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PubMed2024

A methylation risk score for chronic kidney disease: a HyperGEN study.

Chronic kidney disease (CKD) impacts about 1 in 7 adults in the United States, but African Americans (AAs) carry a disproportionately higher burden of disease. Epigenetic modifications, such as DNA methylation at cytosine-phosphate-guanine (CpG) sites, have been linked to kidney function and may have clinical utility in predicting the risk of CKD. Given the dynamic relationship between the epigenome, environment, and disease, AAs may be especially sensitive to environment-driven methylation alterations. Moreover, risk models incorporating CpG methylation have been shown to predict disease across multiple racial groups. In this study, we developed a methylation risk score (MRS) for CKD in cohorts of AAs. We selected nine CpG sites that were previously reported to be associated with estimated glomerular filtration rate (eGFR) in epigenome-wide association studies to construct a MRS in the Hypertension Genetic Epidemiology Network (HyperGEN). In logistic mixed models, the MRS was significantly associated with prevalent CKD and was robust to multiple sensitivity analyses, including CKD risk factors. There was modest replication in validation cohorts. In summary, we demonstrated that an eGFR-based CpG score is an independent predictor of prevalent CKD, suggesting that MRS should be further investigated for clinical utility in evaluating CKD risk and progression.

Humans↗

PubMed2024

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

DNA methylation↗

PubMed2024

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Humans↗

PubMed2025

Novel approaches and applications in identifying DNA methylation markers of cardio-kidney-metabolic disease.

Cardio-kidney-metabolic (CKM) diseases represent a major public health challenge, accounting for a large proportion of global burden of morbidity and mortality. These conditions share risk factors, including genetic predisposition, environmental exposures, and lifestyle influences, which collectively drive disease development and progression. Epigenetic modifications, particularly DNA methylation (DNAm), serve as key mediators and biomarkers between these risk factors and disease phenotypes by regulating gene expression without altering the DNA sequence. Epigenome-wide association studies have identified DNAm markers associated with CKM diseases and related phenotypes, highlighting both shared pathways and disease-specific epigenetic signatures in inflammation, metabolic dysfunction, and aging-related processes. Longitudinal studies further demonstrate the dynamic nature of DNAm changes over time, offering insights into disease trajectories. Additionally, methylation risk scores integrating multiple epigenetic markers show promise in improving disease prediction and risk stratification beyond traditional clinical factors. To synthesize the current evidence, we conducted a targeted literature search in PubMed for English-language, peer-reviewed articles published between 2014 and the present. Future research leveraging large, well-phenotyped cohorts, advanced statistical methods, and innovative study designs will be critical for uncovering novel biomarkers, refining risk prediction models, and developing targeted epigenetic therapies to mitigate the global burden.

Humans↗

PubMed2026

A pragmatic adaptation of the RANO clinical risk score for IDH-Wildtype glioblastoma in the absence of MGMT promoter methylation testing.

BACKGROUND: The novel RANO risk score provides prognostic stratification for patients with IDH-wildtype glioblastoma(GBM) and includes age, Karnofsky Performance Scale(KPS), RANO resection class(RRC), and MGMT promoter methylation(MGMTm). However, MGMTm testing is unavailable in many countries. We aimed to explore the prognostic performance of a RANO-adapted clinical score excluding MGMTm. METHODS: We applied the same scoring system established by original RANO score, excluding MGMTm. Three risk classes were defined as numerical scores derived through tertiles. The primary endpoint was overall survival(OS), and the secondary exploratory endpoint was progression-free survival(PFS). RESULTS: One-hundred twenty patients were included. Three risk classes were identified:low-risk(0-1 points,n:47, 39.2%), intermediate-risk(2-3 points,n:27, 22.5%), and high-risk(≥4 points,n:46, 38.3%). The median OS was 35.5 months(95% CI:21.1-49.9) for the low-risk group, 16 months(95% CI:9.9-22.1) for the intermediate-risk group, and 5 months(95% CI:3.6-6.3) for the high-risk group(p < 0.001). Similarly, the median PFS was 16.3 months(95% CI:12.3-20.3) for the low-risk group, 9.9 months(95% CI:7.2-12.6) for the intermediate-risk group, and 4.1 months(95% CI:3.5-4.7) for the high-risk group(p < 0.001). CONCLUSION: This simplified RANO-adapted score demonstrated promising prognostic stratification using basic clinical parameters. As a pragmatic adaptation study, external validation is required before clinical application, particularly in settings where MGMTm testing is unavailable.

