上海交通大学学报（医学版）

上海交通大学学报（医学版） ›› 2017, Vol. 37 ›› Issue (9): 1188-.doi: 10.3969/j.issn.1674-8115.2017.09.001

• 论著（基础研究） • 上一篇    下一篇

基于非负矩阵分解模型构建胃癌分子分型及预后评估的案例分析

曹颖颖，朱小强，陈豪燕   

  1. 上海交通大学 医学院附属仁济医院，上海 200001
  • 出版日期:2017-09-28 发布日期:2017-10-10
  • 通讯作者: 陈豪燕，电子信箱：haoyan chen@shsmu.edu.cn
  • 作者简介:曹颖颖（1994—），女，硕士生；电子信箱：yying_cao@163.com
  • 基金资助:
    国家自然科学基金（31371273）；上海市教育委员会高校“青年东方学者”（QD2015003）；上海市教育委员会高峰高原学科建设计划（20161309）

Case study of the molecular classification and prognostic prediction of gastric cancer based on nonnegative matrix factorization#br#

CAO Ying-ying, ZHU Xiao-qiang, CHEN Hao-yan   

  1. Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200001, China
  • Online:2017-09-28 Published:2017-10-10
  • Supported by:
    National Natural Science Foundation of China, 31371273; “Youth Eastern Scholar” at Shanghai Institutions of Higher Learning, QD2015003; Shanghai Municipal Education Commission—Gaofeng Clinical Medicine Grant Support, 20161309

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摘要： 目的 · 探究基于非负矩阵分解（NMF）模型构建胃癌分子分型对预后评估的作用。方法 · 从 GEO 下载胃癌样本转录组芯片数 据集分别作为探索集和验证集，挖掘lncRNA 数据集，利用 Consensus Cluster Plus 软件包构建 NMF 分子分型模型。评估各亚组与患 者预后及各项临床指标的相关性。结果 · 基于 NMF 模型将所有样本分为高危、中危和低危 3 个亚组。结合临床病理信息，发现复发 时间、淋巴结转移阳性率、Lauren 分型、TNM 分期、发病年龄在 3 个亚组间的分布差异具有统计学意义。在 GSE62254 和 GSE15459 中生存分析提示高危组复发时间更短，多因素 Cox 风险比例回归分析提示基于 NMF 模型的分子分型是胃癌预后的独立预测因子。基 因集差异分析（GSVA）和基因集富集分析（GSEA）发现高危组富集多个与肿瘤发生相关的通路。结论 · 基于 NMF 模型构建的胃癌 分子分型对胃癌患者预后评估具有一定的指导意义。

关键词:  胃癌, 分子分型, 预后, 生物信息学

Abstract:

Objective · To explore the molecular classification and prognostic prediction of gastric cancer based on nonnegative matrix factorization (NMF).  Methods · Cases of gastric cancer were acquired from Gene Expression Omnibus (GEO). Expression profiling of lncRNA was performed by using a lncRNA-mining approach. NMF model was built with Consensus Cluster Plus package. The relationship among NMF subgroups and clinical relevance was assessed.  Results · According to the molecular classification based on NMF, samples were divided into three subgroups. Significant difference was observed in relapse state, lymph node ratio, Lauren classification, TNM stage and age of onset among three subgroups. High-risk group was identified with shortest relapse time by survival analysis both in GSE62254 and GSE15459. Multivariate Cox proportional-hazards regression showed that NMF model based molecular classfication could be regarded as an independent risk factor for gastric cancer. Gene set variance analysis (GSVA) and gene set enrichment analysis (GSEA) showed that the high-risk subgroup was enriched in several tumor development pathways.  Conclusion · Based on NMF model, the molecular classification of gastric cancer can be used for treatment decision and prognostic prediction.

Key words: gastric cancer, molecular classification, prognosis, bioinformatics