for further therapeutic targets.

Identification and validation of DNA methylation-driven gene PCDHB4 as a novel tumor suppressor for glioblastoma diagnosis and prognosis

Aberrant DNA methylation is a critical regulator of gene expression in the development and progression of glioblastoma (GBM). However, the impact of methylation-driven gene PCDHB4 changes on GBM occurrence and progression remains unclear. Therefore, this study aimed to identify the PCDHB4 gene for early diagnosis and prognostic evaluation and clarify its functional role in GBM. Methylation-driven gene PCDHB4 was selected for GBM using the multi-omics integration method based on publicly available data sets. The diagnostic capabilities of PCDHB4 methylation and 5-hydroxymethylcytosines were validated in tissue and blood cell-free DNA (cfDNA) samples, respectively. Combined survival analysis of PCDHB4 methylation and immune infiltration cells evaluated the prognostic predictive performance of GBM patients. We identified that the PCDHB4 gene achieved high discriminative capabilities for GBM and normal tissues with an area under the curve value of 0.941. PCDHB4 hypermethylation was observed in cfDNA blood samples from GBM patients. Compared with GBM patients with PCDHB4 hypermethylation level, patients with PCDHB4 hypomethylation level had significantly poorer overall survival (p = 0.035). In addition, GBM patients with PCDHB4 hypermethylation and high infiltration of CD4+ T cell activation level had a favorable survival (p = 0.026). Moreover, we demonstrated that mRNA expression of PCDHB4 was downregulated in GBM tissues and upregulated in GBM cell lines with PCDHB4 demethylation, and PCDHB4 overexpression inhibited GBM cell proliferation and migration. In summary, we discovered a novel methylation-driven gene PCDHB4 for the diagnosis and prognosis of GBM and demonstrated that PCDHB4 is a tumor suppressor in vitro experiments.

FAM72 family proteins as poor prognostic markers in clear cell renal carcinoma

Purpose

This study aimed to investigate the prognostic significance of the Family with Sequence Similarity 72 member (FAM72) gene family in clear cell renal carcinoma (ccRCC) using a bioinformatic approach.

Patients and methods

To investigate the association between FAM72 and ccRCC, we utilized various databases and analysis tools, including TCGA, GEPIA, Metscape, cBioPortal, and MethSurv. We conducted an analysis of FAM72 expression levels in ccRCC tissues compared to normal kidney tissues and performed univariate and multivariate Cox analysis to determine the relationship between FAM72 expression and patient prognosis. Furthermore, we carried out Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to identify enriched biological processes associated with FAM72 expression. Additionally, we analyzed immune cell infiltration and the level of methylation in ccRCC patients. Our bioinformatic analysis revealed that FAM72 expression levels were significantly higher in ccRCC tissues than in normal kidney tissues. High expression of FAM72 was associated with poor prognosis in ccRCC patients and was found to be an independent prognostic factor for ccRCC. GO and GSEA analyses indicated that FAM72 was enriched in biological processes related to mitosis, cell cycle, and DNA metabolism. Moreover, we found a significant correlation between FAM72 and immune cell infiltration and the level of methylation in ccRCC patients.

Conclusion

Our findings suggest that FAM72 could serve as an unfavorable prognostic molecular marker for ccRCC. A comprehensive understanding of FAM72 could provide crucial insights into tumor progression and prognosis.

Prospects and feasibility of synergistic therapy with radiotherapy, immunotherapy, and DNA methyltransferase inhibitors in non-small cell lung cancer

The morbidity and mortality of lung cancer are increasing, seriously threatening human health and life. Non-small cell lung cancer (NSCLC) has an insidious onset and is not easy to be diagnosed in its early stage. Distant metastasis often occurs and the prognosis is poor. Radiotherapy (RT) combined with immunotherapy, especially with immune checkpoint inhibitors (ICIs), has become the focus of research in NSCLC. The efficacy of immunoradiotherapy (iRT) is promising, but further optimization is necessary. DNA methylation has been involved in immune escape and radioresistance, and becomes a game changer in iRT. In this review, we focused on the regulation of DNA methylation on ICIs treatment resistance and radioresistance in NSCLC and elucidated the potential synergistic effects of DNA methyltransferases inhibitors (DNMTis) with iRT. Taken together, we outlined evidence suggesting that a combination of DNMTis, RT, and immunotherapy could be a promising treatment strategy to improve NSCLC outcomes.

