bims-dinmec Biomed News
on DNA methylation in cancer
Issue of 2025–11–16
seven papers selected by
Lorena Ancona, Humanitas Research
  1. Computational Workflow for Genome-Wide DNA Methylation Profiling and Differential Methylation Analysis.
  2. Open chromatin-guided interpretable machine learning reveals cancer-specific chromatin features in cell-free DNA.
  3. Ultra-mild bisulfite outperforms existing methods for 5-methylcytosine detection with low input DNA.
  4. Plasma cell-free DNA methylation markers for detection and prognosis of gastric cancer: A case-control study.
  5. Epigenetic mechanisms of PARP inhibitor resistance in ovarian cancer: A systematic review with bioinformatic analysis of clinically actionable genes.
  6. Preferences of BRCA mutation carriers for attributes of risk-reducing surgical options for breast and ovarian cancer.
  7. Functions and Bioinformatics Analysis of FGL1 in Gastric Cancer.
  1. Bio Protoc. 2025 Nov 05. 15(21): e5506
    Computational Workflow for Genome-Wide DNA Methylation Profiling and Differential Methylation Analysis.
    Pei-Yu Lin, Guan-Jun Lin, Kuan-Lin Chen, Shiang-Chin Huang, Pao-Yang Chen.
      DNA methylation is a crucial epigenetic modification that influences gene expression and plays a role in various biological processes. High-throughput sequencing techniques, such as bisulfite sequencing (BS-seq) and enzymatic methyl sequencing (EM-seq), enable genome-wide profiling of DNA methylation patterns with single-base resolution. In this protocol, we present a bioinformatics pipeline for analyzing genome-wide DNA methylation. We outline the step-by-step process of the essential analyses, including quality control using FASTQ for BS- and EM-seqs raw reads, read alignment with commonly used aligners such as Bowtie2 and BS-Seeker2, DNA methylation calling to generate CGmap files, identification of differentially methylated regions (DMRs) using tools including MethylC-analyzer and HOME, data visualization, and post-alignment analyses. Compared to existing workflows, this pipeline integrates multiple steps into a single protocol, lowering the technical barrier, improving reproducibility, and offering flexibility for both plant and animal methylome studies. To illustrate the application of BS-seq and EM-seq, we demonstrate a case study on analyzing a mutant in Arabidopsis thaliana with a mutation in the met1 gene, which encodes a DNA methyltransferase, and results in global CG hypomethylation and altered gene regulation. This example highlights the biological insights that can be gained through systematic methylome analysis. Our workflow is adaptable to any organism with a reference genome and provides a robust framework for uncovering methylation-associated regulatory mechanisms. All scripts and detailed instructions are provided in GitHub repository: https://github.com/PaoyangLab/Methylation_Analysis. Key features • Provides a comprehensive pipeline for genome-wide DNA methylation analysis. • Step-by-step command line for DMR identification and post-analysis with visualization.
    Keywords:  BS-seq; Bioinformatics; Bioinformatics pipeline; Bisulfite sequencing; CGmap; DMR; DNA methylation; EM-seq; NGS
    DOI:  https://doi.org/10.21769/BioProtoc.5506
  2. Commun Biol. 2025 Nov 12. 8(1): 1554
    Open chromatin-guided interpretable machine learning reveals cancer-specific chromatin features in cell-free DNA.
    Sakuntha D Gunarathna, Aerica Nagornyuk, Nazim A Belabbaci, Regina Nguyen, Bappa Ghosh, Sabha Ganai, Tabatha Lemke, Cassy Garry, Mika Saotome, Muhan Yu, Mamoru Takada, Xusheng Wang, Motoki Takaku.
      Cell-free DNAs (cfDNAs) are DNA fragments found in blood, originating mainly from immune cells in healthy individuals and from both immune and cancer cells in cancer patients. While cancer-derived cfDNAs carry mutations, they also retain epigenetic features such as DNA methylation and nucleosome positioning. In this study, we examine nucleosome enrichment patterns in cfDNAs from breast and pancreatic cancer patients and find significant enrichment at open chromatin regions. Differential enrichment is observed not only at cancer cell type specific ATAC-seq peaks but also at CD4+ T cell specific peaks, suggesting both tumor- and immune-derived contributions to the cfDNA signal. To leverage these patterns, we apply an interpretable machine learning model (XGBoost) trained on cell type specific open chromatin regions. This approach improves cancer detection accuracy and highlights key genomic loci associated with the disease state. Our pipeline provides a robust and interpretable framework for cfDNA-based cancer detection.
    DOI:  https://doi.org/10.1038/s42003-025-08920-0
  3. Nat Commun. 2025 Nov 13. 16(1): 9939
    Ultra-mild bisulfite outperforms existing methods for 5-methylcytosine detection with low input DNA.
    Qing Dai, Tanner Baldwin, Ruitu Lyu, Bryan Daniels, Chang Ye, Chen Cao, Chenyou Zhu, Diwen Fan, Liane Lin, Yushuai Liu, Yiding Wang, Chuan He.
