HBCR_DMR: A Hybrid Method Based on Beta-Binomial Bayesian Hierarchical Model and Combination of Ranking Method to Detect Differential Methylation Regions in Bisulfite Sequencing Data.

DNA methylation is a key epigenetic modification involved in gene regulation, contributing to both physiological and pathological conditions. For a more profound comprehension, it is essential to conduct a precise comparison of DNA methylation patterns between sample groups that represent distinct statuses. Analysis of differentially methylated regions (DMRs) using computational approaches can help uncover the precise relationships between these phenomena. This paper describes a hybrid model that combines the beta-binomial Bayesian hierarchical model with a combination of ranking methods known as HBCR_DMR. During the initial phase, we model the actual methylation proportions of the CpG sites (CpGs) within the replicates. This modeling is achieved through beta-binomial distribution, with parameters set by a group mean and a dispersion parameter. During the second stage, we establish the selection of distinguishing CpG sites based on their methylation status, employing multiple ranking techniques. Finally, we combine the ranking lists of differentially methylated CpG sites through a voting system. Our analyses, encompassing simulations and real data, reveal outstanding performance metrics, including a sensitivity of 0.72, specificity of 0.89, and an F1 score of 0.76, yielding an overall accuracy of 0.82 and an AUC of 0.94. These findings underscore HBCR_DMR's robust capacity to distinguish methylated regions, confirming its utility as a valuable tool for DNA methylation analysis.

Application of deep learning in cancer epigenetics through DNA methylation analysis.

DNA methylation is a fundamental epigenetic modification involved in various biological processes and diseases. Analysis of DNA methylation data at a genome-wide and high-throughput level can provide insights into diseases influenced by epigenetics, such as cancer. Recent technological advances have led to the development of high-throughput approaches, such as genome-scale profiling, that allow for computational analysis of epigenetics. Deep learning (DL) methods are essential in facilitating computational studies in epigenetics for DNA methylation analysis. In this systematic review, we assessed the various applications of DL applied to DNA methylation data or multi-omics data to discover cancer biomarkers, perform classification, imputation and survival analysis. The review first introduces state-of-the-art DL architectures and highlights their usefulness in addressing challenges related to cancer epigenetics. Finally, the review discusses potential limitations and future research directions in this field.

MS-HRM protocol: a simple and low-cost approach for technical validation of next-generation methylation sequencing data.

DNA methylation is a fundamental epigenetic process and have a critical role in many biological processes. The study of DNA methylation at a large scale of genomic levels is widely conducted by several techniques that are next-generation sequencing (NGS)-based methods. Methylome data revealed by DNA methylation next-generation sequencing (mNGS), should be always verified by another technique which they usually have a high cost. In this study, we offered a low-cost approach to corroborate the mNGS data. In this regard, mNGS was performed on 6 colorectal cancer (case group) and 6 healthy individual colon tissue (control group) samples. An R-script detected differentially methylated regions (DMRs), was further validated by high resolution melting (MS-HRM) analysis. After analyzing the data, the algorithm found 194 DMRs. Two locations with the highest level of methylation difference were verified by MS-HRM, which their results were in accordance with the mNGS. Therefore, in the present study, we suggested MS-HRM as a simple, accurate and low-cost method, useful for confirming methylation sequencing results.

Crosstalk between DNA methylation and gene expression in colorectal cancer, a potential plasma biomarker for tracing this tumor.

Colorectal cancer (CRC), the second leading cause of cancer mortality, constitutes a significant global health burden. An accurate, noninvasive detection method for CRC as complement to colonoscopy could improve the effectiveness of treatment. In the present study, SureSelectXT Methyl-Seq was performed on cancerous and normal colon tissues and CLDN1, INHBA and SLC30A10 were found as candidate methylated genes. MethyLight assay was run on formalin-fixed paraffin-embedded (FFPE) and fresh case and control tissues to validate the methylation of the selected gene. The methylation was significantly different (p-values < 2.2e-16) with a sensitivity of 87.17%; at a specificity cut-off of 100% in FFPE tissues. Methylation studies on fresh tissues, indicated a sensitivity of 82.14% and a specificity cut-off of 92% (p-values = 1.163e-07). The biomarker performance was robust since, normal tissues indicated a significant 22.1-fold over-expression of the selected gene as compared to the corresponding CRC tissues (p-value < 2.2e-16) in the FFPE expression assay. In our plasma pilot study, evaluation of the tissue methylation marker in the circulating cell-free DNA, demonstrated that 9 out of 22 CRC samples and 20 out of 20 normal samples were identified correctly. In summary, there is a clinical feasibility that the offered methylated gene could serve as a candidate biomarker for CRC diagnostic purpose, although further exploration of our candidate gene is warranted.

