bims-dinmec 2025-10-26 papers

Issue of 2025–10–26
four papers selected by
Lorena Ancona, Humanitas Research

Sci Adv. 2025 Oct 24. 11(43): eadw3027

KnowYourCG: Facilitating base-level sparse methylome interpretation.

David C Goldberg, Hongxiang Fu, Daniel Atkins, Ethan Moyer, Chin Nien Lee, Yanxiang Deng, Wanding Zhou.

Decoding DNA methylomes for biological insights is critical in epigenetics research. We present KnowYourCG (KYCG), a data interpretation framework designed for functional DNA methylation analysis. Unlike existing tools that target genes or genomic intervals, KYCG features direct base-level screenings of diverse biological and technical influences, including sequence motifs, transcription factor binding, histone modifications, replication timing, cell-type-specific methylation, and trait associations. Through implementing efficient infrastructure that rapidly screens and investigates thousands of knowledgebases, KYCG addresses the challenges of data sparsity in various methylation datasets, including low-pass or single-cell DNA methylomes, 5-hydroxymethylation (5hmC) profiles, spatial DNA methylation maps, and array-based datasets for epigenome-wide association studies. Applying KYCG to these datasets provides valuable insights into cell differentiation, cancer origins, epigenome-trait associations, and technical issues such as array artifacts, single-cell batch effects, and Nanopore 5hmC detection accuracy. Our tool simplifies large-scale methylation analysis and integrates seamlessly with standard assay technologies.

DOI: https://doi.org/10.1126/sciadv.adw3027

Sci Rep. 2025 Oct 22. 15(1): 36869

Global DNA methylation signatures associated with chemoresistance and poor prognosis of high grade serous ovarian cancer.

Hafiza Bushra Manzoor, Melisa Acun, Hamed Yasavoli-Sharahi, Chantal Matar, Arvind Mer, Benjamin K Tsang, Meshach Asare-Werehene.

Ovarian cancer (OVCA) is third most lethal gynecologic cancers and acquired chemoresistance is the key link in the high mortality rate of OVCA patients. Currently, there are no reliable methods to predict chemoresistance in OVCA. In our study, we identify genes, pathways and networks altered by DNA methylation in high-grade serous ovarian carcinoma (HGSC) cells that are associated with chemoresistance and prognosis of HGSC patients. We performed methylome-wide profiling using Illumina Infinium MethylationEPIC BeadChip (HM850K) methylation array on a set of HGSC chemoresistant and chemosensitive cell lines. Differentially Methylated CpG Probes (DMPs) were identified between the resistant and sensitive groups in HGSC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) over-representation analyses were conducted to identify both common and unique pathways between resistant and sensitive cells. While the HM850K array was used for the discovery phase to identify differentially methylated probes and regions in HGSC cell lines, the publicly available The Cancer Genome Atlas ovarian cancer (TCGA-OV) dataset generated using the Illumina Infinium HumanMethylation27 BeadChip (27 K array) methylation array served as an independent validation cohort for downstream survival and drug sensitivity analyses. Machine learning methods were applied to our dataset to predict drug sensitivity in the TCGA-OV cohort and to investigate associations with overall survival and progression-free survival. Kaplan-Meier analysis was performed to assess the relationship between differentially methylated genes and patient survival outcomes. The overlapping CpG probes shared between the two Illumina platforms were used for machine learning and survival analyses. Data visualization was performed using various R/Bioconductor packages. Our analysis identified a total of 3,641 DMPs spanning 1,617 differentially methylated genes between chemoresistant and sensitive HGSC cells, whereas 80% of them were hypermethylated CpG sites associated with HGSC resistant cells. Approximately half of the DMPs were distributed on chromosomes 1-3, 6, 11-12 and 17 and top identified hypermethylated CpGs were cg21226224 (SOX17, ∆β = 79%, adj.P = 7.73E-03), cg02538901 (ATP1A1, ∆β = 75%, adj.P = 7.6E-03), and cg17032184 (CD58, ∆β = 64%, adj.P = 4.39E-02). Machine learning analysis identified significant association of global hypermethylation in the HGSC chemoresistant cells with poor overall and progression-free survival of HGSC patients. Further analysis identified four differentially methylated genes (CD58, SOX17, FOXA1, ETV1) that were also positively associated with poor prognosis of HGSC OC patients. Functional enrichment analysis showed enrichment of several cancer-related pathways, including phosphatidylinositol signaling, homologous recombination and ECM-receptor interaction pathways. This study supplements the current knowledge of the underlying mechanism behind acquired chemoresistance in OVCA. Four differentially methylated genes identified in this study may have the potential to serve as promising epigenetic clinical biomarkers for HGSC chemotherapy resistance.

