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DNA methylation alterations have already been linked to cancer, and their usefulness for therapy and diagnosis has encouraged research into the human epigenome. Several biomarker studies have focused on identifying cancer types individually, yet common cancer and multicancer markers are still underexplored. We used The Cancer Genome Atlas (TCGA) to investigate genome‐wide methylation profiles of 14 different cancer types and developed a three‐step computational approach to select candidate biomarker CpG sites. In total, 1991 pan‐cancer and between 75 and 1803 cancer‐specific differentially methylated CpG sites were discovered. Differentially methylated blocks and regions were also discovered for the first time on such a large scale. Through a three‐step computational approach, a combination of four pan‐cancer CpG markers was identified from these sites and externally validated (AUC = 0.90), maintaining comparable performance across tumor stages. Additionally, 20 tumor‐specific CpG markers were identified and made up the final type‐specific prediction model, which could accurately differentiate tumor types (AUC = 0.87–0.99). Our study highlights the power of the methylome as a rich source of cancer biomarkers, and the signatures we identified provide a new resource for understanding cancer mechanisms on the wider genomic scale with strong applicability in the context of new minimally invasive cancer detection assays. We used The Cancer Genome Atlas to study genome‐wide methylation profiles of 14 different cancer types. We identified and validated a combination of four candidate pan‐cancer methylation CpG biomarkers and 20 candidate cancer type‐specific markers. This highlights the methylome as a rich source of biomarkers which can be used in the context of new minimally invasive cancer detection assays.

Background DNA methylation is an epigenetic mechanism involved in gene regulation and cellular differentiation. Accurate and comprehensive assessment of DNA methylation patterns is thus essential for understanding their role in various biological processes and disease mechanisms. Bisulfite sequencing has long been the default method for analyzing methylation marks due to its single-base resolution, but the associated DNA degradation poses a concern. Although several methods have been proposed to circumvent this issue, there is no clear consensus on which method might be better suited for specific study designs. Results We conducted a comparative evaluation of four DNA methylation detection approaches: whole-genome bisulfite sequencing (WGBS), Illumina methylation microarray (EPIC), enzymatic methyl-sequencing (EM-seq) and third-generation sequencing by Oxford Nanopore Technologies (ONT). DNA methylation profiles were assessed across three human genome samples derived from tissue, cell line, and whole blood. We systematically compared these methods in terms of resolution, genomic coverage, methylation calling accuracy, cost, time, and practical implementation. EM-seq showed the highest concordance with WGBS, indicating strong reliability due to their similar sequencing chemistry. ONT sequencing, while showing lower agreement with WGBS and EM-seq, captured certain loci uniquely and enabled methylation detection in challenging genomic regions. Despite a substantial overlap in CpG detection among methods, each method identified unique CpG sites, emphasizing their complementary nature. Conclusions Our findings underscore the strengths and limitations of current DNA methylation detection methods. EM-seq and ONT emerge as robust alternatives to WGBS and EPIC, offering unique advantages: EM-seq delivers consistent and uniform coverage, while ONT excels in long-range methylation profiling and access to challenging genomic regions. These insights provide practical guidance for method selection based on specific experimental goals.

