DNA methylation status is more reliable than gene expression at detecting cancer in prostate biopsy

Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

Cuproptosis-related gene-located DNA methylation in lower-grade glioma: Prognosis and tumor microenvironment

Cuproptosis a novel copper-dependent cell death modality, plays a crucial part in the oncogenesis, progression and prognosis of tumors. However, the relationships among DNA-methylation located in cuproptosis-related genes (CRGs), overall survival (OS) and the tumor microenvironment remain undefined. In this study, we systematically assessed the prognostic value of CRG-located DNA-methylation for lower-grade glioma (LGG). Clinical and molecular data were sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We employed Cox hazard regression to examine the associations between CRG-located DNA-methylation and OS, leading to the development of a prognostic signature. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses were utilized to gauge the accuracy of the signature. Gene Set Enrichment Analysis (GSEA) was applied to uncover potential biological functions of differentially expressed genes between high- and low-risk groups. A three CRG-located DNA-methylation prognostic signature was established based on TCGA database and validated in GEO dataset. The 1-year, 3-year, and 5-year area under the curve (AUC) of ROC curves in the TCGA dataset were 0.884, 0.888, and 0.859 while those in the GEO dataset were 0.943, 0.761 and 0.725, respectively. Cox-regression-analyses revealed the risk signature as an independent risk factor for LGG patients. Immunogenomic profiling suggested that the signature was associated with immune infiltration level and immune checkpoints. Functional enrichment analysis indicated differential enrichment in cell differentiation in the hindbrain, ECM receptor interactions, glycolysis and reactive oxygen species pathway across different groups. We developed and verified a novel CRG-located DNA-methylation signature to predict the prognosis in LGG patients. Our findings emphasize the potential clinical implications of CRG-located DNA-methylation indicating that it may serve as a promising therapeutic target for LGG patients.

Decoding the Influence of Obesity on Prostate Cancer and Its Transgenerational Impact

In recent decades, the escalating prevalence of metabolic disorders, notably obesity and being overweight, has emerged as a pressing concern in public health. Projections for the future indicate a continual upward trajectory in obesity rates, primarily attributable to unhealthy dietary patterns and sedentary lifestyles. The ramifications of obesity extend beyond its visible manifestations, intricately weaving a web of hormonal dysregulation, chronic inflammation, and oxidative stress. This nexus of factors holds particular significance in the context of carcinogenesis, notably in the case of prostate cancer (PCa), which is a pervasive malignancy and a leading cause of mortality among men. A compelling hypothesis arises from the perspective of transgenerational inheritance, wherein genetic and epigenetic imprints associated with obesity may wield influence over the development of PCa. This review proposes a comprehensive exploration of the nuanced mechanisms through which obesity disrupts prostate homeostasis and serves as a catalyst for PCa initiation. Additionally, it delves into the intriguing interplay between the transgenerational transmission of both obesity-related traits and the predisposition to PCa. Drawing insights from a spectrum of sources, ranging from in vitro and animal model research to human studies, this review endeavors to discuss the intricate connections between obesity and PCa. However, the landscape remains partially obscured as the current state of knowledge unveils only fragments of the complex mechanisms linking these phenomena. As research advances, unraveling the associated factors and underlying mechanisms promises to unveil novel avenues for understanding and potentially mitigating the nexus between obesity and the development of PCa.

Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Introduction: Association rule mining (ARM) is a powerful tool for exploring the informative relationships among multiple items (genes) in any dataset. The main problem of ARM is that it generates many rules containing different rule-informative values, which becomes a challenge for the user to choose the effective rules. In addition, few works have been performed on the integration of multiple biological datasets and variable cutoff values in ARM. Methods: To solve all these problems, in this article, we developed a novel framework MOOVARM (multi-objective optimized variable cutoff-based association rule mining) for multi-omics profiles. Results: In this regard, we identified the positive ideal solution (PIS), which maximized the profit and minimized the loss, and negative ideal solution (NIS), which minimized the profit and maximized the loss for all gene sets (item sets), belonging to each extracted rule. Thereafter, we computed the distance (d +) from PIS and distance (d -) from NIS for each gene set or product. These two distances played an important role in determining the optimized associations among various pairs of genes in the multi-omics dataset. We then globally estimated the relative closeness to PIS for ranking the gene sets. When the relative closeness score of the rule is greater than or equal to the pre-defined threshold value, the rule can be considered a final resultant rule. Moreover, MOOVARM evaluated the relative score of the rule based on the status of all genes instead of individual genes. Conclusions: MOOVARM produced the final rank of the extracted (multi-objective optimized) rules of correlated genes which had better disease classification than the state-of-the-art algorithms on gene signature identification.

Altered DNA methylation and gene expression predict disease severity in patients with Aicardi-Goutières syndrome

Aicardi-Goutières Syndrome (AGS) is a rare neuro-inflammatory disease characterized by increased expression of interferon-stimulated genes (ISGs). Disease-causing mutations are present in genes associated with innate antiviral responses. Disease presentation and severity vary, even between patients with identical mutations from the same family. This study investigated DNA methylation signatures in PBMCs to understand phenotypic heterogeneity in AGS patients with mutations in RNASEH2B. AGS patients presented hypomethylation of ISGs and differential methylation patterns (DMPs) in genes involved in "neutrophil and platelet activation". Patients with "mild" phenotypes exhibited DMPs in genes involved in "DNA damage and repair", whereas patients with "severe" phenotypes had DMPs in "cell fate commitment" and "organ development" associated genes. DMPs in two ISGs (IFI44L, RSAD2) associated with increased gene expression in patients with "severe" when compared to "mild" phenotypes. In conclusion, altered DNA methylation and ISG expression as biomarkers and potential future treatment targets in AGS.

Risperidone response in patients with schizophrenia drives DNA methylation changes in immune and neuronal systems

Background: The choice of efficient antipsychotic therapy for schizophrenia relies on a time-consuming trial-and-error approach, whereas the social and economic burdens of the disease call for faster alternatives. Material & methods: In a search for predictive biomarkers of antipsychotic response, blood methylomes of 28 patients were analyzed before and 4 weeks into risperidone therapy. Results: Several CpGs exhibiting response-specific temporal dynamics were identified in otherwise temporally stable methylomes and noticeable global response-related differences were observed between good and bad responders. These were associated with genes involved in immunity, neurotransmission and neuronal development. Polymorphisms in many of these genes were previously linked with schizophrenia etiology and antipsychotic response. Conclusion: Antipsychotic response seems to be shaped by both stable and medication-induced methylation differences.

Genetic and epigenetic signatures associated with plasma oxytocin levels in children and adolescents with autism spectrum disorder

Oxytocin (OT), the brain's most abundant neuropeptide, plays an important role in social salience and motivation. Clinical trials of the efficacy of OT in autism spectrum disorder (ASD) have reported mixed results due in part to ASD's complex etiology. We investigated whether genetic and epigenetic variation contribute to variable endogenous OT levels that modulate sensitivity to OT therapy. To carry out this analysis, we integrated genome-wide profiles of DNA-methylation, transcriptional activity, and genetic variation with plasma OT levels in 290 participants with ASD enrolled in a randomized controlled trial of OT. Our analysis identified genetic variants with novel association with plasma OT, several of which reside in known ASD risk genes. We also show subtle but statistically significant association of plasma OT levels with peripheral transcriptional activity and DNA-methylation profiles across several annotated gene sets. These findings broaden our understanding of the effects of the peripheral oxytocin system and provide novel genetic candidates for future studies to decode the complex etiology of ASD and its interaction with OT signaling and OT-based interventions. LAY SUMMARY: Oxytocin (OT) is an abundant chemical produced by neurons that plays an important role in social interaction and motivation. We investigated whether genetic and epigenetic factors contribute to variable OT levels in the blood. To this, we integrated genetic, gene expression, and non-DNA regulated (epigenetic) signatures with blood OT levels in 290 participants with autism enrolled in an OT clinical trial. We identified genetic association with plasma OT, several of which reside in known autism risk genes. We also show statistically significant association of plasma OT levels with gene expression and epigenetic across several gene pathways. These findings broaden our understanding of the factors that influence OT levels in the blood for future studies to decode the complex presentation of autism and its interaction with OT and OT-based treatment.

