ORIGINAL RESEARCH article
Front. Oncol.
Sec. Genitourinary Oncology
This article is part of the Research TopicKidney Cancer Awareness Month 2025: Current Progress and Future Prospects on Kidney Cancer Prevention, Diagnosis and TreatmentView all 18 articles
A robust ammonia metabolism gene signature identified by machine learning predicts prognosis and immunotherapy response in clear cell renal cell carcinoma
Provisionally accepted
Zhilin Gong1,2
Jue Wang1Hansen Lin1Jintao Hua1,2
Jinhuan Wei1Wei Chen1
Junhang Luo1
Jun Pang2
Xu Chen1,3*- 1The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
- 2The Seventh Affiliated Hospital Sun Yat-sen University, Shenzhen, China
- 3Sun Yat-sen University, Guangzhou, China
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Background: Despite the widespread use of immune checkpoint inhibitors (ICIs) in advanced clear cell renal cell carcinoma (ccRCC), therapeutic resistance persists. The prognostic and immunomodulatory role of ammonia metabolism remains unclear. Methods: We leveraged public RNA-seq data and machine learning to identify ammonia metabolism pathways through enrichment analysis of programmed cell death-related genes. Employing multi-omics data from ccRCC patients, we developed an ammonia metabolism risk score (AMRS) via machine learning, which was validated externally and in immunotherapy cohorts. Additionally, scRNA-seq, WGCNA, TMB analysis, and in vitro assays were performed to characterize the model's functional basis. Results: From 147 prognostic ammonia metabolism-related genes in TCGA, a 4-gene random forest model was constructed using LASSO and multivariate Cox regression. This model demonstrated robust predictive accuracy in external validation (3/5/7-year AUCs: 0.710/0.721/0.771). High-risk patients showed significantly elevated mortality in external cohorts (HR = 4.23, 95% CI 1.57–11.42, p = 0.002) and multiple ICI cohorts (HR = 1.30–1.69, p < 0.05). Functional validation via CSAD-targeted siRNA knockdown suppressed migration and invasion by >44% (p < 0.05) across four ccRCC cell lines. Conclusions: Our integrated approach overcomes modeling constraints from limited samples and high-dimensional data and establishes a novel ammonia metabolism-related prognostic signature for ccRCC. CSAD emerges as a promising biomarker warranting further investigation.
Keywords: Ammonia-metabolism, Clear cell renal cell carcinoma (ccRCC), tumor immunemicroenvironment, Immunotherapy, Prognostic prediction model
Received: 19 Sep 2025; Accepted: 08 Dec 2025.
Copyright: © 2025 Gong, Wang, Lin, Hua, Wei, Chen, Luo, Pang and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xu Chen
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