Four methylation-driven genes detected by linear discriminant analysis model from early-stage colorectal cancer and their methylation levels in cell-free DNA

The process of colorectal cancer (CRC) formation is considered a typical model of multistage carcinogenesis in which aberrant DNA methylation plays an important role. In this study, 752 methylation-driven genes (MDGs) were identified by the MethylMix package based on methylation and gene expression data of CRC in The Cancer Genome Atlas (TCGA). Iterative recursive feature elimination (iRFE) based on linear discriminant analysis (LDA) was used to determine the minimum MDGs (iRFE MDGs), which could distinguish between cancer and cancer-adjacent tissues. Further analysis indicated that the changes in methylation levels of the four iRFE MDGs, ADHFE1-Cluster1, CNRIP1-Cluster1, MAFB, and TNS4, occurred in adenoma tissues, while changes did not occur until stage IV in cell-free DNA. Furthermore, the methylation levels of iRFE MDGs were correlated with the genes involved in the reprogramming process of somatic cells to pluripotent stem cells, which is considered the common signature of cancer cells and embryonic stem cells. The above results indicated that the four iRFE MDGs may play roles in the early stage of colorectal carcinogenesis and highlighted the complicated relationship between tissue DNA and cell-free DNA (cfDNA).

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.

DNA methylation in lung cancer patients: Opening a "window of life" under precision medicine

DNA methylation is a work of adding a methyl group to the 5th carbon atom of cytosine in DNA sequence under the catalysis of DNA methyltransferase (DNMT) to produce 5-methyl cytosine. Some current studies have elucidated the mechanism of lung cancer occurrence and causes of lung cancer progression and metastasis from the perspective of DNA methylation. Moreover, many studies have shown that smoking can change the methylation status of some gene loci, leading to the occurrence of lung cancer, especially central lung cancer. This review mainly introduces the role of DNA methylation in the pathogenesis, early diagnosis and screening, progression and metastasis, treatment, and prognosis of lung cancer, as well as the latest progress. We point out that methylation markers, sample tests, and methylation detection limit the clinical application of DNA methylation. If the liquid biopsy is to become the main force in lung cancer diagnosis, it must make efficient use of limited samples and improve the sensitivity and specificity of the tests. In addition, we also put forward our views on the future development direction of DNA methylation.

Integrative Analysis of Identifying Methylation-Driven Genes Signature Predicts Prognosis in Colorectal Carcinoma

Background

Aberrant DNA methylation is a critical regulator of gene expression and plays a crucial role in the occurrence, progression, and prognosis of colorectal cancer (CRC). We aimed to identify methylation-driven genes by integrative epigenetic and transcriptomic analysis to predict the prognosis of CRC patients.

Methods

Methylation-driven genes were selected for CRC using a MethylMix algorithm and LASSO regression screening strategy, and were further used to construct a prognostic risk-assessment model. The Cancer Genome Atlas (TCGA) database was obtained as the training set for both the screening of methylation-driven genes and the effect of genes signature on CRC prognosis. Then, the prognostic genes signature was validated in three independent expression arrays of CRC data from Gene Expression Omnibus (GEO).

Results

We identified 143 methylation-driven genes, of which the combination of BATF, PHYHIPL, RBP1, and PNPLA4 expression levels was screened as a better prognostic model with the best area under the curve (AUC) (AUC = 0.876). Compared with patients in the low-risk group, CRC patients in the high-risk group had significantly poorer overall survival in the training set (HR = 2.184, 95% CI: 1.404–3.396, P < 0.001). Similar results were observed in the validation set. Moreover, VanderWeele’s mediation analysis indicated that the effect of methylation on prognosis was mediated by the levels of their expression (HR_indirect = 1.473, P = 0.001, Proportion mediated, 69.10%).

Conclusions

We identified a four-gene prognostic signature by integrative analysis and developed a risk-assessment model that is significantly associated with patients’ survival. Methylation-driven genes might be a potential prognostic signature for CRC patients.