Cross-tissue analysis of blood and brain epigenome-wide association studies in Alzheimer's disease

To better understand DNA methylation in Alzheimer's disease (AD) from both mechanistic and biomarker perspectives, we performed an epigenome-wide meta-analysis of blood DNA methylation in two large independent blood-based studies in AD, the ADNI and AIBL studies, and identified 5 CpGs, mapped to the SPIDR, CDH6 genes, and intergenic regions, that are significantly associated with AD diagnosis. A cross-tissue analysis that combined these blood DNA methylation datasets with four brain methylation datasets prioritized 97 CpGs and 10 genomic regions that are significantly associated with both AD neuropathology and AD diagnosis. An out-of-sample validation using the AddNeuroMed dataset showed the best performing logistic regression model includes age, sex, immune cell type proportions, and methylation risk score based on prioritized CpGs in cross-tissue analysis (AUC = 0.696, 95% CI: 0.616 - 0.770, P-value = 2.78 × 10^-5). Our study offers new insights into epigenetics in AD and provides a valuable resource for future AD biomarker discovery.

Alternations of gene expression in PI3K and AR pathways and DNA methylation features contribute to metastasis of prostate cancer

OBJECTIVE

The molecular heterogeneity of prostate cancer (PCa) gives rise to distinct tumor subclasses based on epigenetic modification and gene expression signatures. Identification of clinically actionable molecular subtypes of PCa is key to improving patient outcome, and the balance between specific pathways may influence PCa outcome. It is also urgent to identify progression-related markers through cytosine-guanine (CpG) methylation in predicting metastasis for patients with PCa.

METHODS

We performed bioinformatics analysis of transcriptomic, and clinical data in an integrated cohort of 551 prostate samples. The datasets included retrospective The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs.

RESULTS

We found that PCa progression is more likely to occur after the third year through conditional survival (CS) analysis, and prostate-specific antigen (PSA) was a better predictor of Progression-free survival (PFS) than Gleason score (GS). Our study first demonstrated that PCa tumors have distinct expression profiles based on the expression of genes involved in androgen receptor (AR) and PI3K-AKT, which influence disease outcome. Our results also indicated that there are multiple phenotypes relevant to the AR-PI3K axis in PCa, where tumors with mixed phenotype may be more aggressive or have worse outcome than quiescent phenotype. In terms of epigenetics, we obtained CpG sites and their corresponding genes which have a good predictive value of PFS. However, various evidences showed that the predictive value of CpGs corresponding genes was much lower than GpG sites in Overall survival (OS) and PFS.

CONCLUSIONS

PCa classification specific to AR and PI3K pathways provides novel biological insight into previously established PCa subtypes and may help develop personalized therapies. Our results support the potential clinical utility of DNA methylation signatures to distinguish tumor metastasis and to predict prognosis and outcomes.

DNA Methylation-Specific Analysis of G Protein-Coupled Receptor-Related Genes in Pan-Cancer

Tumor heterogeneity presents challenges for personalized diagnosis and treatment of cancer. The identification method of cancer-specific biomarkers has important applications for the diagnosis and treatment of cancer types. In this study, we analyzed the pan-cancer DNA methylation data from TCGA and GEO, and proposed a computational method to quantify the degree of specificity based on the level of DNA methylation of G protein-coupled receptor-related genes (GPCRs-related genes) and to identify specific GPCRs DNA methylation biomarkers (GRSDMs) in pan-cancer. Then, a ridge regression-based method was used to discover potential drugs through predicting the drug sensitivities of cancer samples. Finally, we predicted and verified 8 GRSDMs in adrenocortical carcinoma (ACC), rectum adenocarcinoma (READ), uveal Melanoma (UVM), thyroid carcinoma (THCA), and predicted 4 GRSDMs (F2RL3, DGKB, GRK5, PIK3R6) which were sensitive to 12 potential drugs. Our research provided a novel approach for the personalized diagnosis of cancer and informed individualized treatment decisions.

Blood-based liquid biopsies for prostate cancer: clinical opportunities and challenges

Liquid biopsy has been established as a powerful, minimally invasive, tool to detect clinically actionable aberrations across numerous cancer types in real-time. With the development of new therapeutic agents in prostate cancer (PC) including DNA repair targeted therapies, this is especially attractive. However, there is unclarity on how best to screen for PC, improve risk stratification and ultimately how to treat advanced disease. Therefore, there is an urgent need to develop better biomarkers to help guide oncologists' decisions in these settings. Circulating tumour cells (CTCs), exosomes and cell-free DNA/RNA (cfDNA/cfRNA) analysis, including epigenetic features such as methylation, have all shown potential in prognostication, treatment response assessment and detection of emerging mechanisms of resistance. However, there are still challenges to overcome prior to implementing liquid biopsies in routine clinical practice such as preanalytical considerations including blood collection and storage, the cost of CTC isolation and enrichment, low-circulating tumour content as a limitation for genomic analysis and how to better interpret the sequencing data generated. In this review, we describe an overview of the up-to-date clinical opportunities in the management of PC through blood-based liquid biopsies and the next steps for its implementation in personalised treatment guidance.

Identification of Differential Genes of DNA Methylation Associated With Alzheimer's Disease Based on Integrated Bioinformatics and Its Diagnostic Significance

Background

Alzheimer's disease (AD) is a common neurodegenerative disease. The pathogenesis is complex and has not been clearly elucidated, and there is no effective treatment. Recent studies have demonstrated that DNA methylation is closely associated with the pathogenesis of AD, which sheds light on investigating potential biomarkers for the diagnosis of early AD and related possible therapeutic approaches.

Methods

Alzheimer's disease patients samples and healthy controls samples were collected from two datasets in the GEO database. Using LIMMA software package in R language to find differentially expressed genes (DEGs). Afterward, DEGs have been subjected to enrichment analysis of GO and KEGG pathways. The PPI networks and Hub genes were created and visualized based on the STRING database and Cytoscape. ROC curves were further constructed to analyze the accuracy of these genes for AD diagnosis.

Results

Analysis of the GSE109887 and GSE97760 datasets showed 477 significant DEGs. GO and KEGG enrichment analysis showed terms related to biological processes related to these genes. The top ten Hub genes were found on the basis of the PPI network using the CytoHubba plugin, and the AUC areas of these top ranked genes were all greater than 0.7, showing satisfactory diagnostic accuracy.

Conclusion

The study identified the top 10 Hub genes associated with AD-related DNA methylation, of which RPSA, RPS23, and RPLP0 have high diagnostic accuracy and excellent AD biomarker potential.

Early fish domestication affects methylation of key genes involved in the rapid onset of the farmed phenotype

Animal domestication is a process of environmental modulation and artificial selection leading to permanent phenotypic modifications. Recent studies showed that phenotypic changes occur very early in domestication, i.e., within the first generation in captivity, which raises the hypothesis that epigenetic mechanisms may play a critical role on the early onset of the domestic phenotype. In this context, we applied reduced representation bisulphite sequencing to compare methylation profiles between wild Nile tilapia females and their offspring reared under farmed conditions. Approximately 700 differentially methylated CpG sites were found, many of them associated not only with genes involved in muscle growth, immunity, autophagy and diet response but also related to epigenetic mechanisms, such as RNA methylation and histone modifications. This bottom-up approach showed that the phenotypic traits often related to domestic animals (e.g., higher growth rate and different immune status) may be regulated epigenetically and prior to artificial selection on gene sequences. Moreover, it revealed the importance of diet in this process, as reflected by differential methylation patterns in genes critical to fat metabolism. Finally, our study highlighted that the TGF-β1 signalling pathway may regulate and be regulated by several differentially methylated CpG-associated genes. This could be an important and multifunctional component in promoting adaptation of fish to a domestic environment while modulating growth and immunity-related traits.

