Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Dazhi Lu, Yan Zheng, Xianyanling Yi, Jianye Hao (), Xi Zeng, Lu Han, Zhigang Li, Shaoqing Jiao, Bei Jiang, Jianzhong Ai () and Jiajie Peng ()
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Dazhi Lu: Northwestern Polytechnical University
Yan Zheng: Tianjin University
Xianyanling Yi: Sichuan University
Jianye Hao: Tianjin University
Xi Zeng: Northwestern Polytechnical University
Lu Han: Northwestern Polytechnical University
Zhigang Li: Tianjin University
Shaoqing Jiao: Northwestern Polytechnical University
Bei Jiang: Tianjin Second People’s Hospital
Jianzhong Ai: Sichuan University
Jiajie Peng: Northwestern Polytechnical University

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov Decision Process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the limitation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies eight potential ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR) and piccolo presynaptic cytomatrix protein (PCLO), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

Date: 2025
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DOI: 10.1038/s41467-025-58439-5

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