Humans↗

PubMed2024

Association analysis between an epigenetic alcohol risk score and blood pressure.

BACKGROUND: Epigenome-wide association studies have identified multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. This study aimed to test the hypothesis that an alcohol consumption epigenetic risk score (ERS) is associated with blood pressure (BP) traits. RESULTS: We implemented an ERS based on a previously reported epigenetic signature of 144 alcohol-associated CpGs in meta-analysis of participants of European ancestry. We found a one-unit increment of ERS was associated with eleven drinks of alcohol consumed per day, on average, across several cohorts (p < 0.0001). We examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with BP levels, i.e., a one-unit increase in ERS was associated with 0.74 mm Hg (p = 0.002) higher SBP and 0.50 mm Hg (p = 0.0006) higher DBP, but not with HTN. Longitudinal analyses in FHS (n = 3260) and five independent external cohorts (n = 4021) showed that the baseline ERS was not associated with a change in BP over time or with incident HTN. CONCLUSIONS: Our findings demonstrate that the ERS has potential clinical utility in assessing lifestyle factors related to cardiovascular risk, especially when self-reported behavioral data (e.g., alcohol consumption) are unreliable or unavailable.

Humans↗

PubMed2026

Potential mitochondria-associated pathogenic genes in sepsis: a multi-omics Mendelian randomization study.

BACKGROUND: Mitochondrial dysfunction has been implicated in the pathophysiology of sepsis. However, human genetic evidence linking mitochondria-related genes to sepsis susceptibility remains limited. This study aimed to identify mitochondria-related genes associated with sepsis risk using a multi-omics Mendelian randomization framework. METHODS: Summary-data-based Mendelian randomization (SMR) was applied using sepsis genome-wide association study (GWAS) summary statistics from the UK Biobank and FinnGen databases. Expression, methylation, single-cell, and protein quantitative trait loci (QTLs) were used as genetic instruments. Colocalization analyses were conducted to evaluate whether SMR associations were driven by shared genetic variants. Expression of prioritized candidate genes was further examined in clinical septic samples, and correlations with disease severity (SOFA scores) were assessed. RESULTS: SMR analysis prioritized 13 mitochondria-related genes associated with sepsis risk. Immune cell-specific eQTL analysis suggested that genetically predicted SURF1 expression in memory B cells and naïve T cells was associated with sepsis risk. Differential expression of 12 candidate genes was confirmed in septic patients by qPCR, and PPOX expression showed a negative correlation with SOFA scores. Integration of mQTL and eQTL data supported a regulatory relationship between methylation at cg06661924 and AK4 expression. Increased genetically predicted AK4 expression was associated with higher sepsis risk (OR = 1.21, 95% CI 1.02-1.42). Protein-level analysis identified DUT as a potential sepsis-associated candidate, with consistent evidence across streptococcal and pneumococcal septicemia subtypes. Subtype analyses also suggested heterogeneous genetic signals across different sepsis subtypes. CONCLUSION: This study prioritized several mitochondria-related genes associated with sepsis susceptibility based on human genetic evidence. These findings provide candidate targets for further mechanistic and translational investigation.

Humans↗

PubMed2026

Age-associated epigenomic heterogeneity in papillary tumors of the pineal region: a multicenter YoungNOA investigation.

BACKGROUND: Papillary tumors of the pineal region (PTPR) are rare CNS neoplasms with adult and pediatric presentations, but whether age defines distinct molecular biology is unclear. METHODS: We assembled a multicenter retrospective cohort of 86 histologically confirmed PTPR with genome-wide DNA methylation data, comprising 62 adult and 24 pediatric tumors. Molecular subgroup, array platform, sex, and tumor purity were incorporated into multivariable models. Analyses included DNA methylation class assignment, differential methylation, copy-number variation (CNV), epigenetic mitotic-clock scores, methylation-based tumor microenvironment deconvolution, and descriptive survival evaluation. RESULTS: Adult and pediatric tumors mapped within the established PTPR-A and PTPR-B methylation framework rather than forming age-defined methylation classes. Pediatric tumors were enriched for PTPR-B (22 of 24 tumors [91.7%]) compared with adult tumors (39 of 62 [62.9%]). After adjustment for methylation-based subgroup as well as technical and biological covariates, 2,923 CpG probes were associated with age at a false discovery rate (FDR) threshold below 0.05, and 530 also met the prespecified effect-size threshold. Global methylation summaries were similar between age groups. CNV patterns were dominated by molecular subgroup; adjusted genomic CNV load was not independently associated with pediatric age. In contrast, epiTOC2 intrinsic rate score and the methylation signature represented by the first principal component (PC1) showed age-associated effects independent of molecular subgroup. Methylation-based deconvolution suggested a limited microenvironmental signal, with neutrophil fraction showing the most consistent adjusted association. CONCLUSIONS: Adult and pediatric PTPR share the established PTPR-A/PTPR-B framework. Pediatric tumors, particularly within PTPR-B, showed age-associated DNA methylation differences and higher epigenetic mitotic-clock (epiTOC2) scores in this retrospective cohort. These tissue-level associations do not establish clinical risk or treatment implications and require prospective clinical annotation and orthogonal validation.