Genome-wide methylomic analyses identify prognostic epigenetic signature in lower grade glioma

Glioma is the most malignant and aggressive type of brain tumour with high heterogeneity and mortality. Although some clinicopathological factors have been identified as prognostic biomarkers, the individual variants and risk stratification in patients with lower grade glioma (LGG) have not been fully elucidated. The primary aim of this study was to identify an efficient DNA methylation combination biomarker for risk stratification and prognosis in LGG. We conducted a retrospective cohort study by analysing whole genome DNA methylation data of 646 patients with LGG from the TCGA and GEO database. Cox proportional hazard analysis was carried out to screen and construct biomarker model that predicted overall survival (OS). The Kaplan‐Meier survival curves and time‐dependent ROC were constructed to prove the efficiency of the signature. Then, another independent cohort was used to further validate the finding. A two‐CpG site DNA methylation signature was identified by multivariate Cox proportional hazard analysis. Further analysis indicated that the signature was an independent survival predictor from other clinical factors and exhibited higher predictive accuracy compared with known biomarkers. This signature was significantly correlated with immune‐checkpoint blockade, immunotherapy‐related signatures and ferroptosis regulator genes. The expression pattern and functional analysis showed that these two genes corresponding with two methylation sites contained in the model were correlated with immune infiltration level, and involved in MAPK and Rap1 signalling pathway. The signature may contribute to improve the risk stratification of patients and provide a more accurate assessment for precision medicine in the clinic.

Detection of MSH2 Gene Methylation in Extramammary Paget's Disease by Methylation-Sensitive High-Resolution Melting Analysis

Background

Extramammary Paget's disease (EMPD) is a rare skin tumor. Hypermethylation in the MSH2 promoter resulting in the downregulation of its protein expression shows a high detection rate in EMPD tumor tissue, which indicates that the methylation of MSH2 may play an important role in the pathogenesis of EMPD.

Objective

This study aims to establish a rapid analysis strategy based on the methylation-sensitive high-resolution melting curve (MS-HRM) to detect the methylation level of the MSH2 promoter.

Methods

With the use of universal methylated human DNA products, we established the MS-HRM standard curve to quantitatively detect the methylation level of the MSH2 promoter. Then, all 57 EMPD tumor DNA samples were analyzed. Pyrosequencing assay was also carried out to test the accuracy and efficacy of MS-HRM. Besides, a total of 54 human normal and other cancerous tissues were included in this study to test the reliability and versatility of the MS-HRM standard curve.

Results

In this study, by using the established MS-HRM, we found that 96.5% (55/57) EMPD tumor samples had varying methylation levels in the MSH2 promoter ranging from 0% to 30%. Then, the methylation data were compared to the results obtained from pyrosequencing, which showed a high correlation between these two techniques by Pearson's correlation (r = 0.9425) and Bland–Altman plots (mean difference = −0.1069) indicating that the methylation levels analyzed by MS-HRM were consistent with DNA pyrosequencing. Furthermore, in 23 normal and 31 other cancerous tissue samples, there were two colorectal cancer (CRC) tissues that tested MSH2 methylation positive (1% and 5%) which confirmed that our established MS-HRM can be widely applied to various types of samples.

Conclusion

MS-HRM standard curve can be used for the detection of the methylation level of MSH2 in EMPD tumor samples and other cancerous tissues potentially, which presents a promising candidate as a quantitative assay to analyze MSH2 promoter methylation in routine pathological procedure.

DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis

Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

DNMT3A epigenetically regulates key microRNAs involved in epithelial-to-mesenchymal transition in prostate cancer

Epithelial-to-Mesenchymal Transition (EMT) is involved in prostate cancer metastatic progression, and its plasticity suggests epigenetic implications. Deregulation of DNMTs and several miRNAs plays a relevant role in EMT, but their interplay has not been clarified yet. In this study we provide evidence that DNMT3A interaction with several miRNAs has a central role in an ex-vivo EMT prostate cancer model obtained via exposure of PC3 cells to conditioned media from cancer-associated fibroblasts (CM-CAFs). The analysis of the alterations of the miRNA profile shows that miR-200 family (miR-200a/200b/429, miR-200c/141), miR-205, and miR-203, known to modulate key EMT factors, are downregulated and hyper-methylated at their promoters. DNMT3A (mainly isoform a) is recruited onto these miRNA promoters, coupled with the increase of H3K27me3/H3K9me3 and/or the decrease of H3K4me3/H3K36me3. Most interestingly, our results reveal the differential expression of two DNMT3A isoforms (a and b) during ex-vivo EMT and a regulatory feedback loop between miR-429 and DNMT3A that can promote and sustain the transition toward a more mesenchymal phenotype. We demonstrate the ability of miR-429 to target DNMT3A 3'UTR and modulate the expression of EMT factors, in particular ZEB1. Survey of the PRAD-TCGA data set shows that patients expressing an EMT-like signature are indeed characterized by down-regulation of the same miRNAs with a diffused hyper-methylation at miR-200c/141 and miR-200a/200b/429 promoters. Finally, we show that miR-1260a also targets DNMT3A, although it does not seem involved in EMT in prostate cancer.

Epigenome-wide DNA methylation signature of benzo[a]pyrene exposure and their mediation roles in benzo[a]pyrene-associated lung cancer development

Benzo[a]pyrene (B[a]P) is a typical carcinogen associated with increased lung cancer risk, but the underlying mechanisms remain unclear. This study aimed to investigate epigenome-wide DNA methylation associated with B[a]P exposure and their mediation effects on B[a]P-lung cancer association in two lung cancer case-control studies of 462 subjects. Their plasma levels of benzo[a]pyrene diol epoxide-albumin (BPDE-Alb) adducts and genome-wide DNA methylations were separately detected in peripheral blood by using enzyme-linked immunosorbent assay (ELISA) and genome-wide methylation arrays. The epigenome-wide meta-analysis was performed to analyze the associations between BPDE-Alb adducts and DNA methylations. Mediation analysis was applied to assess effect of DNA methylation on the B[a]P-lung cancer association. We identified 15 CpGs associated with BPDE-Alb adducts (P_-meta < 1.0 × 10^-5), among which the methylation levels at five loci (cg06245338, cg24256211, cg15107887, cg02211741, and cg04354393 annotated to UBE2O, SAMD4A, ACBD6, DGKZ, and SLFN13, respectively) mediated a separate 38.5%, 29.2%, 41.5%, 47.7%, 56.5%, and a joint 58.2% of the association between BPDE-Alb adducts and lung cancer risk. Compared to the traditional factors [area under the curve (AUC) = 0.788], addition of these CpGs exerted improved discriminations for lung cancer, with AUC ranging 0.828-0.861. Our results highlight DNA methylation alterations as potential mediators in lung tumorigenesis induced by B[a]P exposure.

Integrated Bioinformatics Analysis of DNA Methylation Biomarkers in Thyroid Cancer Based on TCGA Database

Previous studies have reported a cluster of aberrant promoter methylation changes associated with silencing of tumor suppressor genes in thyroid cancer (TC), but these results of individual genes are far from enough. In this work, we aimed to investigate the onset and pattern of methylation changes during the progression of TC by informatics analysis. We downloaded the DNA methylation and RNA sequencing datasets from The Cancer Genome Atlas focusing on TC. Abnormally methylated differentially expressed genes (DEGs) were sorted and pathways were analyzed. The KEGG and GO were then used to perform enrichment and functional analysis of identified pathways and genes. Gene-drug interaction network and human protein atlas were applied to obtain feature DNA methylation biomarkers. In total, we identified 2170 methylation-driven DEGs, including 1054 hypermethylatedlow-expression DEGs and 1116 hypomethylated-high-expression DEGs at the screening step. Further analysis screened total of eight feature DNA methylation biomarkers (RXRG, MET, PDGFRA, FCGR3A, VEGFA, CSF1R, FCGR1A and C1QA). Pathway analysis showed that aberrantly methylated DEGs mainly associated with transcriptional misregulation in cancer, MAPK signaling, and intrinsic apoptotic signaling in TC. Taken together, we have identified novel aberrantly methylated genes and pathways linked to TC, which might serve as novel biomarkers for precision diagnosis and disease treatment.