Background Breast cancer is the most frequent cancer among women worldwide. Biomarkers for early detection and prognosis of these patients are needed. We hypothesized that deafness , autosomal dominant 5 ( DFNA5 ) may be a valuable biomarker, based upon strong indications for its role as tumor suppressor gene and its function in regulated cell death. In this study, we aimed to analyze DFNA5 methylation and expression in the largest breast cancer cohort to date using publicly available data from TCGA, in order to further unravel the role of DFNA5 as detection and/or prognostic marker in breast cancer. We analyzed Infinium HumanMethylation450k data, covering 22 different CpGs in the DFNA5 gene (668 breast adenocarcinomas and 85 normal breast samples) and DFNA5 expression (Agilent 244K Custom Gene Expression: 476 breast adenocarcinomas and 56 normal breast samples; RNA-sequencing: 666 breast adenocarcinomas and 71 normal breast samples). Results DFNA5 methylation and expression were significantly different between breast cancer and normal breast samples. Overall, breast cancer samples showed higher DFNA5 methylation in the putative gene promoter compared to normal breast samples, whereas in the gene body and upstream of the putative gene promoter, the opposite is true. Furthermore, DFNA5 methylation, in 10 out of 22 CpGs, and expression were significantly higher in lobular compared to ductal breast cancers. An important result of this study was the identification of a combination of one CpG in the gene promoter (CpG07504598) and one CpG in the gene body (CpG12922093) of DFNA5 , which was able to discriminate between breast cancer and normal breast samples (AUC = 0.93). This model was externally validated in three independent datasets. Moreover, we showed that estrogen receptor state is associated with DFNA5 methylation and expression. Finally, we were able to find a significant effect of DFNA5 gene body methylation on a 5-year overall survival time. Conclusions We conclude that DFNA5 methylation shows strong potential as detection and prognostic biomarker for breast cancer.

In addition to its implication in hereditary hearing loss, the Gasdermin E (GSDME) gene is also a tumor suppressor involved in cancer progression through programmed cell death. GSDME epigenetic silencing through methylation has been shown in some cancer types, but studies are yet to fully explore its diagnostic/prognostic potential in colorectal cancer on a large‐scale. We used public data from The Cancer Genome Atlas (TCGA) to investigate differences in GSDME methylation and expression between colorectal cancer and normal colorectal tissue, and between left‐ and right‐sided colorectal cancers in 432 samples. We also explored GSDME's diagnostic capacity as a biomarker for colorectal cancer. We observed differential methylation in all 22 GSDME CpGs between tumor and normal tissues, and in 18 CpGs between the left‐ and right‐sided groups. In the cancer tissue, putative promoter probes were hypermethylated and gene body probes were hypomethylated, while this pattern was inversed in normal tissues. Both putative promoter and gene body CpGs correlated well together but formed distinct methylation patterns with the putative promoter exhibiting the most pronounced methylation differences between tumor and normal tissues. Clinicopathological parameters, excluding age, did not show any effect on CpG methylation. Although the methylation of 5 distinct probes was a good predictor of gene expression, we could not identify an association between GSDME methylation and expression in general. Survival analysis showed no association between GSDME methylation and expression on 5‐year patient survival. Through logistic regression, we identified a combination of 2 CpGs, that can discriminate between cancer and normal tissue with high accuracy (AUC = 0.95) irrespective of age and tumor stage. We also validated our model in 3 external methylation datasets, from the Gene Expression Omnibus database, and similar results were reached. Our results suggest that GSDME is a promising biomarker for the detection of colorectal cancer. Aberrant Gasdermin E (GSDME) methylation in cancer makes it a candidate biomarker gene. This is the first study to thoroughly analyze GSDME methylation in the largest colorectal adenocarcinoma patient cohort to date (N = 432) using publicly available data. We identified a combination of CpGs, that can discriminate between colorectal cancer and normal tissue samples with high accuracy (AUC = 0.95). Additionally, we found significant differences in GSDME methylation between left‐ and right‐sided colorectal cancers.

Streptococcus pyogenes is a very important human pathogen, commonly associated with skin or throat infections but can also cause life-threatening situations including sepsis, streptococcal toxic shock syndrome, and necrotizing fasciitis. Various studies involving typing and molecular characterization of S. pyogenes have been published to date; however next-generation sequencing (NGS) studies provide a comprehensive collection of an organism's genetic variation. In this study, the genomes of nine S. pyogenes isolates associated with pharyngitis and skin infection were sequenced and studied for the presence of virulence genes, resistance elements, prophages, genomic recombination, and other genomic features. Additionally, a comparative phylogenetic analysis of the isolates with global clones highlighted their possible evolutionary lineage and their site of infection. The genomes were found to also house a multitude of features including gene regulation systems, virulence factors and antimicrobial resistance mechanisms.