Humans↗

PubMed2026

Methylation-Associated Differentiation Features Define Biological and Prognostic Heterogeneity in CMS4 Colorectal Cancer.

Consensus molecular subtype 4 (CMS4) colorectal cancer (CRC) is associated with an aggressive clinical course and poor survival, yet the biological basis of heterogeneity within this subtype remains incompletely understood. DNA methylation is an epigenetic mechanism involved in transcriptional regulation, cellular differentiation, and colorectal tumorigenesis. Here, we integrated single-cell RNA sequencing (scRNA-seq), bulk data, and promoter DNA methylation data to characterize CMS4-associated cancer cell states and methylation-related features. Using the scAB algorithm, we integrated scRNA-seq with bulk CMS4 data and identified CMS4-related cells distributed across multiple patients. Single-cell analyses of cell-cell communication and transcriptional regulation revealed a CMS4-related cancer cell population characterized by macrophage migration inhibitory factor (MIF)-centered intercellular communication, enhanced caudal type homeobox 1 (CDX1) and Kruppel-like factor 5 (KLF5) regulon activity, and gene modules enriched in differentiation-related pathways. CytoTRACE analysis further stratified CMS4 cancer cells into poorly and well-differentiated states, yielding 802 differentially expressed genes (DEGs). Linking these differentiation-associated DEGs with bulk expression and promoter methylation data identified 218 methylation-associated DEGs showing significant inverse methylation expression correlations, suggesting a link between differentiation-related heterogeneity and promoter methylation. Univariable Cox regression followed by LASSO regression further prioritized eight genes for construction of the methylation and differentiation-related prognostic model (MeDiff-PM). MeDiff-PM consistently stratified overall survival in the TCGA CMS4 cohort and two independent validation cohorts, with cutoff-independent continuous Cox analyses further supporting its prognostic association across cohorts. And MeDiff-PM remained prognostically significant after adjustment for available clinical variables. High MeDiff-PM risk scores were associated with activation of P53, WNT, and ubiquitin-mediated proteolysis pathways and with consistent predicted drug response differences for compounds across three CMS4 cohorts. While individual in silico knockout analysis suggested links between MeDiff-PM genes and metallothionein-related and immune-associated transcriptional responses. Collectively, these findings indicate that methylation-associated differentiation features represent a molecular dimension of intra-CMS4 heterogeneity and provide a biologically informed framework for prognostic stratification within CMS4 CRC.

Humans↗

PubMed2026

Methylation profiling in CNS tumor diagnostics: a single-centre real-world experience from Central Europe.

Genome-wide DNA methylation profiling has transformed neuro-oncology by providing an objective, machine learning-based taxonomy that mitigates interobserver variability and refines the histo-molecular criteria of the current WHO classification. We evaluate the real-world diagnostic performance and clinical utility of this modality in a prospective, consecutively accrued three-year cohort of 291 central nervous system (CNS) tumors across a mixed adult-pediatric population. Successful profiling was completed in 95.9% of cases. Using the Epignostix classifier, a high-confidence diagnostic match (calibrated score [CS] ≥ 0.84) was achieved in 70.3% of analyzable samples, while 26.5% returned lower-confidence scores (≥ 0.3 to < 0.84) and only 3.2% remained completely unclassifiable (CS < 0.3). When integrated into a comprehensive diagnostic framework, methylation profiling provided clinically useful results in 81.1% of cases, establishing diagnoses in 70 cases submitted for molecular subclassification and resolving diagnostic uncertainty or prompting major revisions in 149 histologically challenging tumors. Within truly ambiguous lesions, integration of methylome data dictated tumor grade modifications in 38.8% of cases (upgrading in 29.4% and downgrading in 9.4%), shifting patient risk stratification. Crucially, over half (52.7%) of the lower-confidence cases yielded meaningful clinical integration when supported by histomorphology and ancillary genetic or immunohistochemical markers, demonstrating that rigid score cutoffs should not dictate assay failure. Discrepant or misleading classifications occurred in 1.9%. Updating bioinformatic pipelines from version 11b4 to 12.8 rescued multiple ambiguous entries, increasing overall clinical utility to 84.1%. These findings demonstrate that integrating computational epigenomics with classical neuropathology enhances diagnostic precision, while highlighting the ongoing need for careful clinical-pathological correlation.

