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用于预测前列腺癌术后生化复发的色氨酸代谢相关风险模型及分子亚型的开发与验证

Development and validation of tryptophan metabolism-related risk model and molecular subtypes for predicting postoperative biochemical recurrence in prostate cancer.

作者信息

Shao Yuan, Zhang Xiaolei, Zhang Yinchi, Liu Zihao, Yang Zhen, Liu Yang, Huang Hua, Wang Zeyuan, Fu Zhinan, Wang Yong

机构信息

Department of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.

Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, China.

出版信息

Transl Androl Urol. 2025 Apr 30;14(4):1082-1110. doi: 10.21037/tau-2025-39. Epub 2025 Apr 27.

DOI:10.21037/tau-2025-39
PMID:40376539
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12076229/
Abstract

BACKGROUND: Biochemical recurrence (BCR) following radical prostatectomy (RP) remains a major challenge in prostate cancer (PCa) management. Tryptophan metabolism plays a pivotal role in tumor progression and immune modulation. This study aimed to develop and validate a tryptophan metabolism-related risk model and molecular subtypes to predict BCR in PCa patients after RP. METHODS: The Cancer Genome Atlas-Prostate Adenocarcinoma (TCGA-PRAD) dataset, including 421 PCa patients, was analyzed to identify key tryptophan metabolism-related genes (TMRGs) using differential expression, univariate Cox, and the least absolute shrinkage and selection operator (LASSO) regression analyses. The tryptophan metabolism-related risk model was constructed through multivariate Cox regression, and tryptophan metabolism-related molecular subtypes were established using consensus clustering. External validation was conducted using an independent dataset, while immunohistochemistry (IHC) and single-cell sequencing further confirmed TMRG expression patterns and their roles in the tumor microenvironment (TME). RESULTS: The tryptophan metabolism-related risk model and molecular subtypes effectively stratified PCa patients into low- and high-risk groups or two molecular subtypes. High-risk PCa patients (n=211) and those in Cluster 1 (n=261) exhibited significantly poorer biochemical recurrence-free survival (BRFS) and distinct clinicopathological features, immune infiltration profiles, and TME characteristics. External validation confirmed the robustness of the tryptophan metabolism-related risk model and molecular subtypes. IHC and single-cell sequencing highlighted the expression patterns of TMRGs and their regulatory roles in the TME. CONCLUSIONS: This study established and validated tryptophan metabolism-related risk scores and molecular subtypes as reliable predictors of BCR in PCa patients after RP. These findings provide a foundation for personalized follow-up and treatment strategies, contributing to improved clinical outcomes in PCa management.

摘要

背景：根治性前列腺切除术（RP）后的生化复发（BCR）仍然是前列腺癌（PCa）管理中的一项重大挑战。色氨酸代谢在肿瘤进展和免疫调节中起关键作用。本研究旨在开发并验证一种色氨酸代谢相关风险模型和分子亚型，以预测RP后PCa患者的BCR。 方法：分析癌症基因组图谱-前列腺腺癌（TCGA-PRAD）数据集，其中包括421例PCa患者，通过差异表达、单变量Cox和最小绝对收缩和选择算子（LASSO）回归分析来识别关键的色氨酸代谢相关基因（TMRGs）。通过多变量Cox回归构建色氨酸代谢相关风险模型，并使用一致性聚类建立色氨酸代谢相关分子亚型。使用独立数据集进行外部验证，而免疫组织化学（IHC）和单细胞测序进一步证实了TMRGs的表达模式及其在肿瘤微环境（TME）中的作用。 结果：色氨酸代谢相关风险模型和分子亚型有效地将PCa患者分为低风险和高风险组或两种分子亚型。高风险PCa患者（n = 211）和聚类1中的患者（n = 261）表现出明显较差的无生化复发生存率（BRFS）以及不同的临床病理特征、免疫浸润谱和TME特征。外部验证证实了色氨酸代谢相关风险模型和分子亚型的稳健性。IHC和单细胞测序突出了TMRGs的表达模式及其在TME中的调节作用。 结论：本研究建立并验证了色氨酸代谢相关风险评分和分子亚型，作为RP后PCa患者BCR的可靠预测指标。这些发现为个性化随访和治疗策略提供了基础，有助于改善PCa管理的临床结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/558bca8c7d20/tau-14-04-1082-f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/0bfdf56dde56/tau-14-04-1082-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/16eb31047b72/tau-14-04-1082-f2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/b425e3bc952a/tau-14-04-1082-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/9c0e6730a6dd/tau-14-04-1082-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/282b8064c783/tau-14-04-1082-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/aae7a729858c/tau-14-04-1082-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/7806f4ed5d97/tau-14-04-1082-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/c71eb7622a96/tau-14-04-1082-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/73d428d203c4/tau-14-04-1082-f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/2b7c53fb1c81/tau-14-04-1082-f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/18f513f8368b/tau-14-04-1082-f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/558bca8c7d20/tau-14-04-1082-f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/0bfdf56dde56/tau-14-04-1082-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/16eb31047b72/tau-14-04-1082-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/24eabe21975a/tau-14-04-1082-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/c5b81269d19f/tau-14-04-1082-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/b425e3bc952a/tau-14-04-1082-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/9c0e6730a6dd/tau-14-04-1082-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/282b8064c783/tau-14-04-1082-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/aae7a729858c/tau-14-04-1082-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/7806f4ed5d97/tau-14-04-1082-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/c71eb7622a96/tau-14-04-1082-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/73d428d203c4/tau-14-04-1082-f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/2b7c53fb1c81/tau-14-04-1082-f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/18f513f8368b/tau-14-04-1082-f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8a79/12076229/558bca8c7d20/tau-14-04-1082-f14.jpg

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本文引用的文献

[1]
Recent Patterns and Trends in Global Prostate Cancer Incidence and Mortality: An Update.

Eur Urol. 2025-3

[2]
Targeting of TAMs: can we be more clever than cancer cells?

Cell Mol Immunol. 2024-12

[3]
Molecular mechanisms and therapeutic significance of Tryptophan Metabolism and signaling in cancer.

Mol Cancer. 2024-10-30

[4]
Preoperative multiparametric magnetic resonance imaging based risk stratification system for predicting biochemical recurrence after radical prostatectomy.

Surg Oncol. 2024-12

[5]
Association Between the Decipher Genomic Classifier and Prostate Cancer Outcome in the Real-world Setting.

Eur Urol Oncol. 2024-8-3

[6]
N6-methyladenosine-modified ALDH9A1 modulates lipid accumulation and tumor progression in clear cell renal cell carcinoma through the NPM1/IQGAP2/AKT signaling pathway.

Cell Death Dis. 2024-7-22

[7]
Metabolic adaptations in prostate cancer.

Br J Cancer. 2024-11

[8]
EAU-EANM-ESTRO-ESUR-ISUP-SIOG Guidelines on Prostate Cancer-2024 Update. Part I: Screening, Diagnosis, and Local Treatment with Curative Intent.

Eur Urol. 2024-8

[9]
Exploring Amino Acid Transporters as Therapeutic Targets for Cancer: An Examination of Inhibitor Structures, Selectivity Issues, and Discovery Approaches.

Pharmaceutics. 2024-1-30

[10]
Cancer statistics, 2024.

CA Cancer J Clin. 2024

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