Differential Methylation Analysis Research Articles

Exploring extrahepatic metastasis of hepatocellular carcinoma based on methylation driver genes and establishing a prognostic model for hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC), theseventh most common cancer worldwide, is characterized by a high mortality rate, advanced diagnosis, and susceptibility to extrahepatic metastasis. Numerous studies have shown that DNA methylation is a crucial factor in epigenetic modifications and regulation of carcinogenesis. MethodsHCC patient data were sourced from the TCGA dataset as a training set, while GSE116174 was used as an external validation set for verification. Differential methylation and expression analyses were performed on HCC samples with and without extrahepatic metastasis. In the intersecting genes, the relationship between methylation and expression levels of the intersecting genes was analyzed. Genes with a correlation coefficient≥|0.30| and P<0.05 were identified as methylation driver genes. Cox regression analysis was conducted to identify genes associated with HCC prognosis and establish a risk score. Subsequently, a prognostic model was established and validated using Cox regression analysis incorporating the risk score and other clinical factors. Using immunohistochemistry to evaluate the expression of DHX58 and EIF5A2 in HCC tissues with and without extrahepatic metastasis. Immunoinfiltration analysis was performed on the HCC samples using CIBERSORT. ResultsOur research identified eight methylation driver genes for HCC extrahepatic metastasis, of which two genes (DHX58 and EIF5A2) were associated with HCC patient prognosis. And the study further constructed and validated the risk score and prognostic model. Immunoinfiltration analysis showed that M0 macrophage abundance was correlated with the prognosis of HCC patients. Immunohistochemistry revealed differences in DHX58 and EIF5A2 expression between HCC tissues with and without extrahepatic metastasis, consistent with our bioinformatics findings.

Prostate cancer subtyping and differential methylation analysis based on the ETS family of transcription factors fusion genes

Prostate cancer (PCa) is a highly heterogeneous disease, encompassing various molecular and clinical pathological subtypes. Fusion genes play a facilitating role in the occurrence and progression of PCa. We categorized PCa samples into the ETS family of transcription factors fusion positive and fusion negative subtypes based on fusion genes. This subtyping method is closely related to the epigenomic DNA methylation profiles of PCa, with each sample cluster including more than 85% of the patients. We conducted an analysis of the distribution of the ETS family fusion genes on chromosomes, fusion modes within reading frames, and predictions of structural domains. Among these, the highest frequency of the ETS family related fusion genes occurred on chromosome 21. Compared to the parental genes, fusion genes exhibited new structural domains, such as IG_like, and the most common fusion mode was out-of-frame fusion. The correlation between the methylation levels of hypermethylated CpG sites and the expression levels of their corresponding mRNAs indicates that CD8A and B3GNT5 (with correlations of − 0.388 and − 0.253, respectively) could serve as potential prognostic markers for PCa.

DMAP2: A Pipeline for Analysis of Whole-Genome-Scale DNA Methylation Sequencing Data.

DNA methylation is well-established as a major epigenetic mechanism that can control gene expression and is involved in both normal development and disease. Analysis of high-throughput-sequencing-based DNA methylation data is a step toward understanding the relationship between disease and phenotype. Analysis of CpG methylation at single-base resolution is routinely done by bisulfite sequencing, in which methylated Cs remain as C while unmethylated Cs are converted to U, subsequently seen as T nucleotides. Sequence reads are aligned to the reference genome using mapping tools that accept the C-T ambiguity. Then, various statistical packages are used to identify differences in methylation between (groups of) samples. We have previously developed the Differential Methylation Analysis Pipeline (DMAP) as an efficient, fast, and flexible tool for this work, both for whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite sequencing (RRBS). The protocol described here includes a series of scripts that simplify the use of DMAP tools and that can accommodate the wider range of input formats now in use to perform analysis of whole-genome-scale DNA methylation sequencing data in various biological and clinical contexts. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: DMAP2 workflow for whole-genome bisulfite sequencing (WGBS) and reduced-representation bisulfite sequencing (RRBS).

Sexually dimorphic DNA methylation and gene expression patterns in human first trimester placenta