A Pan-cancer Analysis Reveals the Abnormal Expression and Drug Sensitivity of CSF1

BACKGROUND

Colony-stimulating factor-1 (CSF1) is a cytokine that is closely related to normal organ growth and development as well as tumor progression.

OBJECTIVE

We aimed to summarize and clarify the reasons for the abnormal expression of CSF1 in tumors and explore the role of CSF1 in tumor progression. Furthermore, drug response analysis may provide a reference for clinical medication.

METHODS

The expression of CSF1 was analyzed by TCGA and CCLE. Besides, cBioPortal and MethSurv databases were used to conduct mutation and DNA methylation analyses. Further, correlations between CSF1 expression and tumor stage, survival, immune infiltration, drug sensitivity and enrichment analyses were validated via UALCAN, Kaplan-Meier plotter, TIMER, CTRP and Coexperia databases.

RESULTS

CSF1 is expressed in a variety of tissues, meaningfully, it can be detected in blood. Compared with normal tissues, CSF1 expression was significantly decreased in most tumors. The missense mutation and DNA methylation of CSF1 may cause the downregulated expression. Moreover, decreased CSF1 expression was related with higher tumor stage and worse survival. Further, the promoter DNA methylation level of CSF1 was prognostically significant in most tumors. Besides, CSF1 was closely related to immune infiltration, especially macrophages. Importantly, CSF1 expression was associated with a good response to VEGFRs inhibitors, which may be due to the possible involvement of CSF1 in tumor angiogenesis and metastasis processes.

CONCLUSION

The abnormal expression of CSF1 could serve as a promising biomarker of tumor progression and prognosis in pan-cancer. Significantly, angiogenesis and metastasis inhibitors may show a good response to CSF1-related tumors.

A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset

As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach.

A multi-scale integrated analysis identifies KRT8 as a pan-cancer early biomarker

An early biomarker would transform our ability to screen and treat patients with cancer. The large amount of multi-scale molecular data in public repositories from various cancers provide unprecedented opportunities to find such a biomarker. However, despite identification of numerous molecular biomarkers using these public data, fewer than 1% have proven robust enough to translate into clinical practice. One of the most important factors affecting the successful translation to clinical practice is lack of real-world patient population heterogeneity in the discovery process. Almost all biomarker studies analyze only a single cohort of patients with the same cancer using a single modality. Recent studies in other diseases have demonstrated the advantage of leveraging biological and technical heterogeneity across multiple independent cohorts to identify robust disease biomarkers. Here we analyzed 17149 samples from patients with one of 23 cancers that were profiled using either DNA methylation, bulk and single-cell gene expression, or protein expression in tumor and serum. First, we analyzed DNA methylation profiles of 9855 samples across 23 cancers from The Cancer Genome Atlas (TCGA). We then examined the gene expression profile of the most significantly hypomethylated gene, KRT8, in 6781 samples from 57 independent microarray datasets from NCBI GEO. KRT8 was significantly over-expressed across cancers except colon cancer (summary effect size=1.05; p < 0.0001). Further, single-cell RNAseq analysis of 7447 single cells from lung tumors showed that genes that significantly correlated with KRT8 (p < 0.05) were involved in p53-related pathways. Immunohistochemistry in tumor biopsies from 294 patients with lung cancer showed that high protein expression of KRT8 is a prognostic marker of poor survival (HR = 1.73, p = 0.01). Finally, detectable KRT8 in serum as measured by ELISA distinguished patients with pancreatic cancer from healthy controls with an AUROC=0.94. In summary, our analysis demonstrates that KRT8 is (1) differentially expressed in several cancers across all molecular modalities and (2) may be useful as a biomarker to identify patients that should be further tested for cancer.

MYC DNA Methylation in Prostate Tumor Tissue Is Associated with Gleason Score

Increasing evidence suggests a role of epigenetic mechanisms at chromosome 8q24, an important cancer genetic susceptibility region, in prostate cancer. We investigated whether MYC DNA methylation at 8q24 (six CpG sites from exon 3 to the 3′ UTR) in prostate tumor was associated with tumor aggressiveness (based on Gleason score, GS), and we incorporated RNA expression data to investigate the function. We accessed radical prostatectomy tissue for 50 Caucasian and 50 African American prostate cancer patients at the University of Maryland Medical Center, selecting an equal number of GS 6 and GS 7 cases per group. MYC DNA methylation was lower in tumor than paired normal prostate tissue for all six CpG sites (median difference: −14.74 to −0.20 percentage points), and we observed similar results for two nearby sites in The Cancer Genome Atlas (p < 0.0001). We observed significantly lower methylation for more aggressive (GS 7) than less aggressive (GS 6) tumors for three exon 3 sites (for CpG 212 (chr8:128753145), GS 6 median = 89.7%; GS 7 median = 85.8%; p-value = 9.4 × 10⁻⁴). MYC DNA methylation was not associated with MYC expression, but was inversely associated with PRNCR1 expression after multiple comparison adjustment (q-value = 0.04). Findings suggest that prostate tumor MYC exon 3 hypomethylation is associated with increased aggressiveness.

Colon cancer-specific diagnostic and prognostic biomarkers based on genome-wide abnormal DNA methylation

Abnormal DNA methylation is a major early contributor to colon cancer (COAD) development. We conducted a cohort-based systematic investigation of genome-wide DNA methylation using 299 COAD and 38 normal tissue samples from TCGA. Through conditional screening and machine learning with a training cohort, we identified one hypomethylated and nine hypermethylated differentially methylated CpG sites as potential diagnostic biomarkers, and used them to construct a COAD-specific diagnostic model. Unlike previous models, our model precisely distinguished COAD from nine other cancer types (e.g., breast cancer and liver cancer; error rate ≤ 0.05) and from normal tissues in the training cohort (AUC = 1). The diagnostic model was verified using a validation cohort from The Cancer Genome Atlas (AUC = 1) and five independent cohorts from the Gene Expression Omnibus (AUC ≥ 0.951). Using Cox regression analyses, we established a prognostic model based on six CpG sites in the training cohort, and verified the model in the validation cohort. The prognostic model sensitively predicted patients’ survival (p ≤ 0.00011, AUC ≥ 0.792) independently of important clinicopathological characteristics of COAD (e.g., gender and age). Thus, our DNA methylation analysis provided precise biomarkers and models for the early diagnosis and prognostic evaluation of COAD.

An epigenome-wide association study of Alzheimer's disease blood highlights robust DNA hypermethylation in the HOXB6 gene

A growing number of epigenome-wide association studies have demonstrated a role for DNA methylation in the brain in Alzheimer's disease. With the aim of exploring peripheral biomarker potential, we have examined DNA methylation patterns in whole blood collected from 284 individuals in the AddNeuroMed study, which included 89 nondemented controls, 86 patients with Alzheimer's disease, and 109 individuals with mild cognitive impairment, including 38 individuals who progressed to Alzheimer's disease within 1 year. We identified significant differentially methylated regions, including 12 adjacent hypermethylated probes in the HOXB6 gene in Alzheimer's disease, which we validated using pyrosequencing. Using weighted gene correlation network analysis, we identified comethylated modules of genes that were associated with key variables such as APOE genotype and diagnosis. In summary, this study represents the first large-scale epigenome-wide association study of Alzheimer's disease and mild cognitive impairment using blood. We highlight the differences in various loci and pathways in early disease, suggesting that these patterns relate to cognitive decline at an early stage.

