Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4

Integrated transcriptome analysis and combinatorial machine learning to construct a homeostatic model of acetylation for ccRCC and validate the key gene GCNT4

Abstract Background Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors of the urinary system. Protein acetylation plays a key role in regulating cellular processes and cancer signaling pathways. This study explores the potential biological mechanisms of ccRCC from the...

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Bibliographic Details
Main Authors: Baohua Zhu, Ziyang Mo, Yi Bao, Xinxin Gan, Linhui Wang
Format: Article
Language:English
Published: BMC 2025-06-01
Series:Cancer Cell International
Subjects:
Clear cell renal cell carcinoma
Acetylation
Prognosis
Machine learning
GCNT4
Online Access:https://doi.org/10.1186/s12935-025-03837-4
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https://doi.org/10.1186/s12935-025-03837-4

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