Laboratory and Department of Urology, Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.
CIBERehd. Plataforma de Bioinformática, Centro de Investigación Biomédica en red de Enfermedades Hepáticas y Digestivas, Spain.
Urol Oncol. 2021 Aug;39(8):493.e17-493.e25. doi: 10.1016/j.urolonc.2020.10.075. Epub 2020 Nov 11.
The purpose of the study was to develop an improved classifier for predicting biochemical recurrence (BCR) in clinically localized PCa patients after radical prostatectomy.
Retrospective study including 122 PCa patients who attended our department between 2000 and 2007. Gene expression patterns were analyzed in 21 samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in 101 independent PCa patients who underwent radical prostatectomy. Logistic regression analysis was used to identify individual predictors of BCR. A risk score for predicting BCR including clinicopathological and gene expression variables was developed. Interaction networks were built by GeneMANIA Cytoscape plugin.
A total of 37 patients developed BCR (36.6%) in a median follow-up of 120 months. Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR < 0.1). We found that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion, and positive margins were independent prognostic factors of BCR. A risk score generated using these variables was able to discriminate between 2 groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, P< 0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the 2 genes of the model were not found.
Combination of gene expression patterns and clinicopathological variables in a robust, easy-to-use, and reliable classifier may contribute to improve PCa risk stratification.
本研究旨在开发一种改良的分类器，用于预测接受根治性前列腺切除术的局部前列腺癌（PCa）患者的生化复发（BCR）。
回顾性研究包括 2000 年至 2007 年间在我科就诊的 122 例 PCa 患者。使用 Illumina 微阵列分析了 7 例局限性、6 例局部进展性和 8 例转移性 PCa 患者的 21 例样本中的基因表达模式。在 101 例接受根治性前列腺切除术的独立 PCa 患者中，通过定量 PCR 验证了 41 个基因的表达水平。使用逻辑回归分析确定 BCR 的个体预测指标。开发了一种包括临床病理和基因表达变量的预测 BCR 的风险评分。通过 GeneMANIA Cytoscape 插件构建互作网络。
中位随访 120 个月时，共有 37 例患者发生 BCR（36.6%）。在临床局限性和局部进展-转移性 PCa 组之间，有 3730 个转录本的表达水平存在差异（FDR < 0.1）。我们发现 ASF1B 和 MCL1 的表达以及 Gleason 评分、包膜外扩展、精囊侵犯和阳性切缘是 BCR 的独立预后因素。使用这些变量生成的风险评分能够区分两组患者，两组患者的 BCR 发生概率有显著差异（HR 6.24；95%CI 3.23-12.4，P<0.01），提高了这些变量各自的个体判别性能。在模型的 2 个基因之间没有发现直接相互作用。
在稳健、易用和可靠的分类器中，将基因表达模式与临床病理变量相结合，可能有助于改善 PCa 的风险分层。