Obesity, diabetes and the risk of colorectal adenoma and cancer.

BACKGROUND: Colorectal cancer (CRC) is the fourth most commonly diagnosed gastrointestinal (GI) malignancy and the third leading cause of cancer-related death worldwide. In the current case-control study, an association between diagnosis of CRC, obesity and diabetes was investigated. METHODS: Demographic characteristics, colonoscopy reports, history of drug, smoking, and medical history were collected from patients referred to a colonoscopy unit. The location, size and number of the polyps were recorded during the colonoscopy. Statistically, t-test was conducted for mean comparison for the groups. Pearson's chi-squared test (χ2) was applied to categorize variables. Five classification methods based on the important clinicopathological characteristics such as age, BMI, diabetes, family history of colon cancer was performed to predict the results of colonoscopy. RESULTS: Overall, 693 patients participated in this study. In the present study, 115 and 515 patients were evaluated for adenoma/adenocarcinoma and normal colonoscopy, respectively. The mean age of patients positive for adenoma or adenocarcinoma were significantly higher than the negative groups (p value < 0.001). Incidence of overweight and/or obesity (BMI > 25 kg/m2) were significantly higher in adenoma positive patients as compared to controls (49.9 and 0.9% respectively, p value = 0.04). The results also demonstrated a significant association between suffering from diabetes and having colon adenoma (OR = 1.831, 95%CI = 1.058-3.169, p value = 0.023). The experimental results of 5 classification methods on higher risk factors between colon adenoma and normal colonoscopy data were more than 82% and less than 0.42 for the percentage of classification accuracy and root mean squared error, respectively. CONCLUSIONS: In the current study, the occurrence of obesity measured based on BMI and diabetes in the adenoma positive patient group was significantly higher than the control group although there was no notable association between obesity, diabetes and adenocarcinoma.

The importance of stool DNA methylation in colorectal cancer diagnosis: A meta-analysis.

A large number of tumor-related methylated genes have been suggested to be of diagnostic and prognostic values for CRC when analyzed in patients' stool samples; however, reported sensitivities and specificities have been inconsistent and widely varied. This meta-analysis was conducted to assess the detection accuracy of stool DNA methylation assay in CRC, early stages of CRC (advanced adenoma, non-advanced adenomas) and hyperplastic polyps, separately. We searched MEDLINE, Web of Science, Scopus and Google Scholar databases until May 1, 2016. From 469 publications obtained in the initial literature search, 38 studies were included in the final analysis involving 4867 individuals. The true positive, false positive, true negative and false negative of a stool-based DNA methylation biomarker using all single-gene tests considering a certain gene; regardless of a specific gene were pooled and studied in different categories. The sensitivity of different genes in detecting different stages of CRC ranged from 0% to 100% and the specificities ranged from 73% to 100%. Our results elucidated that SFRP1 and SFRP2 methylation possessed promising accuracy for detection of not only CRC (DOR: 31.67; 95%CI, 12.31-81.49 and DOR: 35.36; 95%CI, 18.71-66.84, respectively) but also the early stages of cancer, adenoma (DOR: 19.72; 95%CI, 6.68-58.25 and DOR: 13.20; 95%CI, 6.01-28.00, respectively). Besides, NDRG4 could be also considered as a significant diagnostic marker gene in CRC (DOR: 24.37; 95%CI, 10.11-58.73) and VIM in adenoma (DOR: 15.21; 95%CI, 2.72-85.10). In conclusion, stool DNA hypermethylation assay based on the candidate genes SFRP1, SFRP2, NDRG4 and VIM could offer potential diagnostic value for CRC based on the findings of this meta-analysis.