Mutual dependency between lncRNA LETN and protein NPM1 in controlling the nucleolar structure and functions sustaining cell proliferation

Fundamental processes such as ribosomal RNA synthesis and chromatin remodeling take place in the nucleolus, which is hyperactive in fast-proliferating cells. The sophisticated regulatory mechanism underlying the dynamic nucleolar structure and functions is yet to be fully explored. The present study uncovers the mutual functional dependency between a previously uncharacterized human long non-coding RNA, which we renamed LETN, and a key nucleolar protein, NPM1. Specifically, being upregulated in multiple types of cancer, LETN resides in the nucleolus via direct binding with NPM1. LETN plays a critical role in facilitating the formation of NPM1 pentamers, which are essential building blocks of the nucleolar granular component and control the nucleolar functions. Repression of LETN or NPM1 led to similar and profound changes of the nucleolar morphology and arrest of the nucleolar functions, which led to proliferation inhibition of human cancer cells and neural progenitor cells. Interestingly, this inter-dependency between LETN and NPM1 is associated with the evolutionarily new variations of NPM1 and the coincidental emergence of LETN in higher primates. We propose that this human-specific protein-lncRNA axis renders an additional yet critical layer of regulation with high physiological relevance in both cancerous and normal developmental processes that require hyperactive nucleoli.

Pan-cancer characterization of long non-coding RNA and DNA methylation mediated transcriptional dysregulation

BACKGROUND

Disruption of DNA methylation (DNAm) is one of the key signatures of cancer, however, detailed mechanisms that alter the DNA methylome in cancer remain to be elucidated.

METHODS

Here we present a novel integrative analysis framework, called MeLncTRN (Methylation mediated LncRNA Transcriptional Regulatory Network), that integrates genome-wide transcriptome, DNA methylome and copy number variation profiles, to systematically identify the epigenetically-driven lncRNA-gene regulation circuits across 18 cancer types.

FINDING

We show that a significant fraction of the aberrant DNAm and gene expression landscape in cancer is associated with long noncoding RNAs (lncRNAs). We reveal distinct types of regulation between lncRNA modulators and target genes that are operative in either only specific cancers or across cancers. Functional studies identified a common theme of cancer hallmarks that lncRNA modulators may participate in. The coupled lncRNA gene interactions via DNAm also serve as markers for classifications of cancer subtypes with different prognoses.

INTERPRETATION

Our study reveals a vital layer of DNAm and associated expression regulation for many cancer-related genes and we also provide a valuable database resource for interrogating epigenetically mediated lncRNA-gene interactions in cancer.

FUNDING

National Natural Science Foundation of China [91959106, 31871255].

Development and validation of 3-CpG methylation prognostic signature based on different survival indicators for colorectal cancer

Abnormal DNA methylation is considered a vital hallmark to regulate gene expression and influence the development and progression of colorectal cancer (CRC). Although CRC-related methylation prognostic models have been developed, their clinical application is limited due to the lack of external validation and extension to other survival evaluation indicators. Therefore, this study aimed to develop and validate novel methylation prognostic models correlated with different survival indicators for individualized prognosis prediction for CRC patients. The prognostic-related CpG sites of methylation-driven genes screened by the MethylMix algorithm were identified and validated in The Cancer Genome Atlas (TCGA) CRC methylation data and our methylation data. The prognostic models correlated with different survival evaluation indicators (overall survival [OS] and disease-free survival [DFS]) were developed and validated in the TCGA CRC dataset (N = 376) and our independent CRC dataset (N = 227). We utilized the combination of selected 3-CpG methylation sites in three genes (DAPP1, FAM3D, and PIGR) to construct a prognostic risk-score model. In the training dataset, Kaplan-Meier survival analysis demonstrated that high-risk patients had significantly poorer survival than low-risk patients (p_OS  = .0014; p_DFS  < .001). Then, the 3-CpG methylation signature was successfully validated as an independent predictor in the testing data set (p_OS  = .016; p_DFS  = .016). A prognostic nomogram was constructed and validated. Additionally, mediation analysis revealed the direct effect of the methylation signature on CRC prognosis (p_OS  = 9.149e-06; p_DFS  = .001). In summary, our study revealed that the 3-CpG methylation signature might be a potential prognostic indicator to facilitate individualized survival prediction for CRC patients.