Advances in Prognostic Methylation Biomarkers for Prostate Cancer

There is a major clinical need for accurate biomarkers for prostate cancer prognosis, to better inform treatment strategies and disease monitoring. Current clinically recognised prognostic factors, including prostate-specific antigen (PSA) levels, lack sensitivity and specificity in distinguishing aggressive from indolent disease, particularly in patients with localised intermediate grade prostate cancer. There has therefore been a major focus on identifying molecular biomarkers that can add prognostic value to existing markers, including investigation of DNA methylation, which has a known role in tumorigenesis. In this review, we will provide a comprehensive overview of the current state of DNA methylation biomarker studies in prostate cancer prognosis, and highlight the advances that have been made in this field. We cover the numerous studies into well-established candidate genes, and explore the technological transition that has enabled hypothesis-free genome-wide studies and the subsequent discovery of novel prognostic genes.

SLCO4C1 promoter methylation is a potential biomarker for prognosis associated with biochemical recurrence-free survival after radical prostatectomy

BACKGROUND

Prostate cancer (PC) is a commonly diagnosed malignancy in males, especially in the western hemisphere. The extensive use of multiple biomarkers plays an important role in the diagnosis and prognosis of PC. However, the accuracy of biomarkers for PC prognosis needs to be urgently improved. This study aimed to identify a novel prognostic biomarker for PC.

MATERIALS AND METHODS

Differentially methylated CpG sites were identified from the GSE76938 dataset ( https://www.ncbi.nlm.nih.gov/geo/ ) using R software version 3.1.4. Four significant CpG sites on the SLCO4C1 gene were found to be closely associated with prognosis in PC. Data downloaded from The Cancer Genome Atlas (TCGA) were used for validation. Co-expression and functional enrichment analyses were used to explore the roles of SLCO4C1 in molecular functions, biological processes and cellular components. Total RNA extraction and qRT-PCR were used to reveal the difference in SLCO4C1 expression between tumour and normal tissues. Bisulfite amplicon sequencing (BSAS) was used to identify methylation levels at the CpG sites.

RESULTS

In the GSE76938 cohort, 10,206 CpG sites were identified to be differentially methylated in tumour versus normal prostate tissues. Among the CpG sites, four sites (cg06480736, cg19774478, cg19788741 and cg22149516) located in the promotor region (TSS200-1500) of SLCO4C1 were found to be significantly hypermethylated in tumour tissues. The results were validated in an independent dataset (TCGA PRAD cohort). In the cohort from TCGA, SLCO4C1 expression was negatively correlated with methylation levels at the four sites. The results of qRT-PCR validated that tumour tissues had a relatively lower expression of SLCO4C1. Bisulfite amplicon sequencing (BSAS) further confirmed a higher methylation level at the SLCO4C1 promoter in tumour tissues. SLCO4C1 (cg06480736, cg19774478, cg19788741 and cg22149516) was identified as a significant promising biomarker for biochemical recurrence-free survival in Kaplan-Meier analysis (P < 0.01) and univariate Cox proportional hazards analysis: cg06480736 (HR 15.914, P < 0.001), cg19774478 (HR 9.001, P < 0.001), cg19788741 (HR 10.759, P = 0.003) and cg22149516 (HR 17.144, P = 0.006). However, three sites, namely, cg06480736 (HR 1.809, P = 0.049), cg19774478 (HR 1.903, P = 0.041) and cg22149516 (HR 2.316, P = 0.008), were confirmed in multivariate analysis.

CONCLUSIONS

SLCO4C1 promoter methylation, including that at three CpG sites, namely, cg06480736, cg19774478 and cg22149516, is a potential biomarker for risk stratification and might offer significantly relevant prognostic information for PC patients after radical prostatectomy.

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.

Voltammetric Application of Polypyrrole-Modified Microelectrode Array for the Characterization of DNA Methylation in Glutathione S-Transferase Pi 1

Direct and efficient label-free voltammetric detection of Glutathione S-Transferase Pi 1 (GSTP1) hypermethylation is reported using a custom developed 16-channel Microelectrode Array chip. The microelectrode array chip is used in a dipstick configuration allowing detection of DNA hybridization in a solution volume of only 0.35 mL. Platinum microelectrode disks (n = 16) 30 µm in diameter have been modified with a polypyrrole bilayer before any contact with the oligonucleotides. The attachment of the 15-mer Probe DNA to the bilayer is random but controlled by the presence of aliphatic tether groups allowing it to form a bidentate complex with the probe DNA. The voltammetric detection procedure of methylated GSTP1-specific target DNA is combined with bisulfite treatment of target DNA. Changes at the interface of the modified microelectrodes in an array configuration are used to record simultaneously cyclic voltammetry on all of the devices. The detection of the hybridization is evaluated statistically for a yes or no event by comparing the changes in recorded cyclic voltammograms before and after exposure to the Target DNA. All cyclic voltammograms of the methylated target show a greater percentage change than those with the non-methylated target exposure and show a greater change in cyclic voltammogram area after methylated target exposure. We observe an average percentage difference of 25.6% ± 4.9 with a variation of 19.1%. These results demonstrate that the fast sensing strategy possesses sensitivity and good specificity. Furthermore, this technology can potentially support rapid, accurate diagnosis and risk assessment of patients with prostate cancer.

Chronic lymphocytic leukemia patients with heterogeneously or fully methylated LPL promotor display longer time to treatment

AIM

We investigated whether DNA methylation regulates expression of LPL and PI3K complex genes in chronic lymphocytic leukemia (CLL) and evaluated the prognostic significance of LPL promoter methylation in CLL patients. Patients & methods: Methylation of LPL promoter was assessed in 112 patients using methylation-sensitive high-resolution melting (MS-HRM).

RESULTS

Patients with a fully or heterogeneously methylated LPL promoter had significantly longer median time to treatment (p < 0.001) and 75% lower (hazard ratio: 0.25; 95% CI: 0.15-0.42; p < 0.001) risk of requirement for treatment as opposed to patients with nonmethylated promoter. Multivariate modeling confirmed independent prognostic value of these findings.

CONCLUSION

Chronic lymphocytic leukemia patients with a fully or heterogeneously methylated LPL gene promoter display indolent disease course and acquisition of heterogeneous methylation of LPL promoter is insufficient to induce gene expression.

A five-CpG DNA methylation score to predict metastatic-lethal outcomes in men treated with radical prostatectomy for localized prostate cancer

BACKGROUND

Prognostic biomarkers for localized prostate cancer (PCa) could improve personalized medicine. Our group previously identified a panel of differentially methylated CpGs in primary tumor tissue that predict disease aggressiveness, and here we further validate these biomarkers.

METHODS

Pyrosequencing was used to assess CpG methylation of eight biomarkers previously identified using the HumanMethylation450 array; CpGs with strongly correlated (r >0.70) results were considered technically validated. Logistic regression incorporating the validated CpGs and Gleason sum was used to define and lock a final model to stratify men with metastatic-lethal versus non-recurrent PCa in a training dataset. Coefficients from the final model were then used to construct a DNA methylation score, which was evaluated by logistic regression and Receiver Operating Characteristic (ROC) curve analyses in an independent testing dataset.

RESULTS

Five CpGs were technically validated and all were retained (P < 0.05) in the final model. The 5-CpG and Gleason sum coefficients were used to calculate a methylation score, which was higher in men with metastatic-lethal progression (P = 6.8 × 10-6 ) in the testing dataset. For each unit increase in the score there was a four-fold increase in risk of metastatic-lethal events (odds ratio, OR = 4.0, 95%CI = 1.8-14.3). At 95% specificity, sensitivity was 74% for the score compared to 53% for Gleason sum alone. The score demonstrated better prediction performance (AUC = 0.91; pAUC = 0.037) compared to Gleason sum alone (AUC = 0.87; pAUC = 0.025).

CONCLUSIONS

The DNA methylation score improved upon Gleason sum for predicting metastatic-lethal progression and holds promise for risk stratification of men with aggressive tumors. This prognostic score warrants further evaluation as a tool for improving patient outcomes.

