Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.
Gene identification for risk of relapse in stage I lung adenocarcinoma patients: a combined methodology of gene expression profiling and computational gene network analysis.
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DOI：
10.18632/oncotarget.8723
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发表时间：
2016-05-24
期刊：
影响因子：
--
通讯作者：
Crinò L
中科院分区：
文献类型：
--
作者：
Ludovini V;Bianconi F;Siggillino A;Piobbico D;Vannucci J;Metro G;Chiari R;Bellezza G;Puma F;Della Fazia MA;Servillo G;Crinò L
风险评估和治疗选择仍然是早期非小细胞肺癌（NSCLC）的一个挑战。本研究的目的是使用高通量技术和计算分析的组合来鉴定与切除的肺腺癌（AD）患者中的早期复发（ER）相比于无复发（NR）的风险相关的新基因。我们确定了18例I期AD患者（13例NR和5例ER）。对ER、NR和相应正常肺组织的冰冻标本进行基因芯片技术和定量PCR（Q-PCR）检测。进行基因网络计算分析以选择预测基因。使用一组独立的79个AD I期样品通过Q-PCR验证所选基因。从微阵列分析中，我们选择了50个基因，使用ER与NR的倍数变化比。通过Q-PCR在合并样本和患者样本（ER和NR）中单独验证。两种方法检测结果一致的基因有14个，25个。使用它们进行计算基因网络分析，鉴定出4个增加的基因（HOXA 10、CLCA 2、AKR 1B 10、FABP 3）和6个减少的基因（SCGB 1A 1、PGC、TFF 1、PSCA、SPRR 1B和PRSS 1）。此外，在一个独立的AD样本数据集中，我们发现FABP 3高表达和SCGB 1A 1低表达均与无病生存率（DFS）较差相关。我们的研究结果表明，通过基因表达和计算分析，可以定义复发风险增加的患者的特征基因谱，这可能成为辅助治疗患者选择的工具。
Risk assessment and treatment choice remains a challenge in early non-smallcell lung cancer (NSCLC). The aim of this study was to identify novel genes involved in the risk of early relapse (ER) compared to no relapse (NR) in resected lung adenocarcinoma (AD) patients using a combination of high throughput technology and computational analysis. We identified 18 patients (n.13 NR and n.5 ER) with stage I AD. Frozen samples of patients in ER, NR and corresponding normal lung (NL) were subjected to Microarray technology and quantitative-PCR (Q-PCR). A gene network computational analysis was performed to select predictive genes. An independent set of 79 ADs stage I samples was used to validate selected genes by Q-PCR. From microarray analysis we selected 50 genes, using the fold change ratio of ER versus NR. They were validated both in pool and individually in patient samples (ER and NR) by Q-PCR. Fourteen increased and 25 decreased genes showed a concordance between two methods. They were used to perform a computational gene network analysis that identified 4 increased (HOXA10, CLCA2, AKR1B10, FABP3) and 6 decreased (SCGB1A1, PGC, TFF1, PSCA, SPRR1B and PRSS1) genes. Moreover, in an independent dataset of ADs samples, we showed that both high FABP3 expression and low SCGB1A1 expression was associated with a worse disease-free survival (DFS). Our results indicate that it is possible to define, through gene expression and computational analysis, a characteristic gene profiling of patients with an increased risk of relapse that may become a tool for patient selection for adjuvant therapy.
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