BackgroundFetal sex and placental development impact pregnancy outcomes and fetal–maternal health, but the critical timepoint of placenta establishment in first trimester is understudied in human pregnancies.MethodsPregnant subjects were recruited in late first trimester (weeks 10–14) at time of chorionic villus sampling, a prenatal diagnostic test. Leftover placenta tissue was collected and stored until birth outcomes were known, then DNA and RNA were isolated from singleton, normal karyotype pregnancies resulting in live births. DNA methylation was measured with the Illumina Infinium MethylationEPIC BeadChip array (n = 56). Differential methylation analysis compared 25 females versus 31 males using a generalized linear model on 743,461 autosomal probes. Gene expression sex differences were analyzed with RNA-sequencing (n = 74). An integrated analysis was performed using linear regression to correlate gene expression and DNA methylation in 51 overlapping placentas.ResultsMethylation analysis identified 151 differentially methylated probes (DMPs) significant at false discovery rate < 0.05, including 89 (59%) hypermethylated in females. Probe cg17612569 (GABPA, ATP5J) was the most significant CpG site, hypermethylated in males. There were 11 differentially methylated regions affected by fetal sex, with transcription factors ZNF300 and ZNF311 most significantly hypermethylated in males and females, respectively. RNA-sequencing identified 152 genes significantly sexually dimorphic at false discovery rate < 0.05. The 151 DMPs were associated with 18 genes with gene downregulation (P < 0.05) in the direction of hypermethylation, including 2 genes significant at false discovery rate < 0.05 (ZNF300 and CUB and Sushi multiple domains 1, CSMD1). Both genes, as well as Family With Sequence Similarity 228 Member A (FAM228A), showed significant correlation between DNA methylation and sexually dimorphic gene expression, though FAM228A DNA methylation was less sexually dimorphic. Comparison with other sex differences studies found that cg17612569 is male-hypermethylated across gestation in placenta and in human blood up to adulthood.ConclusionsOverall, sex dimorphic differential methylation with associated differential gene expression in the first trimester placenta is small, but there remain significant genes that may be regulated through methylation leading to differences in the first trimester placenta.

Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Transgenerational Effect of Maternal Pre-Pregnancy Obesity.

Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the transgenerational effect of maternal obesity, we conducted a multi-omics study, using DNA methylation and gene expression in the CD34+/CD38-/Lin-umbilical cord blood hematopoietic stem cells (uHSCs) and metabolomics of the cord blood, all from a multi-ethnic cohort (n=72) from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (collected between 2016 and 2018). Differential methylation (DM) analysis unveiled a global hypermethylation pattern in the maternal pre-pregnancy obese group (BH adjusted p<0.05), after adjusting for major clinical confounders. Comprehensive functional analysis showed hypermethylation in promoters of genes involved in cell cycle, protein synthesis, immune signaling, and lipid metabolism. Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multi-omics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. This study reveals the significant correlation between pre-pregnancy maternal obesity and multi-omics level molecular changes in the uHSCs of offspring, particularly in DNA methylation.

Differential Expression of mRNA in Peripheral Blood Mononuclear Cells May Predict Postoperative Atrial Fibrillation in Coronary Artery Bypass Surgery Patients.

Postoperative Atrial Fibrillation (POAF) frequently follows Coronary Artery Bypass Grafting (CABG) surgery. This prospective study investigates genes linked to POAF in CABG patients, aiming to create a predictive model. Employing differential gene and methylation analyses, the study identified four genes (WARS2, CKAP2, CHI3L1, HSD17B6) associated with POAF. Preoperative plasma samples and clinical data were collected from 139 CABG patients, categorized into POAF (+) (43) and POAF (-) (96). Real-time quantitative PCR assessed gene expression, and a predictive model using the LASSO method demonstrated robust performance, with AUC values of 0.8895 in the training set and 0.7840 in the test set. This pioneering study integrates genomics and clinical data, suggesting WARS2, CKAP2, and CHI3L1 as potential indicators for POAF prediction.

Epigenome-Wide Association Study of Depressive Symptoms in Black Women in the InterGEN Study.

(1) The prevalence of depression is two times higher in women than men. Black women have an increased risk of depression due to stressors such as low socioeconomic status and perceived discrimination. Depression is likely influenced by both genetic and environmental factors. Psychosocial stressors can influence DNA methylation (DNAm), leading to changes in gene expression and ultimately, depression. The objective of this study was to examine associations between DNAm and depressive symptoms in Black women. (2) This study was a secondary analysis of data from the Intergenerational Impact of Genetic and Psychological Factors on Blood Pressure (InterGEN) Study. Perceived discrimination was assessed using Krieger's Experiences of Discrimination and Waelde's Race-Related Events Scale, and participants were screened for depressive symptoms with the Beck Depression Inventory. Raw data from saliva samples were analyzed using the Illumina Infinium Epic (850 K) BeadChip and then preprocessed in RStudio. (3) Differential methylation analysis identified DNAm sites and regions associated with depressive symptoms. Six DNAm sites had a q-value less than 0.05. Additionally, of the 25 regions identified, 12 were associated with neurological diseases or disorders. (4) These findings suggest that there is a neurological component to depression, which should be considered during treatment.

