1. Machine learning-based discovery of UPP1 as a key oncogene in tumorigenesis and immune escape in gliomas.
- Author
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Zigui Chen, Chao Liu, Chunyuan Zhang, Ying Xia, Jun Peng, Changfeng Miao, and Qisheng Luo
- Subjects
RNA sequencing ,TUMOR growth ,BIOMARKERS ,MACHINE learning ,GLIOMAS ,BRAIN tumors - Abstract
Introduction: Gliomas are the most common and aggressive type of primary brain tumor, with a poor prognosis despite current treatment approaches. Understanding the molecular mechanisms underlying glioma development and progression is critical for improving therapies and patient outcomes. Methods: The current study comprehensively analyzed large-scale single-cell RNA sequencing and bulk RNA sequencing of glioma samples. By utilizing a series of advanced computational methods, this integrative approach identified the gene UPP1 (Uridine Phosphorylase 1) as a novel driver of glioma tumorigenesis and immune evasion. Results: High levels of UPP1 were linked to poor survival rates in patients. Functional experiments demonstrated that UPP1 promotes tumor cell proliferation and invasion and suppresses anti-tumor immune responses. Moreover, UPP1 was found to be an effective predictor of mutation patterns, drug response, immunotherapy effectiveness, and immune characteristics. Conclusions: These findings highlight the power of combining diverse machine learning methods to identify valuable clinical markers involved in glioma pathogenesis. Identifying UPP1 as a tumor growth and immune escape driver may be a promising therapeutic target for this devastating disease. [ABSTRACT FROM AUTHOR] more...
- Published
- 2024
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