Epigenome-wide analysis reveals potential biomarkers for radiation-induced toxicity risk in prostate cancer

Abstract

Background: Prostate cancer is the second most common cancer globally, with radiation therapy (RT) being a key treatment for clinically localized and locally advanced cases. Given high survival rates, addressing long-term side effects of RT is crucial for preserving quality-of-life. Radiogenomics, the study of genetic variations affecting response to radiation, has primarily focussed on genomic biomarkers, while DNA methylation studies offer insights into RT responses. Although most research has centred on tumours, no epigenome-wide association studies have explored peripheral blood biomarkers of RT-induced toxicities in prostate cancer patients. Identifying such biomarkers could reveal molecular mechanisms underlying RT response and enable personalized treatment.

Methods: We analysed 105 prostate cancer patients (52 cases and 53 controls). Cases developed grade ≥ 2 genitourinary and/or gastrointestinal late toxicity after 12 months of starting RT, whereas controls did not. An epigenome-wide association study of post-RT toxicities was performed using the Illumina MethylationEPIC BeadChip, adjusting for age and cell type composition. We constructed two methylation risk scores-one using differentially methylated positions (MRSsites) and another using differentially methylated regions (MRSregions)-as well as a Support Vector Machine-based methylation signature (SVMsites). We evaluated RT effects on biological age and stochastic epigenetic mutations within established radiation response pathways. Gene Ontology and pathway enrichment analyses were also performed.

Results: Pre-RT methylation analysis identified 56 differentially methylated positions (adjusted p-value ≤ 0.05), and 6 differentially methylated regions (p-value ≤ 0.05) associated with the genes NTM, ACAP1, IL1RL2, VOOP1, AKR1E2, and an intergenic region on chromosome 13 related to Short/Long Interspersed Nuclear Elements. Both Methylation Risk Scores (MRSsites AUC = 0.87; MRSregions AUC = 0.89) and the 8-CpG Support Vector Machine signature (SVMsites AUC = 0.98) exhibited strong discriminatory accuracy in classifying patients in the discovery cohort. Gene ontology analysis revealed significant enrichment (adjusted p-value ≤ 0.05) of genes involved in DNA repair, inflammatory response, tissue repair, and oxidative stress response pathways.

Conclusions: Epigenetic biomarkers show potential for predicting severe long-term adverse effects of RT in prostate cancer patients. The identified methylation patterns provide valuable insights into toxicity mechanisms and may aid personalized treatment strategies. However, validation in independent cohorts is essential to confirm their predictive value and clinical applicability.

Keywords: Adverse Effects; Cancer; EWAS; Epigenetic biomarkers; Prostate; Radiogenomics; Radiotherapy; Stratify Patients for Treatments; Therapy Response.

Fig. 1

Comparison of cell type proportion between cases (red) and controls (light blue). The x-axis represents cell types (CT) and the y-axis count proportions. *p-value < 0.05

Fig. 2

EWAS Results. A Volcano plot, B Principal Component Analysis (PCA) plot, C Heatmap, and D Manhattan plot illustrating the association of DNA methylation CpGs with radiation-induced grade ≥ 2 genitourinary and/or gastrointestinal late toxicities in prostate cancer patients (52 cases and 53 controls). In the volcano plot, dark blue and red dots represent significant hypomethylated and hypermethylated CpG sites, respectively, in cases compared to controls (the grey dashed line represents the cutoffs of adjusted p-value < 0.05 and logFC =|0.2|). The PCA plot demonstrates segregation between cases (red dots) and controls (light blue dots), with the explained variance for each principal component (PC1-2) displayed. The heatmap includes unsupervised clustering of the 56 significant differentially methylated positions (DMPs), with a colour gradient where black denotes low methylation and yellow denotes high methylation. In the Manhattan plot, the X-axis represents chromosome positions, while the Y-axis represents the –log10 adjusted p-values. The gray line indicates the significance threshold at an adjusted p-value < 0.05, with red dots highlighting CpGs belonging to significant differential methylation regions

Fig. 3

Boxplots of CpGs Comprising 6 DMRs of Radiation-Induced Severe Adverse Effects. Box plots display the differences in mean DNA methylation levels between cases (red) and controls (light blue). The y-axis represents the methylation rates

Fig. 4

Dot plot of the 18 gene ontology biological processes and the five significantly enriched pathways. Dot colours along the x-axis represent different FDR p-values, and dot size indicates the number of genes involved

Fig. 5

Density plot of methylation changes in genes with significantly enriched biological functions and pathways. Density curves illustrate the distribution of hypo- and hypermethylated genes in cases. A black dashed line marks zero mean methylation change, indicating no net methylation change

Fig. 6

Mean ROC curves and AUC violin plots from cross-validation of three methylation classification models. The mean ROC curves A and AUC violin plots B derived from four-fold cross-validation with 2,000 iterations for three methylation classification models: MRSsites (red), MRSregions (light blue), and SVMsites (dark blue). The ROC curves depict the average classification performance of each model, while the violin plots show the distribution of AUC values across cross-validation iterations. The horizontal red dashed line in the plots indicates a zero-effect threshold

Fig. 7

Violin plots showing weight distributions for six significant DMRs in the MRSregions logistic regression model. Each violin plot depicting the weight distributions from the logistic regression model for six significant differentially methylated regions (DMRs) forming the MRSregions model. Each plot represents the effect size and direction of the DMRs associated with the gene regions ACAP1, AKR1E2, IL1RL2, NTM, VOOP1, and an intergenic region (IR)