A Diagnostic Panel of DNA Methylation Biomarkers for Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common cancers and lethal diseases in the world. Recognition of the undetermined lung nodules at an early stage is useful for a favorable prognosis. However, there is no good method to identify the undetermined lung nodules and predict their clinical outcome. DNA methylation alteration is frequently observed in LUAD and may play important roles in carcinogenesis, diagnosis, and prediction. This study took advantage of publicly available methylation profiling resources and a machine learning method to investigate methylation differences between LUAD and adjacent non-malignant tissue. The prediction panel was first constructed using 338 tissue samples from LUAD patients including 149 non-malignant ones. This model was then validated with data from The Cancer Genome Atlas database and clinic samples. As a result, the methylation status of four CpG loci in homeobox A9 (HOXA9), keratin-associated protein 8-1 (KRTAP8-1), cyclin D1 (CCND1), and tubby-like protein 2 (TULP2) were highlighted as informative markers. A random forest classification model with an accuracy of 94.57% and kappa of 88.96% was obtained. To evaluate this panel for LUAD, the methylation levels of four CpG loci in HOXA9, KRTAP8-1, CCND1, and TULP2 of tumor samples and matched adjacent lung samples from 25 patients with LUAD were tested. In these LUAD patients, the methylation of HOXA9 was significantly upregulated, whereas the methylation of KRTAP8-1, CCND1, and TULP2 were downregulated obviously in tumor samples compared with adjacent tissues. Our study demonstrates that the methylation of HOXA9, KRTAP8-1, CCND1, and TULP2 has great potential for the early recognition of LUAD in the undetermined lung nodules. The findings also exhibit that the application of improved mathematic algorithms can yield accurate and particularly robust and widely applicable marker panels. This approach could greatly facilitate the discovery process of biomarkers in various fields.