DMRFusion: A differentially methylated region detection tool based on the ranked fusion method.

DNA methylation is an important epigenetic modification involved in many biological processes and diseases. Computational analysis of differentially methylated regions (DMRs) could explore the underlying reasons of methylation. DMRFusion is presented as a useful tool for comprehensive DNA methylation analysis of DMRs on methylation sequencing data. This tool is designed base on the integration of several ranking methods; Information gain, Between versus within Class scatter ratio, Fisher ratio, Z-score and Welch's t-test. In this study, DMRFusion on reduced representation bisulfite sequencing (RRBS) data in chronic lymphocytic leukemia cancer displayed 30 nominated regions and CpG sites with a maximum methylation difference detected in the hypermethylation DMRs. We realized that DMRFusion is able to process methylation sequencing data in an efficient and accurate manner and to provide annotation and visualization for DMRs with high fold difference score (p-value and FDR<0.05 and type I error: 0.04).

Review: Diagnostic accuracy of PCR-based detection tests for Helicobacter Pylori in stool samples.

BACKGROUND: Although different methods have been established to detect Helicobacter pylori (H. pylori) infection, identifying infected patients is an ongoing challenge. The aim of this meta-analysis was to provide pooled diagnostic accuracy measures for stool PCR test in the diagnosis of H. pylori infection. METHODS: In this study, a systematic review and meta-analysis were carried out on various sources, including MEDLINE, Web of Sciences, and the Cochrane Library from April 1, 1999, to May 1, 2016. This meta-analysis adheres to the guidelines provided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses report (PRISMA Statement). The clinical value of DNA stool PCR test was based on the pooled false positive, false negative, true positive, and true negative of different genes. RESULTS: Twenty-six of 328 studies identified met the eligibility criteria. Stool PCR test had a performance of 71% (95% CI: 68-73) sensitivity, 96% (95% CI: 94-97) specificity, and 65.6 (95% CI: 30.2-142.5) diagnostic odds ratio (DOR) in diagnosis of H. pylori. The DOR of genes which showed the highest performance of stool PCR tests was as follows: 23S rRNA 152.5 (95% CI: 55.5-418.9), 16S rRNA 67.9 (95%CI: 6.4-714.3), and glmM 68.1 (95%CI: 20.1-231.7). CONCLUSIONS: The sensitivity and specificity of stool PCR test are relatively in the same spectrum of other diagnostic methods for the detection of H. pylori infection. In descending order of significance, the most diagnostic candidate genes using PCR detection were 23S rRNA, 16S rRNA, and glmM. PCR for 23S rRNA gene which has the highest performance could be applicable to detect H. pylori infection.

A robust Correntropy-based method for analyzing multisample aCGH data.

This paper presents a new method for analyzing array comparative genomic hybridization (aCGH) data based on Correntropy. A new formulation based on low-rank aCGH data and Correntropy is proposed and its solution is presented based on Half-Quadratic method. Compared to existing methods, the proposed method is more robust to high corruptions and various kinds of noise. Moreover, it analyzes all aCGH profiles relating to a data set simultaneously. Experimental results illustrate the robustness of the proposed method when the noise is non-Gaussian and show its excellent performance in other cases.

Robust and stable feature selection by integrating ranking methods and wrapper technique in genetic data classification.

High dimensional data increase the dimension of space and consequently the computational complexity and result in lower generalization. From these types of classification problems microarray data classification can be mentioned. Microarrays contain genetic and biological data which can be used to diagnose diseases including various types of cancers and tumors. Having intractable dimensions, dimension reduction process is necessary on these data. The main goal of this paper is to provide a method for dimension reduction and classification of genetic data sets. The proposed approach includes different stages. In the first stage, several feature ranking methods are fused for enhancing the robustness and stability of feature selection process. Wrapper method is combined with the proposed hybrid ranking method to embed the interaction between genes. Afterwards, the classification process is applied using support vector machine. Before feeding the data to the SVM classifier the problem of imbalance classes of data in the training phase should be overcame. The experimental results of the proposed approach on five microarray databases show that the robustness metric of the feature selection process is in the interval of [0.70, 0.88]. Also the classification accuracy is in the range of [91%, 96%].