Robust methylome analysis and tumour–normal classification in TCGA–COAD: a reproducible workflow

Abstract Colorectal adenocarcinoma is caused in part by widespread epigenetic deregulation, yet the analysis of genome-wide DNA methylation of colorectal adenocarcinoma is complicated due to spatial correlation among CpG, multiscale patterns of differential methylation, and confounding cellular heterogeneity in bulk tissue. This study develops a simple yet effective framework that combines rigorous statistical modelling with modern machine learning-based prediction, on the Illumina 450K data from The Cancer Genome Atlas (TCGA) colorectal adenocarcinoma cohort. In our framework, differentially methylated regions (DMRs) were first detected using functional smoothing and permutation-based bump hunting, revealing both focal CpG island hypermethylation and broad hypomethylated domains spanning hundreds of kilobases. Next, we performed reference-based cell-type deconvolution and surrogate variable analysis (SVA) controlled immune/stromal admixture and hidden confounding effects, yielding well-calibrated single-site and region-level inference. Then, for tumour-status prediction, we empirically study the performance of classical Logistic Regression model against Random Forest, Gradient Boosting (XGBoost), and Feed-forward neural network; the results show that Logistic Regression achieves the lowest root-mean-square error and Brier score, reflecting its superior probability calibration. In general, our integrated framework provides biologically interpretable and highly predictive methylation signatures of colorectal cancer and offers a transferable baseline for future large-scale cancer epigenomics studies. Our code is publicly available at https://github.com/matekum/tcga-coad-methylation-ekum-2025a. Author summary We present a reproducible framework for analyzing genome-wide DNA methylation in colorectal cancer using public TCGA data. Our aim is twofold. We started by making single-site and regional findings statistically reliable by accounting for spatial correlation, hidden confounding, and cell-type mixtures; and second, we evaluate practical classifiers for tumour vs. normal prediction. We smooth effect sizes across neighbouring CpG sites and use permutation tests to detect differentially methylated regions, while surrogate variable analysis and reference-based cell-fraction estimates reduce unwanted variation. We then compare logistic regression with random forests, gradient boosting, and a neural network using principal component features. Across models, we observe near-perfect ranking of samples and strong probability calibration, with logistic regression performing best on calibration. All code, parameters, and figure scripts are openly available so others can reproduce and adapt the framework. Beyond colorectal cancer, the approach provides a template for robust methylome analysis and predictive modelling in other large-scale epigenomic studies. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The research was conducted using publicly available data, and all analyses were carried out without external financial support. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: National Cancer Institute (NCI) Genomic Data Commons (GDC) - https://portal.gdc.cancer.gov TCGA COAD (Colorectal Adenocarcinoma) DNA methylation array dataset. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability All data analysed in this study are publicly available through The Cancer Genome Atlas (TCGA) via the NCI Genomic Data Commons (GDC) portal: https://portal.gdc.cancer.gov. No new data were generated. All scripts and workflows used for data processing, analysis, and figure generation are openly available at https://github.com/matekum/tcga-coad-methylation-ekum-2025a and archived on Zenodo: https://doi.org/10.5281/zenodo.1722394 https://github.com/matekum/tcga-coad-methylation-ekum-2025a Data availability All DNA-methylation data analysed here are from The Cancer Genome Atlas (TCGA) colorectal adenocarcinoma (COAD) cohort via the NCI Genomic Data Commons (GDC) portal: https://portal.gdc.cancer.gov/. Access requires a free GDC account and compliance with TCGA data-use policies. All R scripts for downloading, preprocessing and analysis (per-CpG empirical-Bayes modelling, surrogate variable analysis, bumphunter region detection, generalised additive model visualisations, and machine-learning prediction) are openly available at https://github.com/matekum/tcga-coad-methylation-ekum-2025a and archived on Zenodo at https://doi.org/10.5281/zenodo.1722394. These resources contain all code and instructions needed to reproduce the figures, tables, and statistical analyses. Abbreviations - AUC - Area under the receiver operating characteristic curve - BIC - Bayesian information criterion - CpG - Cytosine–phosphate–guanine dinucleotide - DMR - Differentially methylated region - DMP - Differentially methylated position - FDR - False discovery rate - GAM - Generalised additive model - GDC - Genomic Data Commons - ML - Machine learning - PC / PCA - Principal component / Principal component analysis - QC - Quality control - RMSE - Root mean squared error - ROC - Receiver operating characteristic - SNP - Single-nucleotide polymorphism - SVA - Surrogate variable analysis - TCGA - The Cancer Genome Atlas - TCGA–COAD - TCGA colorectal adenocarcinoma cohort - 5-mC - 5-methylcytosine - limma - Linear Models for Microarray Data (R/Bioconductor package)