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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.

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.

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.

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.