Yu Zhaojun, Deng Huanhuan, Chao Haichao, Song Zhen, Zeng Tao
Urology Department, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
Oncol Lett. 2024 Sep 3;28(5):526. doi: 10.3892/ol.2024.14659. eCollection 2024 Nov.
Biochemical recurrence (BCR) is common in prostate cancer (PCa), and patients with BCR usually have a poor prognosis. Cuproptosis is a unique type of cell death, and copper homeostasis is crucial to the occurrence and development of malignancies. The present study aimed to explore the prognostic value of cuproptosis-related long non-coding RNAs (lncRNAs; CRLs) in PCa and to develop a predictive signature for forecasting BCR in patients with PCa. Using The Cancer Genome Atlas database, transcriptomic, mutation and clinical data were collected from patients with PCa. A total of 121 CRLs were identified using Pearson's correlation coefficient. Subsequently, a 6-CRL signature consisting of AC087276.2, CNNM3-DT, AC090198.1, AC138207.5, METTL14-DT and LINC01515 was created to predict the BCR of patients with PCa through Cox and least absolute shrinkage and selection operator regression analyses. Kaplan-Meier curve analysis demonstrated that high-risk patients had a low BCR-free survival rate. In addition, there was a substantial difference between the high- and low-risk groups in the immune microenvironment, immune therapy, drug sensitivity and tumor mutational burden. A nomogram integrating the Gleason score, 6-CRL signature and clinical T-stage was established and evaluated. Finally, the expression of signature lncRNAs in PCa cells was verified through reverse transcription-quantitative PCR. In conclusion, the 6-CRL signature may be a potential tool for making predictions regarding BCR in patients with PCa, and the prognostic nomogram may be considered a practical tool for clinical decision-making.
生化复发(BCR)在前列腺癌(PCa)中很常见,BCR患者的预后通常较差。铜死亡是一种独特的细胞死亡类型,铜稳态对恶性肿瘤的发生和发展至关重要。本研究旨在探讨铜死亡相关长链非编码RNA(lncRNAs;CRLs)在PCa中的预后价值,并建立一个预测模型来预测PCa患者的BCR。利用癌症基因组图谱数据库,收集了PCa患者的转录组、突变和临床数据。使用Pearson相关系数共鉴定出121个CRLs。随后,通过Cox回归和最小绝对收缩和选择算子回归分析,创建了一个由AC087276.2、CNNM3-DT、AC090198.1、AC138207.5、METTL14-DT和LINC01515组成的6-CRLs特征模型,以预测PCa患者的BCR。Kaplan-Meier曲线分析表明,高危患者的无BCR生存率较低。此外,高危组和低危组在免疫微环境、免疫治疗、药物敏感性和肿瘤突变负担方面存在显著差异。建立并评估了一个整合Gleason评分、6-CRLs特征模型和临床T分期的列线图。最后,通过逆转录定量PCR验证了PCa细胞中特征性lncRNAs的表达。总之,6-CRLs特征模型可能是预测PCa患者BCR的潜在工具,而预后列线图可被视为临床决策的实用工具。