O-028 DNA methylation testing with cervical secretion can serve as a non-invasive biomarker for predicting successful implantation in frozen-thawed embryo transfer

Abstract Study question Is cervical secretion gene methylation profile associated with embryo implantation outcome, and can be used as a non-invasive biomarker for pregnancy prediction in FET cycles? Summary answer Cervical secretion gene methylation profiles show significant associations with implantation outcomes, and can serve as a non-invasive predictor of successful pregnancy in FET cycles. What is known already Cause of implantation failure remains a black box in reproductive medicine, and the exact mechanism of how endometrial receptivity is still behind the curtain. Epigenetic modifications including DNA methylation regulate gene expression and may alter endometrium receptivity. Endometrial DNA methylation changes vigorously only during the reconstruction of a new endometrium throughout each menstrual cycle, suggesting a unique methylation landscape in each regenerated endometrium. Cervical secretions contain implantation-related cytokines and genetic material from endometrium, and the DNA methylation profiles of cervical secretions is highly correlated with endometrium. Therefore, cervical secretions can be used as non-invasive surrogates for investigating endometrial condition. Study design, size, duration This multi-centered, case-control study enrolled 72 infertile females who entered the FET cycle with at least one high-quality blastocyst available between Aug 2017 and Dec 2022. Embryos assessed by Gardner scoring system with grade 3BB or above were defined as high-quality. Cervical secretions were collected on the day right before embryo transfer, and then DNA methylation profile were assessed. Participants/materials, setting, methods These females aged between 20-45 years-old with regular menstruation cycle (24-35 days) and normal BMI (17.5–32.0 kg/m2). Uterine cavity lesions and hydrosalpinx without treatment, adenomyosis, large uterine myoma that distorted endometrium, and thin endometrium (&amp;lt;7mm) were excluded. The association between the cervical secretion gene methylation profile and pregnancy outcome was evaluated via quantitative methylation-specific PCR (qMSP), methylation array, and genome-wide analysis. Different machine learning algorithms were utilized for differential methylation analysis and implantation prediction. Main results and the role of chance Cervical secretion DNA methylome differed between receptive (pregnant) and non-receptive (non-pregnant) FET cycle with genome-wide methylation array. The accuracy of pregnancy outcome prediction using top 2000 differential methylation probes (DMPs) in promoter region can be as high as 86% in all tested sample and 96.4% in pregnant samples. Bioinformatic analysis of six selected candidate genes related to implantation from literature review showed significant differences in DNA methylation between receptive and non-receptive endometrium. The methylation level of the six candidate genes with eight regions were measured by the qMSP platform and showed varying degrees of differential methylation status between the pregnancy and non-pregnancy groups. The AUCs for a single gene/region ranged from 0.53–0.67 for implantation prediction. Ten different machine learning models were applied and the results showed that the combination of candidate genes’ DNA methylation profiles could differentiate pregnant from non-pregnant samples with an accuracy as high as 86.67% and an AUC of 0.81 by logistic regression model. The best model from different machine learning algorithms using selected DMPs of genes related to window of implantation (WOI) can predict ongoing pregnancy with accuracy rates as high as 86.78% and AUC values up to 0.91. Limitations, reasons for caution The results obtained from a relatively small cohort size, and the timing of sample collection is on the day right before embryo transfer. Further investigation of methylation profiles in late follicular phase along with large sample size with euploid embryo transfer are mandatory for validation and clinical applications. Wider implications of the findings We firstly demonstrated the feasibility of cervical secretion methylation profile as a non-invasive testing for pregnancy prediction. The promising result may contribute to predicting endometrial receptivity for cycles with a favorable endometrium and maximize the successful pregnancy rate of embryo transfer. Trial registration number not applicable

O-306 Sperm DNA methylome changes in testicular cancer patients following chemotherapy treatment

Abstract Study question Does chemotherapy (CT) treatment for testicular cancer induce sperm methylome changes? Summary answer The analysis of 27 pre- and post-CT samples from testicular cancer survivors identified 65 differentially methylated regions. What is known already Research has shown that cancer treatment can cause DNA damage and aneuploidy in sperm, even years after the treatment. However, there is limited information available on the impact of CT on the sperm epigenome, and most of the information has been obtained from rodent models. Two recent studies, one involving one patient and the other involving three, have investigated this topic in humans. Both studies have suggested that cancer survivors may experience changes in sperm’s DNA methylation following oncological treatment. Study design, size, duration This is a multicenter prospective study. We included 27 patients [31.23 years (CI 95% 29.29-33.17)] diagnosed with testicular cancer who cryopreserved semen before CT (2009-2020). After receiving oncological treatment, these patients recovered their spermatogenesis. Patients who agreed to participate in this IRB-approved study, donated a second semen sample after their recovery, which was also frozen. Participants/materials, setting, methods Semen samples were thawed; sperm DNA was isolated and subsequently hybridized using the Human Infinitum DNA Methylation 850K EPIC BeadChip array platform (Illumina). A paired differential methylation analysis was performed between pre-CT and post-CT samples from the same patient, fitting a linear model of the methylated positions and regions. Minfi and DMRcate R packages were used to find differentially methylated positions (DMPs) and regions (DMRs). Subsequently, the functional impact of involved genes was evaluated. Main results and the role of chance DNA methylation data were available for the 54 sperm samples (27 collected before CT and 27 after CT treatment). Results were controlled by age and time elapsed between the cryopreservation of pre-CT and post-CT samples, which was 4.01 years (CI 95%: 3.42-5.64). After filtering for initial QC, the assessment was done on 445.000 CpGs per sample. The analysis revealed that a total of 228 CpG positions showed a tendency towards statistical significance. DMRcate identified 65 DMRs (40 hypomethylated and 25 hypermethylated) that reached a smoothed FDR p-value &amp;lt; 0.05 when compared post-CT samples to pre-CT within the same patient. We observed that 61 genes were located within DMRs, being TBC1D20, EN1, HOXB1 and SCNN1A the ones within the most significant DMRs (min-smoothed-FDR &amp;lt; 0.001). The functional enrichment analysis of putatively affected genes revealed that the biological processes more affected were: Regulation of macromolecule biosynthetic process, hormone receptor binding, signaling pathways regulating pluripotency of stem cells and pathways in cancer. Limitations, reasons for caution Epigenetics may be affected during cryopreservation and thawing. To ensure comparability, we also froze the samples after undergoing the CT process. Due to the high-throughput nature of the study and the limited access to samples, we only achieved marginal significance. Therefore, our results should be further validated. Wider implications of the findings This is the largest study suggesting distinct sperm DNA methylation signatures in testicular cancer survivors. Our findings emphasize the importance of counseling cancer patients in pretreatment sample collection and may explain the reported increase in birth defects after treatment. Trial registration number NOT APPLICABLE