Central nervous system tumors↗

PubMed2025

Foundation model based multimodal transformer framework for survival analysis in HER2 stratified breast cancer.

Objective. To improve survival prediction for HER2-positive breast cancer by integrating histopathological, molecular, and clinical data using a multimodal transformer framework.Approach. We propose a multimodal transformer framework for breast cancer survival prediction using HER2 stratified (SurvMBC), a foundation model-enhanced architecture that fuses three data modalities: whole-slide images, clinical narratives, and molecular features. Tumor microenvironment features are extracted using a pathology language and image pre-training (PLIP), clinical narratives are processed with BioBERT, and miRNA expression plus DNA methylation data are embedded using Gen2Vec. These representations are integrated through a cross-modal transformer with attention mechanisms for survival prediction.Main results. The model was evaluated on 1,095 HER2-positive breast cancer patients from The Cancer Genome Atlas. SurvMBC achieved a concordance index (C-index) of 0.857 (95% CI: 0.834, 0.880), a low integrated Brier score, and a strong inverse negative binomial log-likelihood. Risk stratification based on model outputs significantly separated high- and low-risk groups (log-rankp< 0.01) and showed strong associations with tumor stage, grade, and hormone receptor status (allp< 0.05).Significance. SurvMBC demonstrates the effectiveness of multimodal fusion in addressing tumor heterogeneity and improving prognostic accuracy. The attention-based integration enables context-aware learning of survival-relevant features across modalities, supporting individualized risk stratification and risk-adaptive treatment planning for HER2 stratified breast cancer patients.

Breast Neoplasms↗

PubMed2026

Prognostic Role of Global DNA Methylation in Renal Cancer Reveals Decitabine Treatment Benefit.

BACKGROUND: Renal cancer presents a significant global health challenge due to its rising incidence and mortality rates. Often undetected in early stages, it complicates diagnosis and treatment. Current therapies face resistance and limited effectiveness, especially in advanced stages. The diverse subtypes of renal cancer highlight the need for new biomarkers and risk assessment tools for targeted treatments. OBJECTIVE: This study aims to assess the prognostic significance of global DNA methylation (GM) levels in renal cancer, identify new biomarkers, and evaluate the therapeutic potential of the DNA methyltransferase inhibitor decitabine. METHODS: Data on RNA sequencing, gene mutations, DNA methylation, and clinical outcomes were collected from TCGA and GEO databases. We calculated global DNA methylation scores (GMS) and categorized patients into high, intermediate, and low GMS groups. Survival analysis and genomic analyses were conducted to explore the relationships between GMS, clinical outcomes, and tumor characteristics. RESULTS: Higher GMS was identified as an independent prognostic factor associated with worse outcomes in renal cancer. Patients with elevated GMS showed increased mutations, copy number variations, and a more aggressive tumor phenotype. Treatment with decitabine was observed to reduce tumor hypermethylation and downregulate cell cycle pathway activity, indicating potential therapeutic benefits. CONCLUSION: Global DNA methylation plays a significant role in renal cancer prognosis. GMS may serve as valuable biomarkers for prognosis and personalized treatment strategies. Decitabine shows potential efficacy for high GMS patients, particularly through its impact on cell cycle regulation, underscoring the importance of personalized approaches in cancer treatment.

Humans↗

PubMed2026

Genetic modifiers of APOE-ε4-associated cognitive decline.

