Genes suppressed by DNA methylation in non-small cell lung cancer reveal the epigenetics of epithelial–mesenchymal transition

Background

DNA methylation is associated with aberrant gene expression in cancer, and has been shown to correlate with therapeutic response and disease prognosis in some types of cancer. We sought to investigate the biological significance of DNA methylation in lung cancer.

Results

We integrated the gene expression profiles and data of gene promoter methylation for a large panel of non-small cell lung cancer cell lines, and identified 578 candidate genes with expression levels that were inversely correlated to the degree of DNA methylation. We found these candidate genes to be differentially methylated in normal lung tissue versus non-small cell lung cancer tumors, and segregated by histologic and tumor subtypes. We used gene set enrichment analysis of the genes ranked by the degree of correlation between gene expression and DNA methylation to identify gene sets involved in cellular migration and metastasis. Our unsupervised hierarchical clustering of the candidate genes segregated cell lines according to the epithelial-to-mesenchymal transition phenotype. Genes related to the epithelial-to-mesenchymal transition, such as AXL, ESRP1, HoxB4, and SPINT1/2, were among the nearly 20% of the candidate genes that were differentially methylated between epithelial and mesenchymal cells. Greater numbers of genes were methylated in the mesenchymal cells and their expressions were upregulated by 5-azacytidine treatment. Methylation of the candidate genes was associated with erlotinib resistance in wild-type EGFR cell lines. The expression profiles of the candidate genes were associated with 8-week disease control in patients with wild-type EGFR who had unresectable non-small cell lung cancer treated with erlotinib, but not in patients treated with sorafenib.

Conclusions

Our results demonstrate that the underlying biology of genes regulated by DNA methylation may have predictive value in lung cancer that can be exploited therapeutically.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-1079) contains supplementary material, which is available to authorized users.     

基于蛋白互作网络识别非小细胞肺癌相关基因功能模块

本研究对非小细胞肺癌(non-small cell lung carcinoma,NSCLC)基因表达数据进行差异表达分析,并与蛋白质相互作用网络(PPIN)数据进行整合,进一步利用Heinz搜索算法识别NSCLC相关的基因功能模块,并对模块中的基因进行功能(GO term)和通路(KEGG)富集分析,旨在探究肺癌发病分子机制。蛋白互作网络分析得到一个包含96个基因和117个相互作用的功能模块,以及8个对NSCLC的发生和发展起到关键作用候选基因标志物。富集分析结果表明,这些基因主要富集于基因转录催化及染色质调控等生物学过程,并在基础转录因子、黏着连接、细胞周期、Wnt信号通路及HTLV-Ⅰ感染等生物学通路中发挥重要作用。本研究对非小细胞肺癌相关的基因和生物学通路进行预测,可用于肺癌的早期诊断和早期治疗,以降低肺癌死亡率。     

Prediction of drug efficacy for cancer treatment based on comparative analysis of chemosensitivity and gene expression data

The NCI60 database is the largest available collection of compounds with measured anti-cancer activity. The strengths and limitations for using the NCI60 database as a source of new anti-cancer agents are explored and discussed in relation to previous studies. We selected a sub-set of 2333 compounds with reliable experimental half maximum growth inhibitions (GI(50)) values for 30 cell lines from the NCI60 data set and evaluated their growth inhibitory effect (chemosensitivity) with respect to tissue of origin. This was done by identifying natural clusters in the chemosensitivity data set and in a data set of expression profiles of 1901 genes for the corresponding tumor cell lines. Five clusters were identified based on the gene expression data using self-organizing maps (SOM), comprising leukemia, melanoma, ovarian and prostate, basal breast, and luminal breast cancer cells, respectively. The strong difference in gene expression between basal and luminal breast cancer cells was reflected clearly in the chemosensitivity data. Although most compounds in the data set were of low potency, high efficacy compounds that showed specificity with respect to tissue of origin could be found. Furthermore, eight potential topoisomerase II inhibitors were identified using a structural similarity search. Finally, a set of genes with expression profiles that were significantly correlated with anti-cancer drug activity was identified. Our study demonstrates that the combined data sets, which provide comprehensive information on drug activity and gene expression profiles of tumor cell lines studied, are useful for identifying potential new active compounds.