Year: 2025
Type: article
Abstract: Background: The mechanisms of epigenetic regulation emerge as a fundamental determinant in the complex landscape of cancer initiation and advancement. However, the specific impact of epigenetic regulation on cancer progression remains unclear. To explore the relationship between epigenetic regulation and cancer progression, we utilized transcriptomic data from The Cancer Genome Atlas (TCGA) datasets to investigate the association. Methods: We obtained transcriptomic data of epigenetic gene dataset from the TCGA database and calculated an epigenetic score using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Additionally, we created a nomogram that integrates the epigenetic score and clinical features, providing a more comprehensive tool for tumor patients prognosis assessment. Results: We calculated the epigenetic score based on the expression levels of epigenetic-related genes. The nomogram we developed incorporates the epigenetic score and clinical characteristics. The epigenetic score was positively correlated with the expression of genes related to hallmarkers of cancer, including glycolysis, epithelial-mesenchymal transition (EMT), cell cycle, DNA repair, angiogenesis, and inflammatory response. Furthermore, we performed gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analysis to explore the signaling pathways and biological processes in high epigenetic score group. Conclusion: The epigenetic scoring system developed in this investigation represents an innovative approach that demonstrates remarkable potential in forecasting survival trajectories across diverse cancer types. These groundbreaking insights not only illuminate the intricate interactions between epigenetic mechanisms and gene expression regulation in oncological contexts, but also indicate that the derived epigenetic metric could potentially emerge as a significant prognostic biomarker for cancer outcomes.
Source: Frontiers in Pharmacology
Authors: Ruiguang Zhang, Shimin Jin, Qi Xu, Rongxiao Dai
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4407715494