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Development of a risk model for bcr-free prognosis in prostate cancer patients linked to nucleotide metabolism
Зохиогч
Wei Liu
Munkhtuya Tumurkhuu
Naranjargal Davaadorj
Tao Feng
Ling Qin
Jianlong Weng
Ganbayar Batmunkh
Lai Fu Han
Hong Zhi
Pingping Wang
Он
2025 оны гуравдугаар сарын 3
Төрөл
journal article
Journal
Salud Ciencia y Tecnología
Volume
5
Start Page
1644
End Page
1644
ISSN
1851-3107
2796-9711
Хураангуй
Introduction: Prostate cancer is a prevalent malignancy among elderly men, with bioinformatics playing a crucial role in advancing diagnosis and treatment paradigms. Recent studies have highlighted the significance of nucleotide metabolism (NM) in Prostate cancer development and progression, linking it to aggressive cancer phenotypes characterized by uncontrolled proliferation and metastasis. Understanding NM-related genes (NMRGs) could provide insights into Prostate cancer pathogenesis and therapeutic targets.Methods: This paper analyzed TCGA-PRAD and GSE70769 datasets to identify critical modules associated with NMRGs using weighted gene co-expression network analysis (WGCNA). Differentially expressed genes (DEGs) between Prostate cancer and control samples were extracted from the TCGA-PRAD dataset, with overlaps identified as NM-related DEGs (DE-NMRGs). A biochemical recurrence (BCR)-free risk model was constructed from 396 Prostate cancer samples, and patients were classified into high- and low-risk groups based on median risk scores. A nomogram model integrating key prognostic factors was developed to predict BCR rates.Results: This paper identified 5 prognostic genes: RGS11, KAT2A, MXD3, TARBP1, and WFIKKN. The low-risk group exhibited significantly higher BCR-free survival rates, ESTIMATE scores, and immunophenoscore (IPS) scores compared to the high-risk group. Additionally, potential therapeutic agents, including KU-55933 and Wee1 inhibitors, were proposed. Conclusions: The identified prognostic genes present promising targets for Prostate cancer diagnosis and treatment, emphasizing their importance in predicting biochemical recurrence and tailoring personalized therapeutic strategies for patients.