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

Genomic DNA Methylation-Derived Algorithm Enables Accurate Detection of Malignant Prostate Tissues

Introduction

The current methodology involving diagnosis of prostate cancer (PCa) relies on the pathology examination of prostate needle biopsies, a method with high false negative rates partly due to temporospatial, molecular, and morphological heterogeneity of prostate adenocarcinoma. It is postulated that molecular markers have a potential to assign diagnosis to a considerable portion of undetected prostate tumors. This study examines the genome-wide DNA methylation changes in PCa in search of genomic markers for the development of a diagnostic algorithm for PCa screening.

Methods

Archival PCa and normal tissues were assessed using genomic DNA methylation arrays. Differentially methylated sites and regions (DMRs) were used for functional assessment, gene-set enrichment and protein interaction analyses, and examination of transcription factor-binding patterns. Raw signal intensity data were used for identification of recurrent copy number variations (CNVs). Non-redundant fully differentiating cytosine-phosphate-guanine sites (CpGs), which did not overlap CNV segments, were used in an L1 regularized logistic regression model (LASSO) to train a classification algorithm. Validation of this algorithm was performed using a large external cohort of benign and tumor prostate arrays.

Results

Approximately 6,000 probes and 600 genomic regions showed significant DNA methylation changes, primarily involving hypermethylation. Gene-set enrichment and protein interaction analyses found an overrepresentation of genes related to cell communications, neurogenesis, and proliferation. Motif enrichment analysis demonstrated enrichment of tumor suppressor-binding sites nearby DMRs. Several of these regions were also found to contain copy number amplifications. Using four non-redundant fully differentiating CpGs, we trained a classification model with 100% accuracy in discriminating tumors from benign samples. Validation of this algorithm using an external cohort of 234 tumors and 92 benign samples yielded 96% sensitivity and 98% specificity. The model was found to be highly sensitive to detect metastatic lesions in bone, lymph node, and soft tissue, while being specific enough to differentiate the benign hyperplasia of prostate from tumor.

Conclusion

A considerable component of PCa DNA methylation profile represent driver events potentially established/maintained by disruption of tumor suppressor activity. As few as four CpGs from this profile can be used for screening of PCa.

SEL120-34A is a novel CDK8 inhibitor active in AML cells with high levels of serine phosphorylation of STAT1 and STAT5 transactivation domains

Inhibition of oncogenic transcriptional programs is a promising therapeutic strategy. A substituted tricyclic benzimidazole, SEL120-34A, is a novel inhibitor of Cyclin-dependent kinase 8 (CDK8), which regulates transcription by associating with the Mediator complex. X-ray crystallography has shown SEL120-34A to be a type I inhibitor forming halogen bonds with the protein's hinge region and hydrophobic complementarities within its front pocket. SEL120-34A inhibits phosphorylation of STAT1 S727 and STAT5 S726 in cancer cells in vitro. Consistently, regulation of STATs- and NUP98-HOXA9- dependent transcription has been observed as a dominant mechanism of action in vivo. Treatment with the compound resulted in a differential efficacy on AML cells with elevated STAT5 S726 levels and stem cell characteristics. In contrast, resistant cells were negative for activated STAT5 and revealed lineage commitment. In vivo efficacy in xenotransplanted AML models correlated with significant repression of STAT5 S726. Favorable pharmacokinetics, confirmed safety and in vivo efficacy provide a rationale for the further clinical development of SEL120-34A as a personalized therapeutic approach in AML.

Integrating Multiple Data Sources for Combinatorial Marker Discovery: A Study in Tumorigenesis

Identification of combinatorial markers from multiple data sources is a challenging task in bioinformatics. Here, we propose a novel computational framework for identifying significant combinatorial markers ( s) using both gene expression and methylation data. The gene expression and methylation data are integrated into a single continuous data as well as a (post-discretized) boolean data based on their intrinsic (i.e., inverse) relationship. A novel combined score of methylation and expression data (viz., ) is introduced which is computed on the integrated continuous data for identifying initial non-redundant set of genes. Thereafter, (maximal) frequent closed homogeneous genesets are identified using a well-known biclustering algorithm applied on the integrated boolean data of the determined non-redundant set of genes. A novel sample-based weighted support ( ) is then proposed that is consecutively calculated on the integrated boolean data of the determined non-redundant set of genes in order to identify the non-redundant significant genesets. The top few resulting genesets are identified as potential s. Since our proposed method generates a smaller number of significant non-redundant genesets than those by other popular methods, the method is much faster than the others. Application of the proposed technique on an expression and a methylation data for Uterine tumor or Prostate Carcinoma produces a set of significant combination of markers. We expect that such a combination of markers will produce lower false positives than individual markers.

RHCG and TCAF1 promoter hypermethylation predicts biochemical recurrence in prostate cancer patients treated by radical prostatectomy

PURPOSE

The lack of biomarkers that can distinguish aggressive from indolent prostate cancer has caused substantial overtreatment of clinically insignificant disease. Here, by genome-wide DNA methylome profiling, we sought to identify new biomarkers to improve the accuracy of prostate cancer diagnosis and prognosis.

EXPERIMENTAL DESIGN

Eight novel candidate markers, COL4A6, CYBA, TCAF1 (FAM115A), HLF, LINC01341 (LOC149134), LRRC4, PROM1, and RHCG, were selected from Illumina Infinium HumanMethylation450 BeadChip analysis of 21 tumor (T) and 21 non-malignant (NM) prostate specimens. Diagnostic potential was further investigated by methylation-specific qPCR analysis of 80 NM vs. 228 T tissue samples. Prognostic potential was assessed by Kaplan-Meier, uni- and multivariate Cox regression analysis in 203 Danish radical prostatectomy (RP) patients (cohort 1), and validated in an independent cohort of 286 RP patients from Switzerland and the U.S. (cohort 2).

RESULTS

Hypermethylation of the 8 candidates was highly cancer-specific (area under the curves: 0.79-1.00). Furthermore, high methylation of the 2-gene panel RHCG-TCAF1 was predictive of biochemical recurrence (BCR) in cohort 1, independent of the established clinicopathological parameters Gleason score, pathological tumor stage, and pre-operative PSA (HR (95% confidence interval (CI)): 2.09 (1.26 - 3.46); P = 0.004), and this was successfully validated in cohort 2 (HR (95% CI): 1.81 (1.05 - 3.12); P = 0.032).

CONCLUSION

Methylation of the RHCG-TCAF1 panel adds significant independent prognostic value to established prognostic parameters for prostate cancer and thus may help to guide treatment decisions in the future. Further investigation in large independent cohorts is necessary before translation into clinical utility.

The role of TET-mediated DNA hydroxymethylation in prostate cancer

Ten-eleven translocation (TET) proteins are recently characterized dioxygenases that regulate demethylation by oxidizing 5-methylcytosine to 5-hydroxymethylcytosine and further derivatives. The recent finding that 5hmC is also a stable and independent epigenetic modification indicates that these proteins play an important role in diverse physiological and pathological processes such as neural and tumor development. Both the genomic distribution of (hydroxy)methylation and the expression and activity of TET proteins are dysregulated in a wide range of cancers including prostate cancer. Up to now it is still unknown how changes in TET and 5(h)mC profiles are related to the pathogenesis of prostate cancer. In this review, we explore recent advances in the current understanding of how TET expression and function are regulated in development and cancer. Furthermore, we look at the impact on 5hmC in prostate cancer and the potential underlying mechanisms. Finally, we tried to summarize the latest techniques for detecting and quantifying global and locus-specific 5hmC levels of genomic DNA.

Candidate diagnostic miRNAs that can detect cancer in prostate biopsy

BACKGROUND

While histopathological evaluation remains the gold standard for diagnosis of prostate cancer (PCa), sampling errors remain a frequent problem; therefore, use of tissue biomarkers that can distinguish between benign and malignant prostate disease is a potentially beneficial diagnostic strategy.