Current evidence supporting associations of DNA methylation measurements with survivorship burdens in cancer survivors: A scoping review.

Identifying reliable biomarkers that reflect cancer survivorship symptoms remains a challenge for researchers. DNA methylation (DNAm) measurements reflecting epigenetic changes caused by anti-cancer therapy may provide needed insights. Given lack of consensus describing utilization of DNAm data to predict survivorship issues, a review evaluating the current landscape is warranted. Provide an overview of current studies examining associations of DNAm with survivorship burdens in cancer survivors. A literature review was conducted including studies if they focused on cohorts of cancer survivors, utilized peripheral blood cell DNAm data, and evaluated the associations of DNAm and survivorship issues. A total of 22 studies were identified, with majority focused on breast (n = 7) or childhood cancer (n = 9) survivors, and half studies included less than 100 patients (n = 11). Survivorship issues evaluated included those related to neurocognition (n = 5), psychiatric health (n = 3), general wellness (n = 9), chronic conditions (n = 5), and treatment specific toxicities (n = 4). Studies evaluated epigenetic age metrics (n = 10) and DNAm levels at individual CpG sites or regions (n = 12) for their associations with survivorship issues in cancer survivors along with relevant confounding factors. Significant associations of measured DNAm in the peripheral blood samples of cancer survivors and survivorship issues were identified. Studies utilizing epigenetic age metrics and differential methylation analysis demonstrated significant associations of DNAm measurements with survivorship burdens. Associations were observed encompassing diverse survivorship outcomes and timeframes relative to anti-cancer therapy initiation. These findings underscore the potential of these measurements as useful biomarkers in survivorship care and research.

Novel bioinformatic analyses of somatic cell contamination in sperm samples

The assessment of epigenetic profiles in sperm is sensitive to somatic cell contamination, which can influence methylation signals at gene promoters. This contamination is particularly problematic in the assessment of DNA methylation in samples with low sperm counts, where fractional amounts of somatic cell DNA can lead to significant shifts in measured methylation state. In this study, a new method of detecting possible somatic cell contamination is proposed through two multi-region bioinformatic models: a traditional differential methylation analysis and a machine learning logistic regression model. These models were trained on publicly available sperm (n = 489) and blood (n = 1029) DNA methylation array data and tested on a contamination set, wherein the sperm of four donors with normal sperm counts were run on a 450k methylation array with four permutations each, including pure blood, half blood and half sperm by DNA concentration, half blood and half sperm by cell count, and pure sperm (n = 16). The DMR and logistic regression model classified the contamination testing set with 100% and 94% accuracy, respectively. These new methods of detecting the effects of somatic cell contamination allow for more accurate differentiation between epigenetic profiles that contain a biological somatic-like shift and those that have somatic-like signatures because of contamination.

Epigenetic differences between wild and cultivated grapevines highlight the contribution of DNA methylation during crop domestication

The domestication process in grapevines has facilitated the fixation of desired traits. Nowadays, vegetative propagation through cuttings enables easier preservation of these genotypes compared to sexual reproduction. Nonetheless, even with vegetative propagation, various phenotypes are often present within the same vineyard due to the accumulation of somatic mutations. These mutations are not the sole factors influencing phenotype. Alongside somatic variations, epigenetic variation has been proposed as a pivotal player in regulating phenotypic variability acquired during domestication. The emergence of these epialleles might have significantly influenced grapevine domestication over time. This study aims to investigate the impact of domestication on methylation patterns in cultivated grapevines. Reduced-representation bisulfite sequencing was conducted on 18 cultivated and wild accessions. Results revealed that cultivated grapevines exhibited higher methylation levels than their wild counterparts. Differential Methylation Analysis between wild and cultivated grapevines identified a total of 9955 differentially methylated cytosines, of which 78% were hypermethylated in cultivated grapevines. Functional analysis shows that core methylated genes (consistently methylated in both wild and cultivated accessions) are associated with stress response and terpenoid/isoprenoid metabolic processes. Meanwhile, genes with differential methylation are linked to protein targeting to the peroxisome, ethylene regulation, histone modifications, and defense response. Collectively, our results highlight the significant roles that epialleles may have played throughout the domestication history of grapevines.

