Predicting DNA methylation from RNA-sequencing data in renal clear cell carcinoma: A deep variational autoencoder approach

Abstract

While RNA-sequencing (RNA-seq) is cost-effective and widely available, obtaining comprehensive DNA methylation profiles remains resource-intensive. To bridge this gap, we developed a deep variational autoencoder (VAE) framework for predicting DNA methylation patterns directly from RNA-seq data in renal clear cell carcinoma. Using The Cancer Genome Atlas–Kidney Renal Clear Cell Carcinoma dataset-sourced from cBioPortal and selected for its clinical relevance in renal clear cell carcinoma and high-quality paired omics data-we implemented a rigorous pre-processing pipeline including quality control, missing data imputation, and RNA feature selection based on variance and predictive power. A patient-wise 80/20 train–test split was employed to prevent data leakage. A deep VAE (NetVAE) with residual encoder blocks was trained to learn a compressed latent representation of the methylome and reconstruct genome-wide beta values directly from RNA-seq input. On the independent test set ( n = 55 patients), the NetVAE achieved R² = 0.627, mean absolute error = 0.135, and Pearson correlation coefficient r = 0.792 ( p < 0.001). Spearman correlation was 0.641 ( p < 0.001). Scatter and density plots confirmed strong predictive power across the full range of beta values. This framework offers a practical, cost-effective method for inferring methylation landscapes from RNA-seq data, with significant potential for biomarker discovery and retrospective epigenetic studies in renal clear cell carcinoma, where direct methylation profiling is unavailable.

Keywords

DNA methylation / RNA sequencing / Machine learning / Renal clear cell carcinoma / Variational autoencoder / Epigenetics

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