随机生存森林在小细胞肺癌预后分析中的应用

[摘要] 目的 辨识与小细胞肺癌具有本质关联的基因变量，可以帮助临床医生制定个性化治疗方案，延长患者生存期，提高患者预后生活质量。 方法 共入组 117例小细胞肺癌患者，含41000个基因变量，8个一般特征。利用随机生存森林方法结合基因表达谱及预后数据从一系列基因变量中探索与小细胞肺癌具有密切相关的基因变量。 结果 一般特征及EGFR、K-ras、p53表达在预后上无明显差异；所挑选的前12个基因中，FTCD、BTC、PSMC4、SLC43A1与小细胞肺癌具有密切的关系，而UCHL5、PSMC4与PSMD7、PCSK4、VPS13D与VPS13A具有调控依赖关系。 结论 随机生存森林可以高效的辨识与预后具有密切相关的本质基因。

[关键字] 小细胞肺癌；随机生存森林；基因表达谱；生存分析；基因调控

[中图分类号] R734 [文献标识码] A [文章编号] 1673-9701（2016）17-0004-05

Application of random survival forests in the analysis of small cell lung cancer prognosis

XIE Ruifei1 WU Bo2

1.Department of Information， Hangzhou Tumor Hospital， Hangzhou 310002， China； 2.Department of Radiology， Taizhou Central Hospital in Zhejiang Province， Taizhou 318000， China

[Abstract] Objective To distinguish the genetic variables with essential relevance with small cell lung cancer， which is able to help clinical physicians to formulate customized therapeutic protocols， prolong patients' survival time， and improve patients' prognosis and life quality. Methods A total of 117 patients with small cell lung cancer were included， with 41000 genetic variables and 8 general characteristics. Random survival forests were applied， combined with gene expression profile and prognostic data， genetic variables closely related to small cell lung cancer were explored in a series of genetic variables. Results General characteristics and EGFR， K-ras and p53 expressions were not significantly different in prognosis； in the former 12 selected genes， FTCD， BTC， PSMC4， SLC43A1 were closely related to small cell lung cancer， but UCHL5， PSMC4 and PSMD7， PCSK4， VPS13D and VPS13A were in the dependent relation of regulation. Conclusion Random survival forests are able to effectively distinguish the essential genes closely related to the prognosis.

[Key words] Small cell lung cancer； Random survival forests； Gene expression profile； Survival analysis； Gene regulation

在全球范围，肺癌是最常见的恶性肿瘤之一，且死亡率较高，预后较差[1-3]。小细胞肺癌（small cell lung cancer，SCLC）较非小细胞肺癌（non-small cell lung cancer，NSCLC）预后更差。在我国，超过80%的小细胞肺癌5年存活率不超过10%[4，5]。因此，寻找与SCLC发生发展相关的基因和分子，对于肿瘤的诊断和治疗尤为重要[2，6，7]。

近年来，转化医学的研究逐渐被重视，越来越多的研究者致力于基因组学的研究。高维基因组数据和生存信息的结合可以帮助研究者从全新的角度认识个体生物学过程以及疾病的发生、发展及预后过程。随机生存森林（random survival forest，RSF）[8-13]可以在高维基因组数据中有效地结合生存信息，提取与预后相关的基因变量，指导临床医生对患者进行个性化治疗[14]。

1 资料与方法

1.1 临床资料

本文数据从117例小细胞肺癌患者中提取，共包含41000个基因，一般特征见表1。EGFR与性别、K-ras与性别及T分期具有较强的相关性。

1.2 随机生存森林

随机生存森林是在随机森林（Random Forest）基础上，加入生存分析，采用bootstrap方法从原始数据中有放回的随机抽取N个样本，建立生存树模型，而袋外37%样本测试生存树模型。

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（收稿日期：2016-04-29）