METHODS

Deep sequencing of the miRNA transcriptome of 14 benign prostatic hyperplasia (BPH) and 60 cancerous and non-cancerous prostate samples extracted from 34 cancer-bearing prostates removed by prostatectomy was performed; of the latter 60 samples, 16, 21, and 23 samples contained <10%, >30%, and no dysplastic cells, respectively. The predictive value of selected miRNAs was then tested by quantitative reverse-transcribed PCR (qRT-PCR), using two separate chemistries, Exiqon and Taqman, to evaluate the tissue samples obtained by prostatectomy. Validation experiments were also performed for a subset of miRNAs by qRT-PCR of 87 prostate core biopsies.

RESULTS

We identified 123 miRNAs significantly dysregulated in PCa (adjusted P-values <0.05); 110 and 13 miRNAs were dysregulated only in cancerous samples and non-cancerous samples extracted from cancer-bearing prostates, respectively, while 31 were dysregulated regardless of the dysplastic cell content of the studied specimens. The clinical utility of eight selected miRNAs was analyzed using the same sample set with two qRT-PCR chemistries. Measurable qRT-PCR signals were obtained for seven and six miRNAs using the Exiqon and Taqman chemistries, respectively, and expression levels of six and four of these miRNAs differed significantly between BPH and PCa samples, regardless of dysplastic cell content. Validation experiments on core biopsies using qRT-PCR confirmed differential expression between BPH and PCa of four miRNAs (miR-187-3p, miR-183-5p, miR-32-5p, and miR-141-5p) using the Exiqon and one miRNA (miR-187-3p) with the Taqman chemistry.

CONCLUSIONS

Our sequencing analyses identified several candidate diagnostic miRNAs and confirmed some which have previously been reported as diagnostic in prostate malignancy. The results of this study suggest also that some of selected miRNAs can differentiate between non-malignant and malignant prostates even when neoplastic cells are missing from the studied specimen.

Validation of DAB2IP methylation and its relative significance in predicting outcome in renal cell carcinoma

We have recently reported tumor suppressive role of DAB2IP in RCC development. In this study, We identified one CpG methylation biomarker (DAB2IP CpG1) located UTSS of DAB2IP that was associated with poor overall survival in a cohort of 318 ccRCC patients from the Cancer Genome Atlas (TCGA). We further validated the prognostic accuracy of DAB2IP CpG methylation by pyrosequencing quantitative methylation assay in 224 ccRCC patients from multiple Chinese centers (MCHC set), and 239 patients from University of Texas Southwestern Medical Center at Dallas (UTSW set) by using FFPE samples. DAB2IP CpG1 can predict the overall survival of patients in TCGA, MCHC, and UTSW sets independent of patient age, Fuhrman grade and TNM stage (all p<0.05). DAB2IP CpG1 successfully categorized patients into high-risk and low-risk groups with significant differences of clinical outcome in respective clinical subsets, regardless of age, sex, grade, stage, or race (HR: 1.63-7.83; all p<0.05). The detection of DAB2IP CpG1 methylation was minimally affected by ITH in ccRCC. DAB2IP mRNA expression was regulated by DNA methylation in vitro. DAB2IP CpG1 methylation is a practical and repeatable biomarker for ccRCC, which can provide prognostic value that complements the current staging system.

Towards integrated oncogenic marker recognition through mutual information-based statistically significant feature extraction: an association rule mining based study on cancer expression and methylation profiles

Background

Marker detection is an important task in complex disease studies. Here we provide an association rule mining (ARM) based approach for identifying integrated markers through mutual information (MI) based statistically significant feature extraction, and apply it to acute myeloid leukemia (AML) and prostate carcinoma (PC) gene expression and methylation profiles.

Methods

We first collect the genes having both expression and methylation values in AML as well as PC. Next, we run Jarque-Bera normality test on the expression/methylation data to divide the whole dataset into two parts: one that ollows normal distribution and the other that does not follow normal distribution. Thus, we have now four parts of the dataset: normally distributed expression data, normally distributed methylation data, non-normally distributed expression data, and non-normally distributed methylated data. A feature-extraction technique, "mRMR" is then utilized on each part. This results in a list of top-ranked genes. Next, we apply Welch t-test (parametric test) and Shrink t-test (non-parametric test) on the expression/methylation data for the top selected normally distributed genes and non-normally distributed genes, respectively. We then use a recent weighted ARM method, "RANWAR" to combine all/specific resultant genes to generate top oncogenic rules along with respective integrated markers. Finally, we perform literature search as well as KEGG pathway and Gene-Ontology (GO) analyses using Enrichr database for in silico validation of the prioritized oncogenes as the markers and labeling the markers as existing or novel.

Results

The novel markers of AML are {ABCB11↑∪KRT17↓} (i.e., ABCB11 as up-regulated, & KRT17 as down-regulated), and {AP1S1-∪KRT17↓∪NEIL2-∪DYDC1↓}) (i.e., AP1S1 and NEIL2 both as hypo-methylated, & KRT17 and DYDC1 both as down-regulated). The novel marker of PC is {UBIAD1¶∪APBA2‡∪C4orf31‡} (i.e., UBIAD1 as up-regulated and hypo-methylated, & APBA2 and C4orf31 both as down-regulated and hyper-methylated).

Conclusion

The identified novel markers might have critical roles in AML as well as PC. The approach can be applied to other complex disease.

RIFS: a randomly restarted incremental feature selection algorithm

The advent of big data era has imposed both running time and learning efficiency challenges for the machine learning researchers. Biomedical OMIC research is one of these big data areas and has changed the biomedical research drastically. But the high cost of data production and difficulty in participant recruitment introduce the paradigm of “large p small n” into the biomedical research. Feature selection is usually employed to reduce the high number of biomedical features, so that a stable data-independent classification or regression model may be achieved. This study randomly changes the first element of the widely-used incremental feature selection (IFS) strategy and selects the best feature subset that may be ranked low by the statistical association evaluation algorithms, e.g. t-test. The hypothesis is that two low-ranked features may be orchestrated to achieve a good classification performance. The proposed Randomly re-started Incremental Feature Selection (RIFS) algorithm demonstrates both higher classification accuracy and smaller feature number than the existing algorithms. RIFS also outperforms the existing methylomic diagnosis model for the prostate malignancy with a larger accuracy and a lower number of transcriptomic features.

Exploiting Epigenetic Alterations in Prostate Cancer

Prostate cancer affects an increasing number of men worldwide and is a leading cause of cancer-associated deaths. Beside genetic mutations, many epigenetic alterations including DNA and histone modifications have been identified in clinical prostate tumor samples. They have been linked to aberrant activity of enzymes and reader proteins involved in these epigenetic processes, leading to the search for dedicated inhibitory compounds. In the wake of encouraging anti-tumor efficacy results in preclinical models, epigenetic modulators addressing different targets are now being tested in prostate cancer patients. In addition, the assessment of microRNAs as stratification biomarkers, and early clinical trials evaluating suppressor microRNAs as potential prostate cancer treatment are being discussed.

Genome-wide DNA methylation measurements in prostate tissues uncovers novel prostate cancer diagnostic biomarkers and transcription factor binding patterns

BACKGROUND

Current diagnostic tools for prostate cancer lack specificity and sensitivity for detecting very early lesions. DNA methylation is a stable genomic modification that is detectable in peripheral patient fluids such as urine and blood plasma that could serve as a non-invasive diagnostic biomarker for prostate cancer.

METHODS

We measured genome-wide DNA methylation patterns in 73 clinically annotated fresh-frozen prostate cancers and 63 benign-adjacent prostate tissues using the Illumina Infinium HumanMethylation450 BeadChip array. We overlaid the most significantly differentially methylated sites in the genome with transcription factor binding sites measured by the Encyclopedia of DNA Elements consortium. We used logistic regression and receiver operating characteristic curves to assess the performance of candidate diagnostic models.

