AccScience Publishing / IMO / Online First / DOI: 10.36922/IMO025420055
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ORIGINAL RESEARCH ARTICLE

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

Muhammad Salman1*
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1 Kabir Medical College, Gandhara University, Peshawar, Pakistan
IMO, 025420055 https://doi.org/10.36922/IMO025420055
Received: 17 October 2025 | Revised: 30 November 2025 | Accepted: 30 April 2026 | Published online: 12 June 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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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
Funding
None.
Conflict of interest
The author declares no conflicts of interest.
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Innovative Medicines & Omics, Electronic ISSN: 3060-8740 Print ISSN: 3060-8910, Published by AccScience Publishing
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