ArticleFrontiers in cell and developmental biology2026
Machine learning-derived AS and AIS scores leverage BCAA metabolism and IL4I1 activity for prognosis and tailored therapy in ccRCC.
Article in Frontiers in cell and developmental biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. 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. Conclusion: 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.