DOI: https://doi.org/10.1038/s41598-025-20827-8

Int J Gynecol Cancer. 2025 Oct 06. pii: S1048-891X(25)01806-7. [Epub ahead of print] 102686

Circulating tumor DNA in the diagnosis of ovarian cancer: a systematic review.

Cristina Taliento, Pantaleo Greco, Giulia Bruni, Ina Marie Dueholm Hjorth, An Coosemans, Wouter Froyman, Dirk Timmerman, John Charles Rotondo, Chiara Mazziotta, Martina Arcieri, Stefano Restaino, Francesco Multinu, Giuseppe Vizzielli, Carlotta Giorgi, Lars Dyrskjøt, Paolo Pinton, Giampaolo Morciano.

OBJECTIVE: Ovarian cancer (OC) remains a leading cause of gynecologic cancer mortality worldwide, largely due to late-stage diagnosis and limited early detection tools. Circulating tumor DNA (ctDNA) has emerged as a promising non-invasive biomarker with the potential to improve diagnostic accuracy through detection of tumor-specific genetic and epigenetic alterations.
METHODS: This systematic review aimed to evaluate the diagnostic accuracy of ctDNA in detecting OC compared to healthy controls or benign conditions. A comprehensive literature search was conducted across PubMed, Web of Science, and EBSCO databases through April 2024, including studies that assessed sensitivity, specificity of ctDNA assays in plasma or serum samples. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. PROSPERO registration number: CRD42024590089.
RESULTS: Nineteen studies met inclusion criteria, employing a variety of molecular techniques including polymerase chain reaction-based methylation assays (73.7%) and sequencing methods (whole genome sequencing/next-generation sequencing) (21%), targeting single genes or multi-gene panels. Diagnostic accuracy of ctDNA varied, with sensitivity (40.6%-94.7%) and specificity (56%-100%) ranging broadly, but often outperforming CA125, particularly in early-stage. Concordance between ctDNA and tumor tissue ranged from moderate (r = 0.428) to strong (r = 0.771).
CONCLUSIONS: Although heterogeneity across studies precluded meta-analysis, narrative synthesis suggests that ctDNA may offer an improved early detection capability over CA125, through methylation and copy number variation analyses. Further controlled prospective studies are needed to validate the clinical utility of ctDNA as a complementary tool in OC detection.

Keywords: Circulating Tumor DNA; Diagnosis; Diagnostic Accuracy; Early Detection; Ovarian Cancer

DOI: https://doi.org/10.1016/j.ijgc.2025.102686

Proc Natl Acad Sci U S A. 2025 Oct 28. 122(43): e2502963122

Roles of transposable elements and DNA methylation in the formation of CpG islands and CpG-depleted regulatory elements.

Jueng Soo You, Steve Pierce, Gangning Liang, Peter A Jones.

The origins of CpG islands (CGIs) are not known. They are relatively short GC-rich regions of DNA with a higher-than-expected occurrence of CpG dinucleotides compared to most of the genome. They constitute less than 1% of the human genome but harbor approximately 40% of all transcription start sites (TSSs). CGIs are usually modulated by histone modifications in somatic cells or, in a minority of cases, permanently silenced by CpG methylation. Those that do not have TSSs are called "orphan CGIs". Here, we show that CGIs containing TSSs almost never contain any of three major classes of transposable elements (TEs) and orphan CGIs rarely do. We hypothesize that CGIs persist across evolutionary time due to counterselection against TE insertion in the germ line. The 99% of the vertebrate genome, which is not CG rich, contains 60% of TSSs and putative enhancers. We postulate that conversion of an ancestral CpG-rich genome into the current CpG-depleted version present in vertebrates may also have allowed reversible DNA methylation to function in complex and dynamic gene control circuits. Therefore, we propose an evolutionary model in which vertebrate TEs are indirectly responsible for the existence of CGIs, and the formation of regulatory elements such as TSSs and enhancers that can potentially utilize dynamic DNA methylation for gene control.

Keywords: CpG islands (CGIs); DNA methylation; transcription start sites (TSSs); transposable elements (TEs)

DOI: https://doi.org/10.1073/pnas.2502963122