The APOE-ε4 allele is the strongest genetic risk factor for late-onset Alzheimer's disease. However, APOE-ε4 is not deterministic, highlighting the need to identify additional genetic and environmental factors. APOE-ε4 has been linked to accelerated cognitive decline, so we sought to investigate genetic factors that modify APOE-ε4-associated cognitive decline. We conduct cross-ancestry APOE-ε4-stratified and interaction GWAS using harmonized cognitive data from 32,778 participants, including 29,354 non-Hispanic White and 3,424 non-Hispanic Black individuals. Our primary outcome is late-life cognition, measured using harmonized composite scores for memory, executive function, and language, modeled as continuous traits reflecting both normative cognitive aging and disease-related decline. We identify two genome-wide significant loci in APOE-ε4 carriers, reaching genome-wide significance for executive function. These loci also demonstrate nominal associations across the other domains, suggesting broad effects on cognition. In non-carriers, we identify a genome-wide significant association at ITGB8 restricted to executive function, and another locus associated with language. We further link these loci to SEMA6D, GRIN3A, and ITGB8 through expression and methylation databases. Post-GWAS analyses implicate additional genes including SLCO1A2, and DNAH11. Genetic correlation analyses reveal differences by APOE-ε4 status for immune-related traits, suggesting immune-related predispositions may exacerbate cognitive risk in APOE-ε4 carriers.

Humans↗

PubMed2026

Associations between CRP-related DNA methylation, stress exposure, and depression severity in a longitudinal clinical cohort.

BACKGROUND: Environmental adversity is linked to major depressive disorder (MDD), potentially via sustained low-grade inflammation. However, serum markers such as C-reactive protein (CRP) are transient and sensitive to acute states. In contrast, epigenetic signatures of inflammation may provide a more stable trace of how stress becomes biologically embedded and contributes to depression risk over time. METHODS: In a subsample of the Marburg-Münster Affective Disorders Cohort Study (MACS; N = 579; 320 healthy controls, 259 with MDD), we examined whether early life adversity (ELA; CTQ) and recent life stress (RLS; LEQ) are associated with CRP-related DNA methylation (CRPm) at baseline. We further tested whether CRPm predicts depressive symptom severity (HAMD) at baseline and at two-year follow-up (n = 407). DNA was extracted from whole blood, and CRPm scores were computed using publicly available genome-wide summary statistics. RESULTS: CRPm explained 21.3% of the variance in serum high-sensitivity CRP (hsCRP). Higher CRPm was significantly associated with both ELA (b = 0.01, SE = 0.003, p = 0.017) and RLS (b = 0.01, SE = 0.004, p = 0.032), after adjusting for age and sex. CRPm also predicted depressive symptom severity at baseline (b = 0.68, SE = 0.27, p = 0.013) and at follow-up (b = 0.79, SE = 0.25, p = 0.002). These associations remained after controlling for white blood cell-type composition but were attenuated after adjusting for BMI and smoking. In contrast, hsCRP was not associated with adversity or depressive symptoms. CONCLUSION: Our study indicates that a methylation-based index of chronic inflammation is associated with stress exposure and depressive symptoms over time, in contrast to fluctuating serum hsCRP. The findings are more consistent with an indirect pathway in which environmental adversity is linked to inflammatory biology via stress-related health behaviors, rather than with a model of direct biological embedding.

Humans↗

PubMed2026

Primary Tumor Epigenetic and Transcriptomic Alterations Associated with Nodal Burden and Metastatic Risk in ER+/HER2- Breast Cancer.

De-escalation of axillary surgery has resulted in the loss of pathologic nodal information, yet the extent of lymph node involvement remains an important determinant of treatment decisions in estrogen receptor-positive (ER+)/HER2- disease. We examined whether primary tumors differed molecularly according to the extent of this regional dissemination. Genome-wide DNA methylation profiling of primary ER+/HER2- tumors from 47 patients with pN1 (n = 29) vs. >pN1 (n = 18) disease showed differences concentrated at promoters of developmental and cell-adhesion genes. By integrating methylomes with transcriptomes from the TCGA-BRCA cohort (n = 148) and clinical outcomes from KM Plotter (RFS, n = 1154; OS, n = 442; DMFS, n = 423), we identified four genes (ARL10, RIC3, CXCL14, KCNH2) showing concordant molecular and clinical associations, from which we derived the Lymph-node Involvement Outcome Numerator (LION) score. Lower LION scores were observed in metastatic lesions from the AURORA US cohort (n = 45). In SCAN-B (n = 3969), lower scores were associated with shorter distant recurrence-free intervals (HR = 0.38; 95% CI 0.23-0.62); this association persisted after adjustment for age, nodal and tumor category but was lost after adjustment for histological grade (HR = 0.83; 95% CI 0.48-1.44), indicating that the score and grade capture overlapping biology. These findings suggest that primary tumors already display coordinated epigenetic and transcriptional alterations associated with the extent of metastatic dissemination.