Exploring early DNA methylation alterations in type 1 diabetes: implications of glycemic control.

Prolonged hyperglycemia causes diabetes-related micro- and macrovascular complications, which combined represent a significant burden for individuals living with diabetes. The growing scope of evidence indicates that hyperglycemia affects the development of vascular complications through DNA methylation. A genome-wide differential DNA methylation analysis was performed on pooled peripheral blood DNA samples from individuals with type 1 diabetes (T1D) with direct DNA sequencing. Strict selection criteria were used to ensure two age- and sex-matched groups with no clinical signs of chronic complications according to persistent mean glycated hemoglobin (HbA1c) values over 5 years: HbA1c<7% (N=10) and HbA1c>8% (N=10). Between the two groups, 8385 differentially methylated CpG sites, annotated to 1802 genes, were identified. Genes annotated to hypomethylated CpG sites were enriched in 48 signaling pathways. Further analysis of key CpG sites revealed four specific regions, two of which were hypermethylated and two hypomethylated, associated with long non-coding RNA and processed pseudogenes. Prolonged hyperglycemia in individuals with T1D, who have no clinical manifestation of diabetes-related complications, is associated with multiple differentially methylated CpG sites in crucial genes and pathways known to be linked to chronic complications in T1D.

Causal impact of DNA methylation on refracture in elderly individuals with osteoporosis – a prospective cohort study

BackgroundOsteoporotic vertebral compression fractures (OVCF) in the elderly increase refracture risk post-surgery, leading to higher mortality rates. Genome-wide association studies (GWAS) have identified susceptibility genes for osteoporosis, but the phenotypic variance explained by these genes has been limited, indicating the need to explore additional causal factors. Epigenetic modifications, such as DNA methylation, may influence osteoporosis and refracture risk. However, prospective cohorts for assessing epigenetic alterations in Chinese elderly patients are lacking. Here, we propose to conduct a prospective cohort study to investigate the causal network of DNA polymorphisms, DNA methylation, and environmental factors on the development of osteoporosis and the risk of refracture.MethodsWe will collect vertebral and peripheral blood from 500 elderly OVCF patients undergoing surgery, extract DNA, and generate whole genome genotype data and DNA methylation data. Observation indicators will be collected and combined with one-year follow-up data. A healthy control group will be selected from a natural population cohort. Epigenome-wide association studies (EWAS) of osteoporosis and bone mineral density will be conducted. Differential methylation analysis will compare candidate gene methylation patterns in patients with and without refracture. Multi-omics prediction models using genetic variants and DNA methylation sites will be built to predict OVCF risk.DiscussionThis study will be the first large-scale population-based study of osteoporosis and bone mineral density phenotypes based on genome-wide data, multi-time point methylation data, and phenotype data. By analyzing methylation changes related to osteoporosis and bone mineral density in OVCF patients, the study will explore the feasibility of DNA methylation in evaluating postoperative osteoporosis intervention effects. The findings may identify new molecular markers for effective anti-osteoporosis treatment and inform individualized prevention and treatment strategies.Trial registrationchictr.org.cn ChiCTR2200065316, 02/11/2022.

Multi-omics profiling the genomic origin and adeno-squamous transdifferentiation mechanism in adenosquamous carcinoma.

