Abstract

Early detection of lung cancer provides the highest potential for saving lives. To date, no routine screening method enabling early detection is available, which is a key factor in the disease’s high mortality rate. Copy number changes and DNA methylation alterations are good indicators of carcinogenesis and cancer prognosis. In this study, we attempted to combine profiles of DNA copy number and methylation patterns in 20 paired cancerous and noncancerous tissue samples from non-small cell lung cancer (NSCLC) patients, and we detected several clinically important genes with genetic and epigenetic relationships. Using array comparative genomic hybridization (aCGH), statistically significant differences were observed across the histological subtypes for gains at 1p31.1, 3q26.1, and 3q26.31–3q29 as well as for losses at 1p21.1, 2q33.3, 2q37.3, 3p12.3, 4q35.2, and 13q34 in squamous cell carcinoma (SQ) patients, and losses at 12q24.33 were measured in adenocarcinoma (AD) patients ( p < 0.05). In an analysis of DNA methylation at 1505 autosomal CpG loci that are associated with 807 cancer-related genes, we identified six and nine loci with higher and lower DNA methylation levels, respectively, in tumor tissue compared to non-tumor lung tissues from AD patients. In addition, three loci with higher and seven loci with lower DNA methylation levels were identified in tumor tissue from SQ patients compared to non-tumor lung tissue. Subsequently, we searched for regions exhibiting concomitant hypermethylation and genomic loss in both ADs and SQs. One clone representing 7p15.2 (which includes candidate genes such as HOXA9 and HOXA11) and one target ID representing HOXA9_E252_R were detected. Quantitative real-time PCR identified the potential candidate gene HOXA9 as being down-regulated in the majority of NSCLC patients. Moreover, following HOXA9 over-expression, the invasion of representative cell lines, A549 and HCC95, were significantly inhibited. Taken together, our results show that the combined profiling analysis technique is a useful tool for identifying biomarkers in lung cancer and that HOXA9 might be a potential candidate gene for the pathogenesis and diagnosis of NSCLC patients.

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