Dyrskjøt Lars, Zieger Karsten, Kruhøffer Mogens, Thykjaer Thomas, Jensen Jens L, Primdahl Hanne, Aziz Natasha, Marcussen Niels, Møller Klaus, Orntoft Torben F
Molecular Diagnostic Laboratory, Department of Clinical Biochemistry, Aarhus University Hospital, Denmark.
Clin Cancer Res. 2005 Jun 1;11(11):4029-36. doi: 10.1158/1078-0432.CCR-04-2095.
Cancer of the urinary bladder is a common malignant disease in the western countries. The majority of patients presents with superficial tumors with a high recurrence frequency, a minor fraction of these patients experience disease progression to a muscle invasive stage. No clinical useful molecular markers exist to identify patients showing later disease progression. The purpose of this study was to identify markers of disease progression using full-genome expression analysis.
We did a full-genome expression analysis (59,619 genes and expressed sequence tags) of superficial bladder tumors from 29 bladder cancer patients (13 without later disease progression and 16 with later disease progression) using high-density oligonucleotide microarrays. We used supervised learning for identification of the optimal genes for predicting disease progression. The identified genes were validated on an independent test set (74 superficial tumor samples) using in house-fabricated 60-mer oligonucleotide microarrays.
We identified a 45-gene signature of disease progression. By monitoring this progression signature in an independent test set, we found a significant correlation between our classifications and the clinical outcome (P < 0.03). The genes identified as differentially expressed were involved in regulating apoptosis, cell differentiation, and cell cycle and hence may represent potential therapeutic targets.
Our results indicate that it may be possible to identify patients with a high risk of disease progression at an early stage using a molecular signature present already in the superficial tumors. In this way, better treatment and follow-up regimens could be assigned to patients suffering from superficial bladder cancer.
膀胱癌是西方国家常见的恶性疾病。大多数患者表现为浅表性肿瘤，复发频率高，其中一小部分患者会进展为肌肉浸润性阶段。目前尚无临床可用的分子标志物来识别疾病进展较晚的患者。本研究的目的是通过全基因组表达分析来识别疾病进展的标志物。
我们使用高密度寡核苷酸微阵列对29例膀胱癌患者（13例无疾病进展较晚情况，16例有疾病进展较晚情况）的浅表膀胱肿瘤进行了全基因组表达分析（59,619个基因和表达序列标签）。我们使用监督学习来识别预测疾病进展的最佳基因。使用自制的60聚体寡核苷酸微阵列在独立测试集（74个浅表肿瘤样本）上对鉴定出的基因进行验证。
我们鉴定出了一个由45个基因组成的疾病进展特征。通过在独立测试集中监测这个进展特征，我们发现我们的分类与临床结果之间存在显著相关性（P < 0.03）。被鉴定为差异表达的基因参与调节细胞凋亡、细胞分化和细胞周期，因此可能代表潜在的治疗靶点。
我们的结果表明，利用浅表肿瘤中已存在的分子特征，有可能在早期识别出疾病进展风险高的患者。这样，对于浅表性膀胱癌患者可以制定更好的治疗和随访方案。