e20032 Background: Adenosquamous carcinoma (ASC) represents a rare subtype (0.4-4%) of non-small-cell lung cancer (NSCLC), with its multi-omic landscape yet to be fully elucidated. This study aims to explore the genomic origins and molecular mechanisms of adeno-squamous transdifferentiation in lung ASC through comprehensive multi-omics. Methods: We analyzed 69 samples from 31 lung ASC patients, including 16 FFPE samples with microdissected paired adenocarcinoma (AC) and squamous cell carcinoma (SCC) components, and additionally, incorporated pure Lung Adenocarcinoma (LUAD, n = 51) and pure Squamous Cell Carcinoma (LUSC, n = 28) for comparative analysis. Techniques included whole-exome sequencing, whole-genome methylation profiling, transcriptomics, single-cell RNA sequencing, and spatial transcriptomics. Results: AC and SCC exhibited similar genomic mutation landscapes, indicating a common genomic origin for AD and SCC. The SCC component in ASC displayed higher homologous recombination repair deficiency (HRD) and a notable 16p13.3 deletion in 31% of ASC transformations. Differential methylation analysis highlighted TP63's lower methylation in SCC, correlating negatively with RNA expression (P &lt; 0.001), indicative of methylation-driven regulation. DMR binding motif analysis revealed hypo-methylation in p63, p53, and AP-1 motifs, and hyper-methylation in MKX2-1, FOXA1/2 motifs (P &lt; 0.05). The transition from AC to SCC in ASC was marked by upregulated MYC signaling, proliferation, and DNA repair, with EMT changes not methylation-driven. Metabolic pathways were upregulated, and EMT/KRAS pathways downregulated in SCC compared to LUSC. Immune infiltration in LUAD-AC-ASC transformation showed an initial increase and subsequent decrease, transitioning from immune-infiltrated and immune-excluded to immune-desert phenotypes. IL36G of the IL1 family was highly expressed in the SCC component and strongly correlated with the squamous marker P63 (Pearson r = 0.72, P &lt; 0.001). Single-cell RNA-seq data demonstrated a significant increase in IL36G in squamous cells of lung ASC. In vivo models showed adeno-squamous differentiation in adenocarcinoma cells expressing IL36G. Conclusions: This study provides a comprehensive multi-omic insight into the complex genomic and epigenomic landscape of ASC, highlighting a shared genomic origin and distinct molecular mechanisms in the adeno-squamous transdifferentiation process. The findings, particularly the role of TP63 methylation and IL36G expression in the SCC component, offer novel insights into the molecular heterogeneity of ASC. These results not only deepen our understanding of ASC but also pave the way for future investigations into targeted therapies that could potentially exploit these molecular distinctions for more effective treatment strategies in ASC.

DNA Methylation in the Anti-Mullerian Hormone Gene and the Risk of Disease Activity in Multiple Sclerosis.

Multiple sclerosis (MS) has a complex pathobiology, with genetic and environmental factors being crucial players. Understanding the mechanisms underlying heterogeneity in disease activity is crucial for tailored treatment. We explored the impact of DNA methylation, a key mechanism in the genetics-environment interplay, on disease activity in MS. Peripheral immune methylome profiling using Illumina Infinium MethylationEPIC BeadChips was conducted on 249 untreated relapsing-remitting MS patients, sampled at the start of disease-modifying treatment (DMT). A differential methylation analysis compared patients with evidence of disease activity (EDA) to those with no evidence of disease activity (NEDA) over 2 years from DMT start. Utilizing causal inference testing (CIT) and Mendelian randomization (MR), we sought to elucidate the relationships between DNA methylation, gene expression, genetic variation, and disease activity. Four differentially methylated regions (DMRs) were identified between EDA and NEDA. Examining the influence of single nucleotide polymorphisms (SNPs), 923 variants were found to account for the observed differences in the 4 DMRs. Importantly, 3 out of the 923 SNPs, affecting DNA methylation in a DMR linked to the anti-Mullerian hormone (AMH) gene, were associated with disease activity risk in an independent cohort of 1,408 MS patients. CIT and MR demonstrated that DNA methylation in AMH acts as a mediator for the genetic risk of disease activity. This study uncovered a novel molecular pathway implicating the interaction between DNA methylation and genetic variation in the risk of disease activity in MS, emphasizing the role of sex hormones, particularly the AMH, in MS pathobiology. ANN NEUROL 2024;96:289-301.

Abstract PO2-01-08: Differential HOXB13 gene expression and promoter methylation analysis in breast cancer

