ORIGINAL RESEARCH article
Front. Cell Dev. Biol.
Sec. Cancer Cell Biology
Machine Learning-Derived AS and AIS Scores Leverage BCAA Metabolism and IL4I1 Activity for Prognosis and Tailored Therapy in ccRCC
Provisionally accepted
- Fujian Medical University Union Hospital, Fuzhou, China
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Background and Objective: Renal cell carcinoma (RCC) is among the most prevalent malignant tumors globally, characterized by a poor prognosis. The 5-year survival rate for advanced clear cell renal cell carcinoma (ccRCC) is below 20%. Materials and Methods: This study utilized single-cell data analysis to examine the differences in branched-chain amino acid metabolism among ccRCC patients. Ten machine learning algorithms were employed to develop Amino acid Signature Score(AS score), integrating data from TCGA and GEO cohorts. We compared and validated the clinical characteristics, molecular features, and drug sensitivity of patients with varying AS scores. To address patient heterogeneity, principal component analysis was applied to construct an Amino acid Individualized Signature Score (AIS score) aimed at guiding personalized treatment and assessing its performance in immunotherapy and targeted therapy. Additionally, we explored the interaction between IL4I1 and branched-chain amino acid metabolism, along with the underlying causes of abnormal expression, using spatial transcriptomics and single-cell multi-omics approaches. This is a provisional file, not the final typeset article Results: Branched-chain amino acid metabolism plays a crucial role in the progression and treatment of ccRCC. The AS score effectively distinguishes clinical characteristics and drug sensitivity across different patient subgroups. The AIS score confers a strategic advantage for second-line and immunotherapy when targeted therapy is ineffective. The elevated expression of IL4I1 enhances the degradation of branched-chain amino acids, promoting tumor growth and metastasis. Further analysis indicated that VHL mutations may elevate IL4I1 expression in tumors by modulating key transcription factors Hif-1a and SFMBT1, thus aggravating tumor progression. Conclusions: Branched-chain amino acid metabolism and IL4I1 are pivotal in the progression of ccRCC. AS classification and the AIS score present a robust framework for personalized treatment strategies, while IL4I1 shows potential as a novel therapeutic target to enhance treatment efficacy.
Keywords: amino acid metabolism, Clear cell renal cell carcinoma, IL4I1, Machinelearning, Prognostic score
Received: 08 Oct 2025; Accepted: 02 Feb 2026.
Copyright: © 2026 weng, Li, Chen, Lin, Liu, Yan and Xie. 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:
Le ye Yan
Ruo yun Xie
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