Gene expression profiling in association with interferon-alpha-sensitivity for the personalized treatment of renal cell carcinoma

背景我们分析了干扰素-α（IFNα）反应与基因表达谱之间的相关性，以预测IFNα敏感性，并确定了调节肾癌（RCC）IFNα反应的关键分子。为了根据IFNα应答评估RCC患者，使用3，840个克隆的基因表达谱芯片进行基因表达谱分析，然后通过聚类分析选择IFNα应答相关基因。基本上，RCC细胞系的IFNα应答通过MTT测定用300至10，000 IU/ml的IFNα处理来评价。微阵列，然后监督分层聚类分析，应用于选定的基因，根据IFNα的敏感性。为了观察IFNα对肾癌细胞基因表达谱的影响，采用基因芯片技术，在IFNα处理肾癌细胞3、6、12 h后，用定量RT-PCR检测mRNA表达水平。根据基因表达谱与IFNα敏感性之间的统计学相关性，建立了IFNα应答预测模型。根据14例肾细胞癌患者的基因芯片分析，选择的基因集可能是确定IFNα应答者的依据。通过基因芯片对IFNα敏感株和IFNα耐药株进行聚类分析，最终筛选出ADFP、MITF、MTUS 1、PME-1、MLLT 3、MLF 1和TNNT 1 7个基因作为肾癌细胞IFNα敏感相关基因的候选基因。我们进一步开发了一个模型来预测四种分子的肿瘤抑制作用，即，ADFP、MITF、MTUS 1和TNNT 1的表达水平进行多元线性回归分析（相关系数= 0.948，p =0.0291），并以原代培养的肾细胞癌细胞为模型进行验证。联合选择的基因的表达水平可以提供关于RCC中IFNα应答的预测信息。此外，IFNα对RCC的反应可能通过调节这些分子的表达水平来调节。

Background. We analyzed the correlation between interferon-α (IFNα) response and gene expression profiles to predict IFNα sensitivity and identified key molecules regulating the IFNα response in renal cell carcinoma (RCC).Methods. To evaluate patients with RCC according to IFNα-response, gene expression profiling was done using 3,840 clones microarray, and then IFNα-responder related genes were selected by clustering analysis. Basically, IFNα-response of RCC cell lines was evaluated by MTT assay with 300 to 10,000 IU/ml of IFNα treatment. Microarray, followed by supervised hierarchical clustering analysis, was applied to selected genes according to IFNα sensitivity. In order to find alteration of expression profiles induced by IFNα, sequential microarray analyses were performed at 3,6, and 12 hrs after IFNα treatment of RCC cell lines and mRNA expression level was confirmed using quantitative RT-PCR. A model for prediction of IFNα response was created according to the statistical correlation between gene expression profiles and IFNα-sensitivity.Results. According to microarray analysis in 14 patients with RCC,IFNα-responder might be identified by the selected gene sets. Microarray followed clustering analysis between IFNα-sensitive and IFNα-resistant line, seven genes, ADFP,MITF,MTUS1,PME-1,MLLT3,MLF1, and TNNT1, were eventually selected as candidates for IFNα-sensitivity-related genes in RCC cell lines. We further developed a model to predict tumor-inhibition with four molecules, i.e., ADFP,MITF,MTUS1, and TNNT1, using multiple linear regression analysis (coefficient=0.948,p=0.0291) and validated the model using primary cultured RCC cells.Conclusions. The expression levels of the combined selected genes can provide predictive information on the IFNα response in RCC. Furthermore, the IFNα-response to RCC might be modulated by regulation of the expression level of these molecules.