      We present Ultra-Mild Bisulfite Sequencing (UMBS-seq), a method for 5-methylcytosine (5mC) detection that minimizes DNA degradation and background noise. UMBS-seq outperforms conventional bisulfite and enzymatic methyl-sequencing (EM-seq) methods in library yield, complexity, and conversion efficiency when applied to low-input DNA samples. In particular, its effectiveness with low-input cell-free DNA (cfDNA) and hybridization-based target capture highlights its potential for clinical applications, including 5mC biomarker detection and early disease diagnosis.
    DOI:  https://doi.org/10.1038/s41467-025-66033-y
  4. Chin J Cancer Res. 2025 Oct 30. 37(5): 851-864
    Plasma cell-free DNA methylation markers for detection and prognosis of gastric cancer: A case-control study.
    Yanan Wang, Longyang Jin, Weiyang He, Jiaxi Peng, Kailai Yin, Xuanhui Liu, Pengcheng Yu, Yi Wang, Zhehan Bao, Yuhang Xia, Fengming Zhang, Chun Song, Li Yuan, Yuying Wang, Lei Lian, Xiaodong Chen, Xiangdong Cheng.
       Objective: Plasma cell-free DNA (cfDNA) methylation has shown potential in the detection and prognostic testing of multiple cancers. Here, we comprehensively investigate the performance of cfDNA methylation for gastric cancer (GC) detection and prognosis.
    Methods: GC-specific differentially methylated regions (DMRs) were identified by sequencing 56 GC tissues and 59 normal adjacent tissues (NATs). We then performed targeted bisulfite sequencing of cfDNA from 294 GC and 446 non-gastric cancer (NGC) plasma samples, identifying 179 DMRs that overlapped with those in tissue samples. The efficacy of plasma cfDNA methylation markers for GC detection and prognosis was evaluated.
    Results: Based on the 179 DMRs overlapping with those in tissue samples, the random forest (RF) model using 28 DMRs achieved an area under the curve (AUC) of 0.998 in the training cohort, whereas further refinement to the top 6 DMRs resulted in an AUC of 0.985. Consistent results were obtained in the validation cohort (28 DMR AUC: 0.985; 6 DMR AUC: 0.988). Support vector machine (SVM) and logistic regression (LR) models also demonstrated robust performance. Additionally, an 11-DMR signature was developed for prognostic prediction, successfully identifying high-risk GC patients with significantly shorter overall survival.
    Conclusions: Our study highlights the potential utility of cfDNA methylation markers for both the detection and prognostication of GC.
    Keywords:  Gastric cancer; circulating cell-free DNA; detection; diagnosis; prognosis
    DOI:  https://doi.org/10.21147/j.issn.1000-9604.2025.05.14
  5. Crit Rev Oncol Hematol. 2025 Nov 10. pii: S1040-8428(25)00400-7. [Epub ahead of print] 105012
    Epigenetic mechanisms of PARP inhibitor resistance in ovarian cancer: A systematic review with bioinformatic analysis of clinically actionable genes.
    Muhammad Habiburrahman, James M Flanagan.
      PARP inhibitors (PARPi) improve ovarian cancer (OC) outcomes, but resistance remains a major challenge without reliable prognostic biomarkers. This study identified epigenetic hallmarks of PARPi resistance by integrating 27 studies (22 preclinical, 5 clinical; 2010-2024) and validating candidate genes using web-based bioinformatics tools and public microarray/RNA-seq datasets from treatment-naïve OC tissues. We hypothesised that early aberrant expression of these epigenetically altered, PARPi resistance-related genes in tumours may be linked to disease progression (PFS) and could serve as early biomarkers to be associated with PARPi resistance during first-line treatment. We confirmed epigenetic involvement in PARPi resistance across 36 genes linked to epigenetic modifications. Of these, 10 genes (n=614-1435)-including RNASEH2B (HR=1.41), VHL (HR=1.26), ATM (HR=1.22), XRCC1 (HR=1.20), NRP1 (HR=1.16), KAT2B (HR=1.16), EZH2 (HR=1.15), CREBBP (HR=1.14), FZD10 (HR=0.87), and CARM1 (HR=0.86)-showed significant prognostic value for PFS (all: p<0.05). This 10-gene signature remained collectively significant (HR 1.27, p=0.014). RNA-seq validation showed differential expression of these genes, with highest fold-change overexpression in tumours for FZD10 (4.20), EZH2 (3.56), and CARM1 (1.61), and lowest in ATM (0.22), KAT2B (0.33), and NRP1 (0.44). GO and KEGG analyses revealed these genes are enriched in key resistance pathways, including impaired DNA repair, reduced replication stress, immune evasion, and stemness maintenance. This review with bioinformatic validation identified a 10-gene epigenetic signature associated with PARPi resistance and disease progression. These clinically actionable genes, aberrantly expressed before treatment, may serve as early biomarkers for risk stratification. Further validation in PARPi-sensitive and -resistant ovarian cancer cohorts is needed.
    Keywords:  DNA repair mechanisms; PARP inhibitors resistance; biomarkers; epigenetic hallmarks; ovarian cancer
    DOI:  https://doi.org/10.1016/j.critrevonc.2025.105012
  6. Cancer. 2025 Nov 15. 131(22): e70148
    Preferences of BRCA mutation carriers for attributes of risk-reducing surgical options for breast and ovarian cancer.