Abstract Background: The Breast Cancer Index (BCI) is a gene expression-based signature comprising two functional biomarker panels: the Molecular Grade Index (MGI) and BCI (H/I), which is a ratio of the HOXB13 and IL17BR gene expression. Several studies have shown that BCI (H/I) is a predictive biomarker for extended adjuvant endocrine therapy benefit in hormone receptor-positive (HR+) early-stage breast cancer. Molecular mechanisms underlying endocrine responsiveness remain to be fully explored. The objective of this analysis is to evaluate the correlation between gene expression and methylation in breast cancer patients displaying different HOXB13 mRNA expression levels from The Cancer Genome Atlas (TCGA) project. Methods: Data from methylation microarrays and mRNA sequencing were examined in combination with clinical metadata from 1,095 TCGA breast cancer patients. After removing outliers, HR+ samples were ordered based on their HOXB13 expression levels and the top 10% of samples (HOXB13-high; n=95) and bottom 10% (HOXB13-low; n=95) were selected for further analysis. Differentially expressed genes (DEGs) were determined by comparing all protein-coding genes between the HOXB13-high and HOXB13-low sets, based on an absolute log-fold change above 2 and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Among the subset of samples with matching methylation data (n=60 for the HOXB13-high group and n=48 for the HOXB13-low group), hypo-methylated and hyper-methylated differentially methylated probes (DMPs) were identified based on at least 10% change of β (i.e., average promoter methylation) and a Benjamini-Hochberg adjusted p-value threshold of 0.01. Motif and transcription factor (TF) enrichment analyses were identified at a minimum incidence of 10 and a lower boundary of 1.5 for the 95% confidence interval of the odds ratio. Results: Our analysis identified a total of 613 DEGs between the HOXB13-high and HOXB13-low groups. Gene ontology analysis of DEGs revealed a statistically significant enrichment of genes associated with PTK6 regulated cell cycle, oncogene-induced cellular senescence, and regulation of T cell activation. Analysis of protein-protein interactions of differentially expressed genes revealed differences in key biological processes including changes in cell cycle regulation, p53 pathway, and IL-17 signaling. A total of 5,295 hyper-methylated and 580 hypo-methylated DMPs were discovered when comparing HOXB13-high and HOXB13-low samples. Changes in global methylation patterns enabled classification of breast cancer samples with high or low HOXB13 expression with an accuracy of 0.90. Reconstruction of gene regulatory networks from DNA methylation and transcriptome profiles elucidated significant pairs of DMPs and differentially expressed genes. Specifically, a total of 197 differentially expressed genes were attributed to hyper-methylated DMPs, including genes involved in response to estrogen, activation of HOX genes during differentiation, and drug-mediated inhibition of CDK4/6 activity. Additionally, 8 differentially expressed genes were attributed to hypo-methylated DMPs and were associated with proteasomal protein catabolic processes. Lastly, motif analysis of transcriptional factors for the hyper-methylated DMPs revealed enrichment of binding motifs attributed to FOXA1, ESR1, XBR1, and FOXP1. Conclusion: A comprehensive and comparative analysis of mRNA expression levels and methylation patterns between TCGA breast cancer samples with high and low expression of HOXB13 revealed key signaling pathways and biological processes that may provide insights on the molecular mechanism of HOXB13 in the regulation of response to endocrine therapy in HR+ breast cancer. Citation Format: Natalia Siuliukina, Yi Zhang, Kai Treuner, Mark Pegram. Differential HOXB13 gene expression and promoter methylation analysis in breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-01-08.

BCAT1, IKZF1 and SEPT9: methylated DNA biomarkers for detection of pan-gastrointestinal adenocarcinomas

Introduction Methylated circulating tumour DNA (ctDNA) blood tests for BCAT1/IKZF1 (COLVERA) and SEPT9 (Epi proColon) are used to detect colorectal cancer (CRC). However, there are no ctDNA assays approved for other gastrointestinal adenocarcinomas. We aimed to characterize BCAT1, IKZF1 and SEPT9 methylation in different gastrointestinal adenocarcinoma and non-gastrointestinal tumours to determine if these validated CRC biomarkers might be useful for pan-gastrointestinal adenocarcinoma detection. Methods Tissue DNA methylation data from colorectal (COAD, READ), gastroesophageal (ESCA, STAD), pancreatic (PAAD) and cholangiocarcinoma (CHOL) adenocarcinoma cohorts within The Cancer Genome Atlas were used for differential methylation analyses. Clinicodemographic predictors of BCAT1, IKZF1 and SEPT9 methylation, and the selectivity of hypermethylated BCAT1, IKZF1 and SEPT9 for colorectal adenocarcinomas in comparison to other cancers were each explored with beta regression. Results Hypermethylated BCAT1, IKZF1 and SEPT9 were each differentially methylated in colorectal and gastroesophageal adenocarcinomas. IKZF1 was differentially methylated in pancreatic adenocarcinoma. Hypermethylated DNA biomarkers BCAT1, IKZF1 and SEPT9 were largely stable across different stages of disease and were highly selective for gastrointestinal adenocarcinomas relative to other cancer types. Discussion Existing CRC methylated ctDNA blood tests for BCAT1/IKZF1 and SEPT9 might be usefully repurposed for use in other gastrointestinal adenocarcinomas and warrant further prospective ctDNA studies.

Abstract 7007: 5hmc-sequencing identifies candidate genes in Puerto Rican Hispanic/Latino men with aggressive prostate cancer

