Due to the high mortality of lung cancer, early diagnosis followed by early effective treatment is the key to prognosis improvement, which demands to identify the biological targets. Therefore, a multistage screening analysis was used to identify biological targets of lung adenocarcinoma. Two independent datasets of DNA methylation and RNA expression microarray in lung adenocarcinoma and normal lung tissues were chosen from the Gene Expression Omnibus. The association between DNA methylation and gene expression was explored, and the prognostic value of the hub genes was also evaluated. In this study, 8533 differential methylation sites, mostly located in the CpG island region and corresponding to 2754 genes (referred as differential methylation genes), were detected from methylation microarray. Besides, we obtained 830 differential expression genes, including 570 downregulated and 260 upregulated genes, through differential expression analysis. Protein–protein interaction analysis identified CXCL12, GFR, KDR, MMP9, TEK, and TWIST as core nodes, involving in the process of tumor cell identification, cell growth, cytokine secretion, inflammatory response, and angiogenesis. Among them, MMP9 and TWIST1 were identified as more valuable biological targets for the early diagnosis and targeted therapy of lung cancer through Kaplan–Meier analysis of TCGA lung adenocarcinoma datasets. Our study should contribute to the diagnosis and treatment of lung adenocarcinoma.
Get full access to this article
View all access options for this article.
References
1.
BaderG.D., and HogueC.W. (2003). An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 4, 2.
2.
BeerD.G., KardiaS.L., HuangC.C., GiordanoT.J., LevinA.M., MisekD.E., et al. (2002). Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med, 8, 816–824.
3.
BurnsT.F., DobromilskayaI., MurphyS.C., GajulaR.P., ThiyagarajanS., ChatleyS.N., et al. (2013). Inhibition of TWIST1 leads to activation of oncogene-induced senescence in oncogene-driven non-small cell lung cancer. Mol Cancer Res, 11, 329–338.
4.
DasP.M., and SingalR. (2004). DNA methylation and cancer. J Clin Oncol, 22, 4632–4642.
5.
Diaz-LagaresA., Mendez-GonzalezJ., HervasD., SaigiM., PajaresM.J., GarciaD., et al. (2016). A novel epigenetic signature for early diagnosis in lung cancer. Clin Cancer Res, 22, 3361–3371.
6.
DibounI., WernischL., OrengoC.A., and KoltzenburgM. (2006). Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics, 7, 252.
7.
EdgarR., and BarrettT. (2006). NCBI GEO standards and services for microarray data. Nat Biotechnol, 24, 1471–1472.
8.
EdgarR., DomrachevM., and LashA.E. (2002). Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 30, 207–210.
9.
GautierL., CopeL., BolstadB.M., and IrizarryR.A. (2004). affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics, 20, 307–315.
10.
HendrixM.J., SeftorE.A., HessA.R., and SeftorR.E. (2003). Vasculogenic mimicry and tumour-cell plasticity: lessons from melanoma. Nat Rev Cancer, 3, 411–421.
11.
Huang daW., ShermanB.T., and LempickiR.A. (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4, 44–57.
12.
HungJ.J., YangM.H., HsuH.S., HsuW.H., LiuJ.S., and WuK.J. (2009). Prognostic significance of hypoxia-inducible factor-1alpha, TWIST1 and Snail expression in resectable non-small cell lung cancer. Thorax, 64, 1082–1089.
13.
IrizarryR.A., BolstadB.M., CollinF., CopeL.M., HobbsB., and SpeedT.P. (2003). Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res, 31, e15.
14.
IssaJ.P. (2004). CpG island methylator phenotype in cancer. Nat Rev Cancer, 4, 988–993.
15.
KarlssonA., JonssonM., LaussM., BrunnstromH., JonssonP., BorgA., et al. (2014). Genome-wide DNA methylation analysis of lung carcinoma reveals one neuroendocrine and four adenocarcinoma epitypes associated with patient outcome. Clin Cancer Res, 20, 6127–6140.
16.
KellyM.G., AlveroA.B., ChenR., SilasiD.A., AbrahamsV.M., ChanS., et al. (2006). TLR-4 signaling promotes tumor growth and paclitaxel chemoresistance in ovarian cancer. Cancer Res, 66, 3859–3868.
17.
KimY., and KimD.H. (2015). CpG island hypermethylation as a biomarker for the early detection of lung cancer. Methods Mol Biol, 1238, 141–171.
18.
KryczekI., WeiS., KellerE., LiuR., and ZouW. (2007). Stroma-derived factor (SDF-1/CXCL12) and human tumor pathogenesis. Am J Physiol Cell Physiol, 292, C987–C995.
19.
LawrenceM.S., StojanovP., PolakP., KryukovG.V., CibulskisK., SivachenkoA., et al. (2013). Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature, 499, 214–218.
20.
LeeB.H., YegnasubramanianS., LinX., and NelsonW.G. (2005). Procainamide is a specific inhibitor of DNA methyltransferase 1. J Biol Chem, 280, 40749–40756.
21.
LeipoldA., HessJ., and ZaouiK. (2015). [The epigenome. Target for innovative therapies in head and neck carcinoma]. HNO, 63, 786–791.