An Information Theoretical Multilayer Network Approach to Breast Cancer Transcriptional Regulation

Breast cancer is a complex, highly heterogeneous disease at multiple levels ranging from its genetic origins and molecular processes to clinical manifestations. This heterogeneity has given rise to the so-called intrinsic or molecular breast cancer subtypes. Aside from classification, these subtypes have set a basis for differential prognosis and treatment. Multiple regulatory mechanisms-involving a variety of biomolecular entities-suffer from alterations leading to the diseased phenotypes. Information theoretical approaches have been found to be useful in the description of these complex regulatory programs. In this work, we identified the interactions occurring between three main mechanisms of regulation of the gene expression program: transcription factor regulation, regulation via noncoding RNA, and epigenetic regulation through DNA methylation. Using data from The Cancer Genome Atlas, we inferred probabilistic multilayer networks, identifying key regulatory circuits able to (partially) explain the alterations that lead from a healthy phenotype to different manifestations of breast cancer, as captured by its molecular subtype classification. We also found some general trends in the topology of the multi-omic regulatory networks: Tumor subtype networks present longer shortest paths than their normal tissue counterpart; epigenomic regulation has frequently focused on genes enriched for certain biological processes; CpG methylation and miRNA interactions are often part of a regulatory core of conserved interactions. The use of probabilistic measures to infer information regarding theoretical-derived multilayer networks based on multi-omic high-throughput data is hence presented as a useful methodological approach to capture some of the molecular heterogeneity behind regulatory phenomena in breast cancer, and potentially other diseases.

Drug Response Associated With and Prognostic lncRNAs Mediated by DNA Methylation and Transcription Factors in Colon Cancer

Colon cancer is the most commonly diagnosed malignancy and the leading cause of cancer deaths worldwide. As well as lifestyle, genetic and epigenetic changes are key factors that influence the risk of colon cancer. However, the impact of epigenetic alterations in non-coding RNAs and their consequences in colon cancer have not been fully characterized. We detected differential methylation sites (DMSs) in long non-coding RNA (lncRNA) promoters and identified lncRNA expression quantitative trait methylations (lncQTMs) by association tests. To investigate how transcription factor (TF) binding was affected by DNA methylation, we characterized the occurrence of known TFs among DMSs collected from the MEME suite. We further combined methylome and transcriptome data to construct TF-methylation-lncRNA relationships. To study the role of lncRNAs in drug response, we used pharmacological and lncRNA profiles from the Cancer Cell Line Encyclopedia (CCLE) and investigated the association between lncRNAs and drug activity. We also used combinations of TF-methylation-lncRNA relationships to stratify patient survival using a risk model. DNA methylation sites displayed global hyper-methylation in lncRNA promoters and tended to have negative relationships with the corresponding lncRNAs. Negative lncQTMs located near transcription start sites (TSSs) had more significant correlations with the corresponding lncRNAs. Some lncRNAs found to be mediated by the interplay between DNA methylation and TFs were previously identified as markers for colon cancer. We also found that the ELF1-cg05372727- LINC00460 relationship were prognostic signatures for colon cancer. These findings suggest that lncRNAs mediated by the interplay between DNA methylation and TFs are promising predictors of drug response, and that combined TF-methylation-lncRNA can serve as a prognostic signature for colon cancer.

Interleukin-6-mediated epigenetic control of the VEGFR2 gene induces disorganized angiogenesis in human breast tumors

Disorganized vessels in the tumor vasculature lead to impaired perfusion, resulting in reduced accessibility to immune cells and chemotherapeutic drugs. In the breast tumor-stroma interplay, paracrine factors such as interleukin-6 (IL-6) often facilitate disordered angiogenesis. We show here that epigenetic mechanisms regulate the crosstalk between IL-6 and vascular endothelial growth factor receptor 2 (VEGFR2) signaling pathways in myoepithelial (CD10+) and endothelial (CD31+, CD105+, CD146+, and CD133-) cells isolated from malignant and nonmalignant tissues of clinically characterized human breast tumors. Tumor endothelial (Endo-T) cells in 3D cultures exhibited higher VEGFR2 expression levels, accelerated migration, invasion, and disorganized sprout formation in response to elevated IL-6 levels secreted by tumor myoepithelial (Epi-T) cells. Constitutively, compared with normal endothelial (Endo-N) cells, Endo-T cells differentially expressed DNA methyltransferase isoforms and had increased levels of IL-6 signaling intermediates such as IL-6R and signal transducer and activator of transcription 3 (STAT3). Upon IL-6 treatment, Endo-N and Endo-T cells displayed altered expression of the DNA methyltransferase 1 (DNMT1) isoform. Mechanistic studies revealed that IL-6 induced proteasomal degradation of DNMT1, but not of DNMT3A and DNMT3B and subsequently led to promoter hypomethylation and expression/activation of VEGFR2. IL-6-induced VEGFR2 up-regulation was inhibited by overexpression of DNMT1. Transfection of a dominant-negative STAT3 mutant, but not of STAT1, abrogated VEGFR2 expression. Our results indicate that in the breast tumor microenvironment, IL-6 secreted from myoepithelial cells influences DNMT1 stability, induces the expression of VEGFR2 in endothelial cells via a promoter methylation-dependent mechanism, and leads to disordered angiogenesis.

