bims-dinmec 2025-07-20 papers

Issue of 2025–07–20
five papers selected by
Lorena Ancona, Humanitas Research

Front Oncol. 2025 ;15 1547797

The advances of DNA methylation in the liquid biopsy for the detection of lung cancer.

Lung cancer is the most common cancer in the world and the leading cause of cancer death. The absence of effective early detection is one of the major contributors to high mortality rate of lung cancer. Liquid biopsy has the potential to become as a new method for early detection of cancer due to its non-invasive nature, ease of access, and overall presentation of tumor. Liquid biopsy has garnered increasing attention for its role in early detection and tumor genome assessment through the examination of circulating tumor DNA (ctDNA) released by apoptotic or necrotic tumor cells. DNA methylation is a potential biomarker for liquid biopsy due to its early onset, cancer specificity, biological stability, and accessibility in bodily fluids. This review aims to present an overview of the process of DNA methylation, identify potential methylation gene targets, and explore the application of liquid biopsy in the detection of lung cancer.

Keywords: CtDNA; DNA Methylation; biomarker; liquid biopsy; lung cancer

DOI: https://doi.org/10.3389/fonc.2025.1547797

Clin Epigenetics. 2025 Jul 11. 17(1): 122

Multiple highly methylated CpG sites as potential epigenetic markers for the diagnosis of prostate cancer.

Jean-Pierre Roperch, Guillaume Charbonnier, Sandy Figiel, Alastair Lamb, Ian Mills, Claude Hennion, Géraldine Cancel-Tassin, Olivier Cussenot.

BACKGROUND: Prostate cancer (PCa) remains the leading cause of cancer deaths in men. The prostate-specific antigen (PSA) test is widely used for PCa screening, but it lacks specificity and can lead to over-diagnosis and over-treatment. New, effective and affordable markers are therefore needed.
RESULTS: Using enzymatic methyl sequencing (EM-Seq), methylation-specific PCR (MS-PCR), and transcriptomics including a spatial approach, we analyzed tumor and non-tumor samples from radical prostatectomy specimens. Comprehensive methylome was performed in 15 paired samples of prostate cancer and their adjacent non-tumor tissue by EM-Seq. From over 4-million differentially methylated CpG sites, we identified 66 CpGs sites representing eight genes: CLDN5, GSTP1, NBEAL2, PRICKLE2, SALL3, TAMALIN/GRASP, TJP2, and TMEM106A which were hypermethylated in PCa tissues (p-value < 0.0001), and were confirmed by MS-PCR. A very good correlation between EM-Seq and MS-PCR results was observed (Pearson's correlation of 0.93). Differential expression of these candidate genes was analyzed first, using an Affymetrix RNA array dataset from a cohort of 68 non-tumor samples and 101 tumors with different aggressiveness patterns and, second, by in situ expression using Visium 10X spatial genomics transcriptomics on eight prostate tissue sections with different tumor grades and non-tumor glands. Lower expression level was found, using RNA arrays, in tumor compared to non-tumor tissues for six of the eight genes (p ≤ 0.0001) and in tumor glands with high aggressiveness compared to non-tumor glands (p < 0.0001) for the eight genes using in situ transcriptomics.
CONCLUSIONS: Our study identifies promising DNA methylation markers for the diagnosis of prostate cancer.

Keywords: Epigenetic DNA markers; Methylation-specific PCR; Methylome sequencing; Prostate cancer

DOI: https://doi.org/10.1186/s13148-025-01930-z

Int J Mol Sci. 2025 Jun 21. pii: 5961. [Epub ahead of print]26(13):

Ovarian Cancer: Multi-Omics Data Integration.

Anna Kliuchnikova, Arina Gordeeva, Aziz Abdurakhimov, Tatiana Materova, Svetlana Tarbeeva, Elizaveta Sarygina, Anna Kozlova, Olga Kiseleva, Elena Ponomarenko, Ekaterina Ilgisonis.

