早期肺腺癌和鳞状细胞癌患者中无组织学亚型特异性预后特征的证据
No evidence of histology subtype-specific prognostic signatures among lung adenocarcinoma and squamous cell carcinoma patients at early stages
摘要：
背景非小细胞肺癌（NSCLC）是肺癌的主要组织学类型，占病例的85%。疾病分期通常用于确定NSCLC患者的辅助治疗资格，然而，它是个体患者预后的不精确预测因素。目前，许多研究人员求助于微阵列技术来识别相关的遗传预后标志物，特别注意修剪或扩展Cox回归模型。在NSCLC中，腺癌（AC）和鳞状细胞癌（SCC）是两种主要的组织学亚型。已有研究表明，它们之间在潜在机制上存在根本差异，这促使我们假设可能存在与每个组织学亚型的预后相关的特定基因。结果提出了一种基于Cox回归模型的滤波器特征选择算法。将该方法应用于真实世界的微阵列数据，没有发现证据支持组织学特异性预后基因签名的存在。然而，获得了早期AC和SCC样品的31个基因的预后基因标签，当与其他相关标签相比时，其提供了相当的性能。结论我们的建议是概念上简单，直接实施。因此，希望其他研究者，特别是统计知识和经验较少的研究者，能够很容易地采用这种方法来检验自己的研究假设。
英文摘要：
Background Non-small cell lung cancer (NSCLC) is the predominant histological type of lung cancer, accounting for up to 85% of cases. Disease stage is commonly used to determine adjuvant treatment eligibility of NSCLC patients, however, it is an imprecise predictor of the prognosis of an individual patient. Currently, many researchers resort to microarray technology for identifying relevant genetic prognostic markers, with particular attention on trimming or extending a Cox regression model. Among NSCLC, adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two major histology subtypes. It has been demonstrated that there exist fundamental differences in the underlying mechanisms between them, which motivated us to postulate there might exist specific genes relevant to prognosis of each histology subtype. Results In this article, we propose a simple filterer feature selection algorithm with a Cox regression model as the base. Applying this method to a real-world microarray data, no evidence has been found to support the existence of histology-specific prognostic gene signature. Nevertheless, a 31-gene prognostic gene signature for the early-stage AC and SCC samples is obtained, which provides comparable performance when compared with other relevant signatures. Conclusions Our proposal is conceptually simple and straightforward to implement. Therefore, it is expected that other researchers, especially those with less statistical knowledge and experience, can adapt this method readily to test their own research hypotheses.
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