The Impact of CRISPR-Cas9 on Age-related Disorders: From Pathology to Therapy

With advances in medical technology, the number of people over the age of 60 is on the rise, and thus, increasing the prevalence of age-related pathologies within the aging population. Neurodegenerative disorders, cancers, metabolic and inflammatory diseases are some of the most prevalent age-related pathologies affecting the growing population. It is imperative that a new treatment to combat these pathologies be developed. Although, still in its infancy, the CRISPR-Cas9 system has become a potent gene-editing tool capable of correcting gene-mediated age-related pathology, and therefore ameliorating or eliminating disease symptoms. Deleting target genes using the CRISPR-Cas9 system or correcting for gene mutations may ameliorate many different neurodegenerative disorders detected in the aging population. Cancer cells targeted by the CRISPR-Cas9 system may result in an increased sensitivity to chemotherapeutics, lower proliferation, and higher cancer cell death. Finally, reducing gene targeting inflammatory molecules production through microRNA knockout holds promise as a therapeutic strategy for both arthritis and inflammation. Here we present a review based on how the expanding world of genome editing can be applied to disorders and diseases affecting the aging population.

Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection

Whereas nonlinear relationships between genes are acknowledged, there exist only a few methods for estimating nonlinear gene coexpression networks or gene regulatory networks (GCNs/GRNs) with common deficiencies. These methods often consider only pairwise associations between genes, and are, therefore, poorly capable of identifying higher-order regulatory patterns when multiple genes should be considered simultaneously. Another critical issue in current nonlinear GCN/GRN estimation approaches is that they consider linear and nonlinear dependencies at the same time in confounded form nonparametrically. This severely undermines the possibilities for nonlinear associations to be found, since the power of detecting nonlinear dependencies is lower compared to linear dependencies, and the sparsity-inducing procedures might favor linear relationships over nonlinear ones only due to small sample sizes. In this paper, we propose a method to estimate undirected nonlinear GCNs independently from the linear associations between genes based on a novel semiparametric neighborhood selection procedure capable of identifying complex nonlinear associations between genes. Simulation studies using the common DREAM3 and DREAM9 datasets show that the proposed method compares superiorly to the current nonlinear GCN/GRN estimation methods.

Complex impact of DNA methylation on transcriptional dysregulation across 22 human cancer types

Accumulating evidence has demonstrated that transcriptional regulation is affected by DNA methylation. Understanding the perturbation of DNA methylation-mediated regulation between transcriptional factors (TFs) and targets is crucial for human diseases. However, the global landscape of DNA methylation-mediated transcriptional dysregulation (DMTD) across cancers has not been portrayed. Here, we systematically identified DMTD by integrative analysis of transcriptome, methylome and regulatome across 22 human cancer types. Our results revealed that transcriptional regulation was affected by DNA methylation, involving hundreds of methylation-sensitive TFs (MethTFs). In addition, pan-cancer MethTFs, the regulatory activity of which is generally affected by DNA methylation across cancers, exhibit dominant functional characteristics and regulate several cancer hallmarks. Moreover, pan-cancer MethTFs were found to be affected by DNA methylation in a complex pattern. Finally, we investigated the cooperation among MethTFs and identified a network module that consisted of 43 MethTFs with prognostic potential. In summary, we systematically dissected the transcriptional dysregulation mediated by DNA methylation across cancer types, and our results provide a valuable resource for both epigenetic and transcriptional regulation communities.