摘要
[目的]分析早期和晚期肺腺癌的差异基因和信号通路。[方法]从美国国立生物信息中心（NCBI）的GEO数据库下载GSE10072数据集，去除临床指标缺失的样本，按照TNM分期将肺腺癌样本分为早期（Ⅰ期．共16例）和晚期（Ⅲ-Ⅳ期，共15例）两组。原始数据经dChip进行质量检验、标准化，然后进行差异基因分析。从MsigDB数据库获得344个生物信号基因集，通过GSEA进行信号通路富集分析，[结果]获得SEMA3、PLAU、CDKN2A等14个明显差异基因，获取的差异基因主要与细胞凋亡、细胞黏附等过程密切相关。选取MsigDB中来源于Bicarta、KEGG、GenMAPP三大数据库的344个基因集进行富集分析，结果发现Death pathway、Leukocyte transemtothelial migration和Focaladhesion等22条信号通路在晚期肺腺癌中明显富集．富集通路主要涉及细胞凋亡、细胞黏附和迁移等过程。[结论]早期与晚期肺癌中存在一些明显的差异表达基因，有部分信号通路在晚期肺腺癌中明显富集。
{ Purpose] To analyze the differential expression genes and gene sets associated with lung adeno- carcinoma in early and advanced lung adenocarcinoma. [Methods] l,ung adenocarcinnma gene expression profile data GSE10072 were obtained from Gene Expression Omnibus （GEO） database of National Center for Biotechnology Informalion. Samples without clinical data were excluded. The samples were divided into early stage group （stage Ⅰ , 16 samples） and advanced stage grnup （stage Ⅲand Ⅳ, 15 samples）. Raw data were normalized,quality control and analyses of differentially expressed genes by dChip software. Three hundred and forty-four gene sets of cell signal pathways from MsigDB bank.The enrichment of gene sets was analyzed bv GSEA software. [Results] Fourteen differentially expressed genes including SEMA3,PLAU, CDKN2A were obtained by dChip analysis. Data mining showed that differentially expressed genes obtained were related with developmental programmed cell death,cell adhesion. Analysis of gene sets enrichment against 344 pathways from Bicarta,KEGG,GenMAPP,showed that three pathways including Death pathway, Leukocyte transendothelial migration and Foeal adhesion were enriched in advanced lung adenocarcinoma. [Conclusion] There are some differential expression genes between early aud advanced hmg adenocarcinoma,and some pathways are enriching in advanced hmg adenneareinnma.
出处
《中国肿瘤》
CAS
2013年第1期54-58,共5页
China Cancer
基金
广东省自然科学基金项目(S2011010004147)
广州医学院2011年科研基金博士启动基金(2011C06)
关键词
肺腺癌
表达谱
基因通路
基因富集
lung adenocareinoma
gene expression profile
signal pathway
GSEA
作者简介
通讯作者：王桂平，E-mail：docgpwang@yahoo.com.cn