    Katherine C Fitch, Jui-Chen Yang, Emma S Ryan, Karen A Monuszko, Katherine N Penvose, Sue Friedman, Jennifer K Plichta, Haley A Moss, Shelby D Reed, Laura J Havrilesky.
       BACKGROUND: Risk-reducing surgeries are the most effective strategies for cancer prevention in patients with germline pathogenic variants in the BRCA1/BRCA2 genes; these surgeries are associated with early menopause, loss of childbearing potential, and cosmetic effects. The authors assessed women's preferences for tradeoffs related to risk-reducing surgical decision making.
    METHODS: Carriers of pathogenic mutations in BRCA1/BRCA2 aged 25-50 years without a personal history of breast, ovarian, peritoneal, or tubal cancer were recruited to complete one of four discrete choice surveys based on their age (younger than 40 years or 40 years and older) and BRCA mutation status (BRCA1 or BRCA2). Participants responded to a series of choices between a do-nothing strategy and two profiles representing various effects of surgical options on menopause, childbearing potential (those younger than 40 years only), breast appearance, and 10-year and lifetime risks of breast and ovarian cancer. A conditional logit model was used to quantify participants' choices as a function of surgical options and outcomes.
    RESULTS: In total, 298 participants completed the survey. Each cohort younger than <40 years more frequently chose profiles representing risk-reducing salpingo-oophorectomy (RRSO) at age 40 versus 30 years. The cohort aged 40 years and older with BRCA1 mutations favored RRSO at age 40 years but with a 56.6% choice probability of delayed RRSO after ages 35-40 years, as recommended by National Comprehensive Cancer Network guidelines. The cohort aged 40 and older with BRCA2 mutations favored RRSO at age 40, 45, or 50 years fairly equally, with a 33.0% choice probability of guideline-nonconcordant RRSO timing. All cohorts favored mastectomy at younger ages and with reconstruction versus no mastectomy.
    CONCLUSIONS: These findings demonstrate the heterogeneity of preferences and support individualized discussion of treatment goals relating to risk-reducing surgical planning.
    Keywords:  BRCA; discrete‐choice experiment; hereditary breast cancer; hereditary ovarian cancer; risk reduction
    DOI:  https://doi.org/10.1002/cncr.70148
  7. Clin Lab. 2025 Nov 01. 71(11):
    Functions and Bioinformatics Analysis of FGL1 in Gastric Cancer.
    Hui Zhang, Hong Tao, Guang-Yue Hu, Min Jiang, Wen Gu, Hong Yin.
       BACKGROUND: FGL1 (fibrinogen-like protein 1) is considered to be closely related to cell proliferation and differentiation. The purpose of this study was to explore the value and mechanism of FGL1 as a prognostic indicator for gastric cancer (GC).
    METHODS: Initially, online database was used to analyze the expression of FGL1; surgical paired samples were collected from 10 patients with GC, and FGL1 was detected by RT-qPCR. The relationship between FGL1 and clinicopathological parameters was evaluated, as well as overall survival rate, post-progression survival rate, and relapse-free survival rate (OS, PPS, RFS). FGL1 expression was altered in cell lines to observe its effect on proliferation, migration, invasion, and apoptosis of GC cells (CCK-8 assay, colony formation assay, wound-healing assay, Transwell assay, and apoptosis assay). Finally, differential expression genes (DEGs) of GC were screened by gene chip, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and PPI networks were performed on DEGs, and Hub genes were identified.
    RESULTS: FGL1 was highly expressed in GC cell lines and GC tissues. FGL1 expression was significantly correlated with histopathological differentiation, tumor infiltration, and lymph node metastasis of GC (p = 0.007; p = 0.008; p = 0.042); there was no correlation with age, gender, or distant metastasis (all p > 0.05). OS, PPS, and RFS of patients in GC with high FGL1 expression were shorter than of patients with low FGL1 expression (HR = 1.38, logrank p < 0.01; HR = 1.36, logrank p < 0.01; HR = 1.27, logrank p < 0.01). Suppressing FGL1 inhibited the pro¬liferation, migration, and invasion ability of SGC7901 cells and arrested cell cycle at the G1 phase. FGL1 silencing did not induce apoptosis of GC cells. In total, 634 GC DEGs were obtained by gene chip analysis. GO analysis found that most of the genes were related to cell membrane and extracellular matrix. KEGG analysis revealed that DEGs were involved in signal pathways such as combined proteoglycan interaction, glycoprotein hormone and peptide hormone biosynthesis, non-integrin membrane-extracellular matrix interaction, and protein digestion and absorption, and the top 20 key genes of GC were identified, including OASL, ISG20, RAB3A, CREM, BIRC7, LHB, etc. Conclusions: FGL1 is a potential biomarker for the diagnosis and prognosis of GC. FGL1-mediated signaling pathways may be a new target for GC therapy in future.
    DOI:  https://doi.org/10.7754/Clin.Lab.2025.250126
This page is being maintained by Thomas Krichel. It was last updated on 2025‒12‒01 at 13:10.