Humans↗

PubMed2026

Detection and classification of lymphoma from cell-free methylome data.

Diagnosing lymphoma traditionally relies on invasive tissue biopsies, which can yield insufficient material for histopathological evaluation and carry a risk of complications. Minimally invasive assessment of cell-free DNA (cfDNA) in plasma offers a promising alternative for lymphoma detection that could aid the rapid evaluation of malignant vs. benign lymphadenopathy. Here, we examine the methylome of plasma samples from 165 lymphoma patients and 47 controls using cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). Differential methylation analysis of a discovery cohort (142 out of 212 samples) revealed 13,897 hypermethylated genomic regions in lymphoma cases, which were subsequently used for classification using regularized binomial generalized linear models. In a validation cohort (70 samples), we identified lymphomas with an accuracy of 0.89, positive predictive value (PPV) of 0.90 and negative predictive value (NPV) of 0.87. cfDNA methylation scores were significantly associated with orthogonal measures of cfDNA tumor burden, stage, and clinical outcomes. Our results highlight the feasibility of cfDNA methylation profiling as a sensitive and minimally invasive method for detecting lymphoma.

Journal Article↗

PubMed2025

Cell-Free DNA Bisulfite Sequencing Reveals Epithelial-Mesenchymal Transition Signatures for Breast Cancer.

Cell-free DNA (cfDNA), shed by malignant tumor cells into extracellular fluid, provides valuable epigenetic information indicative of cancer status. Nipple aspirate fluid (NAF), a noninvasive liquid biopsy from at-risk women, contains nucleic acid and protein biomarkers from adjacent cancer cells, showing promise for breast cancer (BrC) detection. However, despite its potential, the application of cfDNA in NAF for BrC screening is still underexplored. Here, we report a proof-of-concept study for using cfDNA bisulfite sequencing (cfBS) to assess tumor DNA methylation signatures from NAF samples. For four healthy individuals and three BrC patients, cfBS achieved greater than 20× sequencing depth with an average coverage of 26.5× on the genome. A total of 7471 differentially methylated regions were identified, with significant hypermethylation in BrC samples compared to healthy controls. Gene set enrichment analysis indicated that the differentially methylated genes (DMGs) were significantly associated with epithelial-mesenchymal transition (EMT). By developing a novel EMT scoring metric, we found that BrC samples had more of a mesenchymal phenotype than samples from healthy individuals. CDH1, WNT2, and TRIM29 were hypermethylated near the promoter region, while COL5A2 was hypermethylated in the coding region. The DNA methylation and EMT changes were validated through The Cancer Genome Atlas Breast Invasive Carcinoma study, which confirmed that DMGs were associated with gene expression change and that our methylation-based EMT score reliably distinguished tumors from healthy controls. Our findings support the utilization of the NAF cfDNA cfBS methylation profile for noninvasive BrC screening and pave the way for enhanced early detection of this disease.

Humans↗

PubMed2025

Multi-omics reveals cross-tissue regulatory mechanisms of autism risk loci via gut microbiota-immunity-brain axis.

Autism Spectrum Disorder (ASD) involves a multi-system interaction mechanism among genetics, immunity, and gut microbiota, yet its regulatory network remains undefined. This study conducted a meta-analysis on Genome-Wide Association Study data from four independent ASD cohorts to identify potential genetic loci. By integrating Polygenic Priority Score, brain region, and brain cell eQTL enrichment analyses, and combining summary-data-based Mendelian Randomisation (SMR) analyses of brain cis-eQTL and mQTL, bidirectional Mendelian Randomisation analyses of 473 gut microbiota, and SMR analysis of blood eQTL, SNPs such as rs2735307 and rs989134 with significant multi-dimensional associations were identified. These loci exert cross-tissue regulatory effects by participating in gut microbiota regulation, involving immune pathways such as T cell receptor signal activation and neutrophil extracellular trap formation, as well as cis-regulating neurodevelopmental genes (HMGN1 and H3C9P), or synergistically influencing epigenetic methylation modifications to regulate the expression of BRWD1 and ABT1. The cross-scale evidence chain constructed in this study provides a theoretical foundation for precision medicine research in ASD, holding promise to advance the development of innovative therapeutic strategies.

Autism spectrum disorder↗