This study focuses on the systematization and integration of ovarian cancer multi-omics data, revealing patterns in the application of different omics-based approaches and assessing factors that affect the identification of potential biomarkers. An integrative analysis of 51 publications revealed 1649 potential biomarkers. The findings emphasized the molecular diversity of ovarian cancer. They demonstrated the importance of performing the comprehensive integration of molecular and clinical data to search for diagnostic alternatives and molecular patterns underlying ovarian cancer. The heterogeneity of data sources, differences in data acquisition and analysis protocols, and the lack of uniform standards affect the reproducibility of the results of genomic and post-genomic profiling. Multi-omics studies are more promising than mono-omics-based ones. Despite technological advances, researchers continue to focus on results obtained over a decade ago, which may hinder the scientific community from exploring new horizons in ovarian cancer research.

Keywords: biomarkers; molecular heterogeneity; multi-omics; omics; ovarian carcinoma; personalized therapy

DOI: https://doi.org/10.3390/ijms26135961

Cell Rep. 2025 Jul 08. pii: S2211-1247(25)00729-6. [Epub ahead of print] 115958

Time is encoded by methylation changes at clustered CpG sites.

Bracha-Lea Ochana, Daniel Nudelman, Daniel Cohen, Ayelet Peretz, Sheina Piyanzin, Ofer Gal Rosenberg, Amit Horn, Netanel Loyfer, Miri Varshavsky, Ron Raisch, Ilona Shapiro, Yechiel Friedlander, Hagit Hochner, Benjamin Glaser, Yuval Dor, Tommy Kaplan, Ruth Shemer.

Age-dependent changes in DNA methylation allow chronological and biological age inference, but the underlying mechanisms remain unclear. Using ultra-deep sequencing of >300 blood samples from healthy individuals, we show that age-dependent methylation changes occur regionally across clusters of CpG sites either stochastically or in a coordinated block-like manner. Deep learning of single-molecule patterns from two genomic loci predicts chronological age with a median accuracy of 1.36-1.7 years on held-out samples, dramatically improving current clocks. Predictions are robust to sex, smoking, BMI, and biological age measures. Longitudinal 10-year analysis shows that early deviations from predicted age persist throughout life, and subsequent changes faithfully record time. Strikingly, accurate chronological age predictions are possible using as few as 50 DNA molecules, suggesting that age is encoded by individual cells. Overall, DNA methylation changes in clustered CpG sites illuminate the principles of time measurement by cells and tissues and facilitate medical and forensic applications.

Keywords: CP: Metabolism; CP: Molecular biology; DNA methylation; age prediction; aging; biological age; chronological age; computational biology; deep learning; epigenetics; forensics; neural networks

DOI: https://doi.org/10.1016/j.celrep.2025.115958

Signal Transduct Target Ther. 2025 Jul 18. 10(1): 219

Targeting epigenetic regulators as a promising avenue to overcome cancer therapy resistance.

Jiawei Song, Ping Yang, Canting Chen, Weiqun Ding, Olivier Tillement, Hao Bai, Shuyu Zhang.

Cancer remains one of the leading health threats globally, with therapeutic resistance being a long-standing challenge across chemotherapy, radiotherapy, targeted therapy, and immunotherapy. In recent years, the association between epigenetic modification abnormalities and therapeutic resistance in tumors has garnered widespread attention, spurring interest in the development of approaches to target epigenetic factors. In this review, we explore the widespread dysregulation and crosstalk of various types of epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNA changes, which interact through complex regulatory networks in tumors. Clinically, single-targeted therapy based on epigenetic modification usually has its limited effect against cancer. However, the combination of epigenetic drugs with other treatment modalities, such as chemotherapy, targeted therapy, or immunotherapy, shows potential for synergistically enhancing efficacy and reducing drug resistance. Therefore, we evaluate the possibility and potential mechanisms of targeting epigenetic modifications to overcome resistance in cancer therapy, and discuss the challenges and opportunities in moving epigenetic therapy into clinical practice. Moreover, the application of multi-omics technologies will aid in identifying core epigenetic factors from complex epigenetic networks, enabling precision treatment and overcoming therapeutic resistance in tumors. Furthermore, the development of spatial multi-omics technologies, by providing spatial coordinates of cellular and molecular heterogeneity, revolutionizes our understanding of the tumor microenvironment, offering new perspectives for precision therapy. In summary, the combined application of epigenetic therapies and the integration of multi-omics technologies herald a new direction for cancer treatment, holding the potential to achieve more effective personalized treatment strategies.

DOI: https://doi.org/10.1038/s41392-025-02266-z