Integrative bioinformatic and functional analyses of genes related to non-small cell lung cancer in female
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摘要:
目的 通过对女性非小细胞肺癌(non-small cell lung cancer, NSCLC)相关基因的生物信息学分析和功能预测，探讨女性NSCLC的发病机制和预后生物标志物。 方法 从基因表达综合数据库(Gene Expression Omnibus, GEO)下载女性NSCLC患者的数据，使用limma包鉴定差异表达基因(differentially expressed genes, DEGs)。对DEGs进行基因本体(Gene Ontology, GO)和Kyoto Encyclopedia of Genes and Genomes(KEGG)分析和构建蛋白质相互作用(protein-protein interaction, PPI) 网络分析。利用igraph包筛选关键DEG，利用Oncomine数据库及NSCLC细胞系验证关键DEGs的表达情况，并采用Log-rank检验分析关键DEGs的表达水平与女性NSCLC患者预后的关联。 结果 共筛选出500个DEGs，GO富集分析表明，DEGs主要参与细胞增殖、细胞迁移和细胞外结构组成(均有P < 0.05)。KEGG富集分析表明，这些途径主要与细胞外基质(extracellular matrix, ECM)受体相互作用、蛋白质消化吸收和白细胞跨内皮迁移信号有关。通过PPI网络分析得到3个关键DEGs：白细胞介素-6(interleukin-6, IL-6)、表皮细胞生长因子(epidermal growth factor, EGF)和基质金属蛋白酶-9(matrix metalloprotein-9, MMP-9)。Oncomine数据库分析和NSCLC细胞系结果显示NSCLC组织和细胞系中IL-6的表达较低，而EGF和MMP-9的表达较高。Log-rank检验表明IL-6(χ2= 6.90，P=0.009)和EGF(χ2= 7.73，P=0.005)表达与女性NSCLC患者预后有关。 结论 在女性NSCLC患者中，IL-6和EGF可能是NSCLC发病与预后机制中的重要基因，是治疗的潜在靶点。 Abstract:Objective To explore the pathogenesis and prognostic biomarkers of non-small cell lung cancer (NSCLC) in female patients by bioinformatic analysis and functional prediction of potential NSCLC-associated genes. Methods Gene expression profile data for female patients with NSCLC were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified using limma package. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) analyses were performed for DEGs. The igraph package was used to screen the key DEGs from PPI. The expression pattern of key DEGs were confirmed in Oncomine database and NSCLC cell lines. The Log-rank test was used to assess the association between key DEGs and overall survival. Results A total of 500 DEGs were identified, and GO analysis showed that these genes were mainly involved in cell proliferation, cell migration, and extracellular structure organization (P < 0.05). The KEGG analysis indicated that these genes were primarily related to ECM-receptor interactions, protein digestion and absorption, and leukocyte transendothelial migration signalling. Three key DEGs [interleukin-6 (IL-6), epidermal growth factor (EGF), matrix metalloprotein-9 (MMP-9)] were screened from the PPI network. The expression of IL-6 was downregulated in NSCLC tissues and cell lines, while EGF and MMP-9 expressions were upregulated. The log-rank test found that IL-6 and EGF were associated with overall survival of female patients with NSCLC. Conclusions In female patients, IL-6 (χ2 =6.90, P=0.009) and EGF (χ2 =7.73, P=0.005) may be key genes for pathogenesis and prognosis of NSCLC, and represent novel therapeutic targets. -
图 1 GSE19804和GSE31210数据集中筛选DEGs
Figure 1. Screening DEGs in GSE19804 and GSE31210 datasets
图 2 DEGs的GO分析和KEGG分析
注：A：生物学过程；B：细胞成分；C：分子功能；D: KEGG富集分析的气泡图。
Figure 2. GO and KEGG analysis of DEGs
图 3 使用STRING在线工具对DEGs进行PPI网络分析
Figure 3. PPI network analysis of DEGs using STRING online tool
图 4 女性NSCLC组织样本和癌旁组织中IL-6、EGF和MMP-9的相对表达水平
注：A-C：GSE19804中表达情况(60对NSCLC组织和癌旁组织)；D-F：GSE31210中表达情况(121例NSCLC样本和9例癌旁组织)。
Figure 4. Relative expression levels of IL-6, EGF and MMP-9 in female NSCLC tissue samples and adjacent tissues
图 5 IL-6、EGF和MMP-9的表达水平与女性NSCLC患者生存期的关联
Figure 5. Relationship between expression levels of IL-6, EGF and MMP-9 and survival time of female patients with NSCLC
图 6 Oncomine数据库中IL-6、EGF和MMP-9表达水平分析
注：1~8代表 8个不同的研究数据集，红色代表过度表达，蓝色代表低表达。
Figure 6. Expression levels of IL-6, EGF and MMP-9 in Oncomine database
图 7 细胞中IL-6、EGF和MMP-9的相对表达水平
注：A：IL-6在16HBE、PC9、SK-MES-1细胞中的相对表达水平；B：EGF在16HBE、PC9、SK-MES-1细胞中的相对表达水平；C：MMP-9在16HBE、PC9、SK-MES-1细胞中的相对表达水平；a P＜0.01。
Figure 7. Relative expression levels of IL-6, EGF and MMP-9 in cells
表 1 各基因引物
Table 1. Primers for each gene
基因引物 引物序列 IL-6上游引物 GAGATACCACTCCCAACAGACC IL-6下游引物 AAGTGCATCATCGTTGTTCATACA EGF上游引物 TCTGAACCCGGACGGATTG EGF下游引物 GACATCGCTCGCGAACGTAG MMP-9上游引物 GAGATACCACTCCCAACAGACC MMP-9下游引物 CCGAGTTGGAACCACGACGC GAPDH上游引物 GGAGCGAGATCCCTCCAAAAT GAPDH下游引物 GGCTGTTGTCATACTTCTCATGG 
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