22.
LiuD., MartinV., FueyoJ., LeeO.H., XuJ., Cortes-SantiagoN., et al. (2010). Tie2/TEK modulates the interaction of glioma and brain tumor stem cells with endothelial cells and promotes an invasive phenotype. Oncotarget, 1, 700–709.
23.
LuT.P., TsaiM.H., LeeJ.M., HsuC.P., ChenP.C., LinC.W., et al. (2010). Identification of a novel biomarker, SEMA5A, for non-small cell lung carcinoma in nonsmoking women. Cancer Epidemiol Biomarkers Prev, 19, 2590–2597.
24.
MarsdenC.G., WrightM.J., PochampallyR., and RowanB.G. (2009). Breast tumor-initiating cells isolated from patient core biopsies for study of hormone action. Methods Mol Biol, 590, 363–375.
25.
MericoD., IsserlinR., StuekerO., EmiliA., and BaderG.D. (2010). Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One, 5, e13984.
26.
MiyamotoK., and UshijimaT. (2005). Diagnostic and therapeutic applications of epigenetics. Jpn J Clin Oncol, 35, 293–301.
27.
PascualG., AvgustinovaA., MejettaS., MartinM., CastellanosA., AttoliniC.S., et al. (2017). Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature, 541, 41–45.
28.
PoirierJ.T., GardnerE.E., ConnisN., MoreiraA.L., de StanchinaE., HannC.L., et al. (2015). DNA methylation in small cell lung cancer defines distinct disease subtypes and correlates with high expression of EZH2. Oncogene, 34, 5869–5878.
29.
ShannonP., MarkielA., OzierO., BaligaN.S., WangJ.T., RamageD., et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13, 2498–2504.
30.
SinghT., PrasadR., and KatiyarS.K. (2013). Inhibition of class I histone deacetylases in non-small cell lung cancer by honokiol leads to suppression of cancer cell growth and induction of cell death in vitro and in vivo. Epigenetics, 8, 54–65.
31.
StresemannC., and LykoF. (2008). Modes of action of the DNA methyltransferase inhibitors azacytidine and decitabine. Int J Cancer, 123, 8–13.
32.
SunB., ZhangD., ZhaoN., and ZhaoX. (2017). Epithelial-to-endothelial transition and cancer stem cells: two cornerstones of vasculogenic mimicry in malignant tumors. Oncotarget, 8, 30502–30510.
33.
SuvaM. L., RiggiN., and BernsteinB.E. (2013). Epigenetic reprogramming in cancer. Science, 339, 1567–1570.
34.
SzklarczykD., MorrisJ.H., CookH., KuhnM., WyderS., SimonovicM., et al. (2017). The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res, 45, D362–D368.
35.
TakahashiY., KitadaiY., BucanaC.D., ClearyK.R., and EllisL.M. (1995). Expression of vascular endothelial growth factor and its receptor, KDR, correlates with vascularity, metastasis, and proliferation of human colon cancer. Cancer Res, 55, 3964–3968.
36.
TomasettiM., AmatiM., NeuzilJ., and SantarelliL. (2017). Circulating epigenetic biomarkers in lung malignancies: from early diagnosis to therapy. Lung Cancer, 107, 65–72.
37.
TravisW.D., BrambillaE., NoguchiM., NicholsonA.G., GeisingerK.R., YatabeY., et al. (2011). International association for the study of lung cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol, 6, 244–285.
38.
VaureC., and LiuY. (2014). A comparative review of toll-like receptor 4 expression and functionality in different animal species. Front Immunol, 5, 316.
39.
WagenblastE., SotoM., Gutierrez-AngelS., HartlC.A., GableA.L., MaceliA.R., et al. (2015). A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature, 520, 358–362.
40.
WangD., YanL., HuQ., SuchestonL.E., HigginsM.J., AmbrosoneC.B., et al. (2012). IMA: an R package for high-throughput analysis of Illumina's 450K Infinium methylation data. Bioinformatics, 28, 729–730.
41.
WuF., LuM., QuL., LiD.Q., and HuC.H. (2015). DNA methylation of hMLH1 correlates with the clinical response to cisplatin after a surgical resection in non-small cell lung cancer. Int J Clin Exp Pathol, 8, 5457–5463.
42.
XieC., MaoX., HuangJ., DingY., WuJ., DongS., et al. (2011). KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res, 39, W316–W322.
43.
YanaiharaN., CaplenN., BowmanE., SeikeM., KumamotoK., YiM., et al. (2006). Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. Cancer Cell, 9, 189–198.
44.
YuG., WangL.G., HanY., and HeQ.Y. (2012). clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS, 16, 284–287.
45.
ZhangH., WroblewskiK., JiangY., PenneyB.C., AppelbaumD., SimonC.A., et al. (2015). A new PET/CT volumetric prognostic index for non-small cell lung cancer. Lung Cancer, 89, 43–49.
46.
ZhangY.B., HeF.L., FangM., HuaT.F., HuB.D., ZhangZ.H., et al. (2009). Increased expression of Toll-like receptors 4 and 9 in human lung cancer. Mol Biol Rep, 36, 1475–1481.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
