Centre for Molecular Oncology, Barts Cancer Institute, London, United Kingdom.
Centre for Tumour Biology, Barts Cancer Institute, London, United Kingdom.
Genome Med. 2014 Dec 3;6(12):105. doi: 10.1186/s13073-014-0105-3. eCollection 2014.
Improved usage of the repertoires of pancreatic ductal adenocarcinoma (PDAC) profiles is crucially needed to guide the development of predictive and prognostic tools that could inform the selection of treatment options.
Using publicly available mRNA abundance datasets, we performed a large retrospective meta-analysis on 466 PDAC patients to discover prognostic gene signatures. These signatures were trained on two clinical cohorts (n = 70), and validated on four independent clinical cohorts (n = 246). Further validation of the identified gene signature was performed using quantitative real-time RT-PCR.
We identified 225 candidate prognostic genes. Using these, a 36-gene signature was discovered and validated on fully independent clinical cohorts (hazard ratio (HR) = 2.06, 95% confidence interval (CI) = 1.51 to 2.81, P = 3.62 × 10(-6), n = 246). This signature serves as a good alternative prognostic stratification marker compared to tumour grade (HR = 2.05, 95% CI = 1.45 to 2.88, P = 3.18 × 10(-5)) and tumour node metastasis (TNM) stage (HR = 1.13, 95% CI = 0.66 to 1.94, P = 0.67). Upon multivariate analysis with adjustment for TNM stage and tumour grade, the 36-gene signature remained an independent prognostic predictor of clinical outcome (HR = 2.21, 95% CI = 1.17 to 4.16, P = 0.01). Univariate assessment revealed higher expression of ITGA5, SEMA3A, KIF4A, IL20RB, SLC20A1, CDC45, PXN, SSX3 and TMEM26 was correlated with shorter survival while B3GNT1, NOSTRIN and CADPS down-regulation was associated with poor outcome.
Our 36-gene classifier is able to prognosticate PDAC independent of patient cohort and microarray platforms. Further work on the functional roles, downstream events and interactions of the signature genes is likely to reveal true molecular candidates for PDAC therapeutics.
迫切需要提高胰腺导管腺癌 (PDAC) 特征谱的使用，以指导预测和预后工具的开发，从而为治疗方案的选择提供信息。
使用公开的 mRNA 丰度数据集，我们对 466 名 PDAC 患者进行了大规模回顾性荟萃分析，以发现预后基因特征。这些特征在两个临床队列（n=70）中进行训练，并在四个独立的临床队列（n=246）中进行验证。使用定量实时 RT-PCR 进一步验证鉴定的基因特征。
我们确定了 225 个候选预后基因。使用这些基因，发现并验证了一个 36 基因特征在完全独立的临床队列中（风险比 (HR)=2.06，95%置信区间 (CI)=1.51 至 2.81，P=3.62×10(-6)，n=246）。与肿瘤分级（HR=2.05，95%CI=1.45 至 2.88，P=3.18×10(-5)）和肿瘤淋巴结转移（TNM）分期（HR=1.13，95%CI=0.66 至 1.94，P=0.67）相比，该特征是一种良好的替代预后分层标志物。在调整 TNM 分期和肿瘤分级的多变量分析中，36 基因特征仍然是临床结局的独立预后预测因子（HR=2.21，95%CI=1.17 至 4.16，P=0.01）。单因素评估显示，ITGA5、SEMA3A、KIF4A、IL20RB、SLC20A1、CDC45、PXN、SSX3 和 TMEM26 的高表达与生存时间缩短相关，而 B3GNT1、NOSTRIN 和 CADPS 的下调与不良预后相关。
我们的 36 基因分类器能够独立于患者队列和微阵列平台预测 PDAC。进一步研究该特征基因的功能作用、下游事件和相互作用，可能会揭示真正的 PDAC 治疗分子候选物。