Abstract Introduction: Puerto Rican (PR) Hispanic/Latino (H/L) men are an understudied population which has the highest prostate cancer (PCa) specific mortality when compared with other Hispanic populations. According to the recent PR Cancer Registry data, PCa is the leading cancer type in terms of incidence (38% of all cancer cases) and mortality (16% of all cancer deaths) in PR H/L men. PR H/L men also have significantly higher PCa-specific mortality than non-Hispanic Whites (NHW) and non-Hispanic Blacks (NHB). So far, little is known about higher mortality in PR H/L men. It is believed that epigenetic changes of key genes play a critical role in aggressive tumors. Therefore, our study was aimed to identify key 5-hydroxymethylcytosine (5hmC) changes in PR H/L men with aggressive PCa. Methods: We prepared sequencing libraries using the enriched 5hmC DNA from 42 prostate tumor and 44 adjacent normal FFPE samples. We performed sequencing on an Illumina NextSeq 500 with 75bp single end. We used DESeq2 package to identify differentially methylated genes (DMGs). We also downloaded additional 5hmC data (Database: EGAS00001004942) of 51 localized PCa and 7 adjacent normal for confirmation analysis. We used Gleason score (GS) as a cut-off to identify DMGs in aggressive PCa patients (GS- 7 (3+4) or less, Non-aggressive; GS- 7 (4+3) and above, Aggressive). Results: We identified 808 DMGs in tumors compared to adjacent normal tissues (FDR&amp;lt;0.05, log2FC&amp;gt;|0.4|). Pathway analysis of DMGs demonstrated that cell-cycle, cell division, and DNA repair related pathways were most upregulated in tumors. Since 5hmC abundance is highly correlated with gene expression levels, we further investigated 808 DMGs with TCGA prostate cancer gene expression data. This analysis identified 59/73 DMGs (80.1%, (FDR&amp;lt;0.05, delta&amp;gt;|1|) with significant 5hmC and gene expression changes in the same direction. We also performed differential methylation analysis in another dataset and observed that 129 DMGs were common with our results. Finally, we demonstrated 111 DMGs (p&amp;lt;0.05) in aggressive tumors (n=7) compared to non-aggressive tumors (n=15). These 111 DMGs include important 5hmC hypomethylated genes (BCCIP and KLK10) and hypermethylated genes (COX6C, ARMC2, PVT1) in aggressive PCa patients. Conclusions: Our study identifies 59 genes having coordinated epigenetic and transcriptomic changes in PR H/L men and 111 DMGs in aggressive tumors. The coordinated 59 genes include tumor suppressor genes such as DKK3 and PRDM8 with downregulated 5hmC and gene expression levels. Also, previous studies reported downregulation of BCCIP and KLK10 and upregulation of COX6C, ARMC2, PVT1 expression in PCa. The coordinated changes in 5hmC could be critical in aggressive PCa biology, hence in future, these genes will be validated as candidate biomarkers and targets for aggressive PCa among PR H/L men. Citation Format: Manishkumar S. Patel, Muosa Almubarak, Jaime Matta, Carmen Ortiz-Sánchez, Jarline Encarnacion, Gilberto Ruiz-Deya, Julie Dutil, Jasreman Dhillon, Kosj Yamoah, Anders Berglund, Hyun Park, Liang Wang, Jong Y. Park. 5hmc-sequencing identifies candidate genes in Puerto Rican Hispanic/Latino men with aggressive prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7007.

Abstract 3454: Cell-free DNA methylation and fragmentomic signatures identify tissue damage and predict cancer risk

Abstract Cell-free DNA (cfDNA) epigenetic and fragmentomic profiling has emerged as prominent non-invasive approaches for early cancer detection and subtyping. However, owing to difficulties in observing the early development of human malignancies, most cancer biomarker and evolution studies to date have primarily examined biologics following a diagnosis. Utilizing cfDNA as a screening tool for early disease detection requires assessment of blood plasma samples collected from asymptomatic individuals prior to the diagnosis of cancers. Here, we leverage the Ontario Health Study (OHS), a prospective longitudinal population cohort, to assess cfDNA profiles in blood plasma collected from participants that developed breast (n=476), prostate (n=342) and pancreatic (n=32) cancers throughout the study follow up time between 2009 and 2019. We performed cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) on baseline plasma samples from incident cancer cases up to eight years prior to diagnosis, in addition to matched controls (n=404) with no history of cancer through follow-up. Genome-wide plasma cfDNA differential methylation analysis revealed significantly hypermethylated regions, particularly among CpG island and shore regions, were predictive of early breast cancers, and overlapped hypermethylated regions in solid cancer tissues relative to adjacent normal tissues and peripheral blood leukocytes. Using a repeated cross-validation strategy we found that as few as 200 hypermethylated regions can identify individuals with breast cancer in blood up to seven years prior to diagnosis among discovery set samples (AUC=0.725) and are highly generalizable to samples processed in separate batches (AUC = 0.650). Remarkably, integrating biomarker hypermethylated regions with fragmentomic features including fragment length and 5’ tetranucleotide motif proportions further increased accuracy for detecting individuals with early prostate cancers (AUC=0.804). In addition, we observed shared genome-wide cfDNA methylome changes associated with aging and heavy alcohol consumption in cfDNA. We identified increased shedding of tissue-specific methylation markers from liver and pancreatic tissue among individuals consuming more than three drinks a week relative to non-drinkers, reflective of potential signatures of tissue damage. Citation Format: Nicholas Cheng, Kimberly Skead, Tom Oullette, Scott Bratman, Daniel De Carvalho, David Soave, Philip Awadalla. Cell-free DNA methylation and fragmentomic signatures identify tissue damage and predict cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3454.