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无偏数据挖掘识别出可预测非惰性 Gleason 评分 7 前列腺癌的细胞周期转录本。

Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer.

作者信息

Johnston Wendy L, Catton Charles N, Swallow Carol J

机构信息

Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.

Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.

出版信息

BMC Urol. 2019 Jan 7;19(1):4. doi: 10.1186/s12894-018-0433-5.

Abstract

BACKGROUND

Patients with newly diagnosed non-metastatic prostate adenocarcinoma are typically classified as at low, intermediate, or high risk of disease progression using blood prostate-specific antigen concentration, tumour T category, and tumour pathological Gleason score. Classification is used to both predict clinical outcome and to inform initial management. However, significant heterogeneity is observed in outcome, particularly within the intermediate risk group, and there is an urgent need for additional markers to more accurately hone risk prediction. Recently developed web-based visualization and analysis tools have facilitated rapid interrogation of large transcriptome datasets, and querying broadly across multiple large datasets should identify predictors that are widely applicable.

METHODS

We used camcAPP, cBioPortal, CRN, and NIH NCI GDC Data Portal to data mine publicly available large prostate cancer datasets. A test set of biomarkers was developed by identifying transcripts that had: 1) altered abundance in prostate cancer, 2) altered expression in patients with Gleason score 7 tumours and biochemical recurrence, 3) correlation of expression with time until biochemical recurrence across three datasets (Cambridge, Stockholm, MSKCC). Transcripts that met these criteria were then examined in a validation dataset (TCGA-PRAD) using univariate and multivariable models to predict biochemical recurrence in patients with Gleason score 7 tumours.

RESULTS

Twenty transcripts met the test criteria, and 12 were validated in TCGA-PRAD Gleason score 7 patients. Ten of these transcripts remained prognostic in Gleason score 3 + 4 = 7, a sub-group of Gleason score 7 patients typically considered at a lower risk for poor outcome and often not targeted for aggressive management. All transcripts positively associated with recurrence encode or regulate mitosis and cell cycle-related proteins. The top performer was BUB1, one of four key MIR145-3P microRNA targets upregulated in hormone-sensitive as well as castration-resistant PCa. SRD5A2 converts testosterone to its more active form and was negatively associated with biochemical recurrence.

CONCLUSIONS

Unbiased mining of large patient datasets identified 12 transcripts that independently predicted disease recurrence risk in Gleason score 7 prostate cancer. The mitosis and cell cycle proteins identified are also implicated in progression to castration-resistant prostate cancer, revealing a pivotal role for loss of cell cycle control in the latter.

摘要

背景

新诊断的非转移性前列腺腺癌患者通常根据血液前列腺特异性抗原浓度、肿瘤T分期和肿瘤病理Gleason评分分为低、中、高疾病进展风险组。分类用于预测临床结果并指导初始治疗。然而,在结果中观察到显著的异质性,特别是在中风险组中,迫切需要额外的标志物来更准确地优化风险预测。最近开发的基于网络的可视化和分析工具促进了对大型转录组数据集的快速查询,跨多个大型数据集进行广泛查询应该能够识别出广泛适用的预测因子。

方法

我们使用camcAPP、cBioPortal、CRN和美国国立卫生研究院癌症研究所GDC数据门户对公开可用的大型前列腺癌数据集进行数据挖掘。通过识别具有以下特征的转录本来开发一组生物标志物测试集:1)在前列腺癌中丰度改变;2)在Gleason评分为7的肿瘤和生化复发患者中表达改变;3)在三个数据集(剑桥、斯德哥尔摩、纪念斯隆凯特琳癌症中心)中表达与生化复发时间的相关性。然后,使用单变量和多变量模型在验证数据集(TCGA-PRAD)中检查符合这些标准的转录本,以预测Gleason评分为7的肿瘤患者的生化复发。

结果

20个转录本符合测试标准,其中12个在TCGA-PRAD Gleason评分为7的患者中得到验证。这些转录本中的10个在Gleason评分3 + 4 = 7的患者中仍然具有预后价值,Gleason评分7的患者亚组通常被认为预后不良风险较低,且通常不进行积极治疗。所有与复发呈正相关的转录本都编码或调节有丝分裂和细胞周期相关蛋白。表现最佳的是BUB1,它是在激素敏感性和去势抵抗性前列腺癌中上调的四个关键MIR145-3P微小RNA靶点之一。SRD5A2将睾酮转化为其更活跃的形式,与生化复发呈负相关。

结论

对大型患者数据集进行无偏倚挖掘,确定了12个转录本,它们独立预测Gleason评分为7的前列腺癌患者的疾病复发风险。所确定的有丝分裂和细胞周期蛋白也与去势抵抗性前列腺癌的进展有关,揭示了细胞周期控制丧失在后者中的关键作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/78c6/6322345/83c55e49925f/12894_2018_433_Fig3_HTML.jpg

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