Background Helsmoortel–Van der Aa syndrome is a neurodevelopmental disorder in which patients present with autism, intellectual disability, and frequent extra-neurological features such as feeding and gastrointestinal problems, visual impairments, and cardiac abnormalities. All patients exhibit heterozygous de novo nonsense or frameshift stop mutations in the Activity-Dependent Neuroprotective Protein ( ADNP ) gene, accounting for a prevalence of 0.2% of all autism cases worldwide. ADNP fulfills an essential chromatin remodeling function during brain development. In this study, we investigated the cerebellum of a died 6-year-old male patient with the c.1676dupA/p.His559Glnfs*3 ADNP mutation. Results The clinical presentation of the patient was representative of the Helsmoortel–Van der Aa syndrome. During his lifespan, he underwent two liver transplantations after which the child died because of multiple organ failure. An autopsy was performed, and various tissue samples were taken for further analysis. We performed a molecular characterization of the cerebellum, a brain region involved in motor coordination, known for its highest ADNP expression and compared it to an age-matched control subject. Importantly, epigenome-wide analysis of the ADNP cerebellum identified CpG methylation differences and expression of multiple pathways causing neurodevelopmental delay. Interestingly, transcription factor motif enrichment analysis of differentially methylated genes showed that the ADNP binding motif was the most significantly enriched. RNA sequencing of the autopsy brain further identified downregulation of the WNT signaling pathway and autophagy defects as possible causes of neurodevelopmental delay. Ultimately, label-free quantification mass spectrometry identified differentially expressed proteins involved in mitochondrial stress and sirtuin signaling pathways amongst others. Protein–protein interaction analysis further revealed a network including chromatin remodelers (ADNP, SMARCC2, HDAC2 and YY1), autophagy-related proteins (LAMP1, BECN1 and LC3) as well as a key histone deacetylating enzyme SIRT1, involved in mitochondrial energy metabolism. The protein interaction of ADNP with SIRT1 was further biochemically validated through the microtubule-end binding proteins EB1/EB3 by direct co-immunoprecipitation in mouse cerebellum, suggesting important mito-epigenetic crosstalk between chromatin remodeling and mitochondrial energy metabolism linked to autophagy stress responses. This is further supported by mitochondrial activity assays and stainings in patient-derived fibroblasts which suggest mitochondrial dysfunctions in the ADNP deficient human brain. Conclusion This study forms the baseline clinical and molecular characterization of an ADNP autopsy cerebellum, providing novel insights in the disease mechanisms of the Helsmoortel–Van der Aa syndrome. By combining multi-omic and biochemical approaches, we identified a novel SIRT1-EB1/EB3-ADNP protein complex which may contribute to autophagic flux alterations and impaired mitochondrial metabolism in the Helsmoortel–Van der Aa syndrome and holds promise as a new therapeutic target. Graphical abstract Highlights The ADNP patient mutation affects genome-wide methylation and leads to neurodevelopmental abnormalities. The ADNP brain transcriptome reveals impaired neuronal differentiation and cellular homeostasis by aberrant signaling of the WNT pathway and autophagy process. ADNP forms a complex with SIRT1 through the microtubule end-binding proteins EB1 and EB3. Mitochondrial gene expression is impaired in the ADNP brain and patient-derived cellular models.

