鉴定基于嘧啶代谢的分子亚型和预后特征以预测前列腺癌的免疫景观和指导临床治疗。

Annals of medicine Pub Date : 2025-12-01 Epub Date: 2025-01-13 DOI:10.1080/07853890.2025.2449584
Yu-Zhong Yu, Xiao Xie, Mao-Ping Cai, Ya-Ying Hong, Yang-Zi Ren, Xi Kang, Hai-Chen Yan, Yang Xiong, Hong Chen, Xing-Cheng Wu, Dao-Sheng Luo, Shan-Chao Zhao
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引用次数: 0

摘要

背景:我们之前描述了血浆外泌体代谢物在CRPC、PCa和TFC队列中的富集,并发现嘧啶代谢物的显著差异。pmg与几种癌症的临床预后相关,但其在前列腺癌中的生物学作用尚不清楚。方法:本研究提取98个可靠的pmg,分析其体细胞突变、表达水平及预后意义。基于与PCa预后相关的6个pmg,采用无监督聚类方法对PCa患者进行分类。比较各组间的TME、基因突变和免疫逃逸能力。开发了一种基于预测pmg的评分算法,称为pmg评分。鉴定了TK1,并通过功能丧失实验确定了TK1的生物学功能。随后进行RNA测序以确定与TK1功能的潜在机制相关的分子。结果:98个pmg中有6个同时在PCa中表现出差异表达,并与BCR相关。根据这6种pmg的表达水平将患者分为两类,反映了不同的临床结局和免疫细胞浸润特征。分析临床特征、肿瘤预后及功能注释。随后,我们使用这六个pmg构建了一个预后特征。结合其他临床特征,我们发现6种pmg的预后特征是PCa患者的独立预后因素。最后,我们发现在三个GEO数据集中,TK1在CRPC组织中的表达高于PCa组织。结果表明,TK1促进了PCa细胞的生长和转移。结论:我们为PCa患者的PMG特征提供了准确预测临床预后的证据。TK1在PCa细胞的进展中起着至关重要的作用,可以作为CRPC的潜在治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of pyrimidine metabolism-based molecular subtypes and prognostic signature to predict immune landscape and guide clinical treatment in prostate cancer.

Background: We previously described the enrichment of plasma exosome metabolites in CRPC, PCa, and TFC cohorts, and found significant differences in pyrimidine metabolites. The PMGs is associated with the clinical prognosis of several cancers, but its biological role in PCa is still unclear.

Methods: This study extracted 98 reliable PMGs, and analyzed their somatic mutations, expression levels, and prognostic significance. Unsupervised clustering was applied to classify patients with PCa into clusters based on six PMGs that were related to the prognosis of PCa. The TME, gene mutations, and immune escape ability were compared among the clusters. A scoring algorithm based on prognostic PMGs, referred to as the PMGscore, was developed. TK1 was identified and the biological functions of TK1 were determined using loss-of-function experiments. RNA sequencing was subsequently performed to determine the molecules associated with the underlying mechanisms of TK1 function.

Results: In total, six out of 98 PMGs simultaneously exhibited differential expression in PCa and were correlated with BCR. Patients were clustered into two clusters according to the expression levels of these six PMGs, which reflected distinct clinical outcomes and immune cell infiltration characteristics. Clinical features, tumor prognosis, and functional annotation were analyzed. Subsequently, we constructed a prognostic signature using these six PMGs. In combination with other clinical traits, we found that the six PMGs' prognostic signature was an independent prognostic factor for patients with PCa. Finally, we found that the expression of TK1 was higher in CRPC tissues than in PCa tissues in three GEO datasets. The results indicated that TK1 promotes the growth and metastasis of PCa cells.

Conclusions: We provide evidence for a PMG signature for PCa patients to accurately predict clinical prognosis. TK1 plays crucial roles in the progression of PCa cells and can be used as a potential therapeutic target for CRPC.

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