# Machine learning-derived AS and AIS scores leverage BCAA metabolism and IL4I1 activity for prognosis and tailored therapy in ccRCC

## TL;DR

This study uses machine learning to develop scores for predicting ccRCC prognosis and treatment response based on amino acid metabolism and IL4I1 activity.

## Contribution

Introduces AS and AIS scores leveraging BCAA metabolism and IL4I1 for personalized ccRCC therapy and prognosis.

## Key findings

- AS score distinguishes clinical features and drug sensitivity in ccRCC patients.
- AIS score improves treatment strategies for second-line and immunotherapy.
- IL4I1 promotes tumor growth by enhancing BCAA degradation, linked to VHL mutations.

## Abstract

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%.

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.

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.

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.