Research work on Methylation-based Markers in Breast Cancer: Insights from Functional and Co-Methylation Network Analysis by Dr. Nita Parekh and her students Sri Lakshmi Bhavani Pagolu and Suba S received the Best Poster Award by Digital Discovery (Royal Society of Chemistry) at the International Conference on Modeling Chemical and Biological (Re)Activity (MCBR-2025) held in Hyderabad from 11 to 14 December 2025.
Here is the summary of the paper as explained by the authors:
The role of epigenetic alterations in tumor development and molecular heterogeneity is now well known. Genome-wide analyses have revealed widespread methylation differences across breast cancer subtypes, specifically in the context of hormone-dependent subtypes. Thus, differential methylation analysis and associated transcriptomic changes can provide insights in unravelling heterogeneity in breast tumours and identify markers for improved diagnosis and prognosis at the epigenetic level.
Results: A knowledge-based approach is proposed for filtering relevant CpG sites from the high-dimensional DNA methylation data to identify 56 novel markers (protein-coding and lncRNA genes) for subtype-specific disease stratification. Classification of breast cancer samples into three molecular subtypes: Luminal, HER2-enriched, and Triple-Negative, using these markers as features in SVM-RBF resulted in an accuracy of 94% (MCC = 0.87) and 88% (MCC=0.79) on holdout test and external sets, respectively, indicating their diagnostic potential. Functional analysis revealed the associated genes linked to key cancer processes epithelial–mesenchymal transition (EMT), transcriptional regulation, endocrine resistance, ESR-mediated signaling, mammary gland development and cancer-specific mechanisms (proteoglycans in cancer). Subtype-specific co-methylation gene clusters were constructed and analyzed to uncover distinct epigenetic architectures and coordinated regulatory patterns, reflecting underlying differences in tumor biology. The Luminal cluster comprised ESR1-associated genes linked to estrogen-driven proliferation and EMT, and HER2 cluster was mainly enriched with lncRNAs while the shared co-methylated sites indicate overlapping regulatory programs in the two subtypes. TNBC cluster exhibited dual regulation, with hypermethylated genes involved in immune modulation and hypomethylated-upregulated genes participating in oncogenic signaling and invasion-associated proliferative pathways. Functional analysis of lncRNA markers revealed association with alternate programmed cell death pathways (necroptosis and disulfidptosis), as oncogenic regulators (LINC01956, LGALS8-AS1, LINC01354, and FOXCUT), activating multiple transcription factors, and forming ceRNA networks. A prognostic model developed using multivariate COX regression model identified a subset of 15-CpG prognostic signature that stratified patients into high- and low-risk groups with strong predictive performance (AUCs ³ 0.7, C-Index ³ 0.65).
Conclusion: This study identifies DNA methylation-driven signature with diagnostic and prognostic potential in breast cancer. Analysis of subtype-specific co-methylation patterns reveal distinct regulatory mechanisms and offer potential targets for precision epigenetic therapies.
January 2026