Aberrant promoter methylation is frequently observed in different types of lung cancer. Epigenetic modifications are believed to occur before the clinical onset of the disease and hence hold a great promise as early detection markers. Extensive analysis of DNA methylation has been impeded by methods that are either too labor intensive to allow large-scale studies or not sufficiently quantitative to measure subtle changes in the degree of methylation. We used a novel quantitative DNA methylation analysis technology to complete a large-scale cytosine methylation profiling study involving 47 gene promoter regions in 96 lung cancer patients. Each individual contributed a lung cancer specimen and corresponding adjacent normal tissue. The study identified six genes with statistically significant differences in methylation between normal and tumor tissue (P < 10(-6)). We explored the quantitative methylation data using an unsupervised hierarchical clustering algorithm. The data analysis revealed that methylation patterns differentiate normal from tumor tissue. For validation of our approach, we divided the samples to train a classifier and test its performance. We were able to distinguish normal from lung cancer tissue with >95% sensitivity and specificity. These results show that quantitative cytosine methylation profiling can be used to identify molecular classification markers in lung cancer.