Background Pleural mesothelioma (PM) is a rare and aggressive cancer type, typically diagnosed at advanced stages. Distinguishing PM from other lung diseases is often challenging. There is an urgent need for biomarkers that can enable early detection. Interest in the field of epigenetics has increased, particularly in the context of tumour development and biomarker discovery. This study aims to identify specific changes in DNA methylation from healthy pleural tissue to PM and to compare these methylation patterns with those found in other lung diseases. Results EPIC methylation array data (850 K) were generated for 11 PM and 29 healthy pleura in-house collected samples. This is the first time such a large dataset of healthy pleura samples has been generated. Additional EPIC methylation array data (850 K) for pleural mesothelioma and other lung-related diseases were downloaded from public databases. We conducted pairwise differential methylation analyses across all tissue types, which facilitated the identification of significantly differentially methylated CpG sites. Extensive differential methylation between PM and healthy pleura was observed, identifying 81,968 differentially methylated CpG sites across all genomic regions. Among these, five CpG sites located within four genes ( MIR21 , RNF39 , SPEN and C1orf101 ) exhibited the most significant and pronounced methylation differences between PM and healthy pleura. Moreover, our analysis delineated distinct methylation patterns specific to PM subtypes. Finally, the methylation profiles of PM were distinctly different from those of other lung cancers, enabling accurate differentiation. Conclusions DNA methylation analyses provide a robust method for distinguishing PM from healthy pleural tissues, and specific methylation patterns exist within PM subtypes. These methylation differences underscore their importance in understanding disease progression and may serve as viable biomarkers or therapeutic targets. Moreover, differential methylation patterns between PM and other lung cancers highlights its diagnostic potential. These findings necessitate further translational studies to explore their clinical applications.

Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide, but its pathophysiological mechanisms remain elusive. It is a progressive disease, encompassing hepatic steatosis, steatohepatitis with (out) fibrosis, and ultimately cirrhosis and hepatocellular carcinoma. DNA methylation (DNAm) is dysregulated in MASLD and may play a central role in its pathogenesis. Additionally, aging is associated with MASLD and shares common processes of chronic inflammation and oxidative stress. Therefore, this study focuses on DNAm changes in relation to MASLD progression and epigenetic age acceleration (EAA). Results Liver biopsies from 22 individuals with varying MASLD status were analyzed using Infinium MethylationEPIC BeadChip arrays. Strikingly, progression of MASLD was characterized by gradual DNAm changes, revealing multiple associated KEGG pathways. Additionally, Horvath’s EAA significantly correlated with MASLD stage and individual histological MASLD parameters while LiverClock’s EAA correlated only with MASLD stage. In contrast, both Horvath’s intrinsic EAA and HepClock’s EAA showed no significant correlations. Integrative analyses, leveraging both gradual MASLD and Horvath’s EAA DNAm signatures, gene expression ( n  = 118), and a MASLD-specific transcriptional regulatory network, identified (regulon-specific) transcription factors implicated in MASLD and EAA progression, representing a transcription factor-network of redox (ferroptosis), immune, and metabolic/endocrine related epigenetic processes. Conclusion Gradual DNAm changes were found to align with progression of MASLD and EAA, with EAA a potential nonbiased quantitative biomarker for MASLD. Integrative analysis highlighted potential new therapeutic transcription factor targets, with special emphasis on AEBP1 and emerging nuclear receptors including CAR(NR1I3), MR(NR3C2), GR(NR3C1), and ESRRG, underscoring the potential of epigenetic redox-metabolic therapies for MASLD. Graphical abstract

Background Biomarker discovery in colorectal cancer has mostly focused on methylation patterns in normal and colorectal tumor tissue, but adenomas remain understudied. Therefore, we performed the first epigenome-wide study to profile methylation of all three tissue types combined and to identify discriminatory biomarkers. Results Public methylation array data (Illumina EPIC and 450K) were collected from a total of 1 892 colorectal samples. Pairwise differential methylation analyses between tissue types were performed for both array types to “double evidence” differentially methylated probes (DE DMPs). Subsequently, the identified DMPs were filtered on methylation level and used to build a binary logistic regression prediction model. Focusing on the clinically most interesting group (adenoma vs carcinoma), we identified 13 DE DMPs that could effectively discriminate between them (AUC = 0.996). We validated this model in an in-house experimental methylation dataset of 13 adenomas and 9 carcinomas. It reached a sensitivity and specificity of 96% and 95%, respectively, with an overall accuracy of 96%. Our findings raise the possibility that the 13 DE DMPs identified in this study can be used as molecular biomarkers in the clinic. Conclusions Our analyses show that methylation biomarkers have the potential to discriminate between normal, precursor and carcinoma tissues of the colorectum. More importantly, we highlight the power of the methylome as a source of markers for discriminating between colorectal adenomas and carcinomas, which currently remains an unmet clinical need.