RESULTS

We identified methylation patterns that have a high predictive power for distinguishing malignant prostate tissue from benign-adjacent prostate tissue, and these methylation signatures were validated using data from The Cancer Genome Atlas Project. Furthermore, by overlaying ENCODE transcription factor binding data, we observed an enrichment of enhancer of zeste homolog 2 binding in gene regulatory regions with higher DNA methylation in malignant prostate tissues.

CONCLUSIONS

DNA methylation patterns are greatly altered in prostate cancer tissue in comparison to benign-adjacent tissue. We have discovered patterns of DNA methylation marks that can distinguish prostate cancers with high specificity and sensitivity in multiple patient tissue cohorts, and we have identified transcription factors binding in these differentially methylated regions that may play important roles in prostate cancer development.

Evaluation of a short RNA within Prostate Cancer Gene 3 in the predictive role for future cancer using non-malignant prostate biopsies

Background

Prostate Cancer 3 (PCA3) is a long non-coding RNA (ncRNA) upregulated in prostate cancer (PCa). We recently identified a short ncRNA expressed from intron 1 of PCA3. Here we test the ability of this ncRNA to predict the presence of cancer in men with a biopsy without PCa.

Methods

We selected men whose initial biopsy did not identify PCa and selected matched cohorts whose subsequent biopsies revealed PCa or benign tissue. We extracted RNA from the initial biopsy and measured PCA3-shRNA2, PCA3 and PSA (qRT-PCR).

Results

We identified 116 men with and 94 men without an eventual diagnosis of PCa in 2–5 biopsies (mean 26 months), collected from 2002–2008. The cohorts were similar for age, PSA and surveillance period. We detected PSA and PCA3-shRNA2 RNA in all samples, and PCA3 RNA in 90% of biopsies. The expression of PCA3 and PCA3-shRNA2 were correlated (Pearson’s r = 0.37, p<0.01). There was upregulation of PCA3 (2.1-fold, t-test p = 0.02) and PCA3-shRNA2 (1.5-fold) in men with PCa on subsequent biopsy, although this was not significant for the latter RNA (p = 0.2). PCA3 was associated with the future detection of PCa (C-index 0.61, p = 0.01). This was not the case for PCA3-shRNA2 (C-index 0.55, p = 0.2).

Conclusions

PCA3 and PCA3-shRNA2 expression are detectable in historic biopsies and their expression is correlated suggesting co-expression. PCA3 expression was upregulated in men with PCa diagnosed at a future date, the same did not hold for PCA3-shRNA2. Futures studies should explore expression in urine and look at a time course between biopsy and PCa detection.

The importance of DNA methylation in prostate cancer development

After briefly reviewing the nature of DNA methylation, its general role in cancer and the tools available to interrogate it, we consider the literature surrounding DNA methylation as relating to prostate cancer. Specific consideration is given to recurrent alterations. A list of frequently reported genes is synthesized from 17 studies that have reported on methylation changes in malignant prostate tissue, and we chart the timing of those changes in the diseases history through amalgamation of several previously published data sets. We also review associations with genetic alterations and hormone signalling, before the practicalities of investigating prostate cancer methylation using cell lines are assessed. We conclude by outlining the interplay between DNA methylation and prostate cancer metabolism and their regulation by androgen receptor, with a specific discussion of the mitochondria and their associations with DNA methylation.

Prospective Study of CRMP4 Promoter Methylation in Prostate Biopsies as a Predictor For Lymph Node Metastases

Background

For patients with prostate cancer (PCa), the presence of pelvic lymph node metastasis (LNM) is a strong predictor of poor outcome. However, the approaches with promising sensitivity and specificity to detect LNM are still lacking. We investigated the value of collapsin response mediator protein 4 (CRMP4) promoter methylation in biopsies as a predictor for LNM.

Methods

CRMP4 promoter methylation at two previously identified CpG sites was determined in 80 case-matched biopsy samples (the training set) using bisulfite pyrosequencing. The predictive cutoff value was independently validated using cohort I of 339 PCa patients (Southern China) and cohort II of 328 case patients (Germany, across China). Mann-Whitney U test, the receiver operating characteristic curve, McNemar's test, and logistic regression were used to assess data. All statistical tests were two-sided.

Results

In the training set, CRMP4 promoter methylation (≥15.0% methylated) was statistically significantly associated with LNM (P < 001). Successful validations were achieved in both cohorts I and II (sensitivity = 92.3%, 95% confidence interval [CI] = 79.3 to 97.9, and sensitivity = 92.2%, 95% CI = 81.1 to 97.8, respectively; specificity = 92.7%, 95% CI = 80.2 to 99.1, and specificity = 91.3%, 95% CI = 87.4 to 94.4, respectively). The sensitivity of CRMP4 promoter methylation is superior to conventional MRI (cohort I: 92.3% vs 26.2%, P < 001; cohort II: 92.2% vs 33.3%, P < 001). CRMP4 promoter methylation is an independent predictor of LNM (cohort I: hazard ratio [HR] = 8.35, 95% CI = 5.64 to 12.35, P < 001; cohort II: HR = 12.46, 95% CI = 5.82 to 26.70, P < 001) in a multivariable analysis model.

Conclusion

CRMP4 promoter methylation in diagnostic biopsies could be a robust biomarker for LNM in PCa.

Identifying Epigenetic Biomarkers using Maximal Relevance and Minimal Redundancy Based Feature Selection for Multi-Omics Data

Epigenetic Biomarker discovery is an important task in bioinformatics. In this article, we develop a new framework of identifying statistically significant epigenetic biomarkers using maximal-relevance and minimal-redundancy criterion based feature (gene) selection for multi-omics dataset. Firstly, we determine the genes that have both expression as well as methylation values, and follow normal distribution. Similarly, we identify the genes which consist of both expression and methylation values, but do not follow normal distribution. For each case, we utilize a gene-selection method that provides maximal-relevant, but variable-weighted minimum-redundant genes as top ranked genes. For statistical validation, we apply t-test on both the expression and methylation data consisting of only the normally distributed top ranked genes to determine how many of them are both differentially expressed andmethylated. Similarly, we utilize Limma package for performing non-parametric Empirical Bayes test on both expression and methylation data comprising only the non-normally distributed top ranked genes to identify how many of them are both differentially expressed and methylated. We finally report the top-ranking significant gene-markerswith biological validation. Moreover, our framework improves positive predictive rate and reduces false positive rate in marker identification. In addition, we provide a comparative analysis of our gene-selection method as well as othermethods based on classificationperformances obtained using several well-known classifiers.

Analysis of the interplay between methylation and expression reveals its potential role in cancer aetiology

With ongoing developments in technology, changes in DNA methylation levels have become prevalent to study cancer biology. Previous studies report that DNA methylation affects gene expression in a direct manner, most probably by blocking gene regulatory regions. In this study, we have studied the interplay between methylation and expression to improve our knowledge of cancer aetiology. For this purpose, we have investigated which genomic regions are of higher importance; hence, first exon, 5'UTR and 200 bp near the transcription start sites are proposed as being more crucial compared to other genomic regions. Furthermore, we have searched for a valid methylation level change threshold, and as a result, 25 % methylation change in previously determined genomic regions showed the highest inverse correlation with expression data. As a final step, we have examined the commonly affected genes and pathways by integrating methylation and expression information. Remarkably, the GPR115 gene and ErbB signalling pathway were found to be significantly altered for all cancer types in our analysis. Overall, combining methylation and expression information and identifying commonly affected genes and pathways in a variety of cancer types revealed new insights of cancer disease mechanisms. Moreover, compared to previous methylation-based studies, we have identified more important genomic regions and have defined a methylation change threshold level in order to obtain more reliable results. In addition to the novel analysis framework that involves the analysis of four different cancer types, our study exposes essential information regarding the contribution of methylation changes and its impact on cancer disease biology, which may facilitate the identification of new drug targets.