Background

DNA hypermethylation is a key event in oncogenesis and may act as a biomarker for the early detection of lung adenocarcinoma (LUAD). Here, we aimed to identify LUAD-specific methylation diagnostic biomarkers and explored potential mechanisms using data mining.

Methods

Using The Cancer Genome Atlas (TCGA) LUAD and GSE83842 datasets, we identified overlapping common differentially methylated positions (DMPs) with negative correlations between methylation and gene expression. Methylation profiles of the TCGA LUAD samples were compared with 185 blood samples and 370 lung squamous cell carcinoma (LUSC) samples to build a logistic regression model. Diagnosis performance was evaluated using an independent dataset.

Results

160 genes were aberrantly methylated in LUAD since stage I; these genes were enriched in DNA-binding transcription factor activity, multiple embryonic development processes, and cell signaling. A diagnostic prediction model based on 10 CpG could distinguish LUAD from LUSC (area under the curve: 0.943). The derived model showed higher sensitivity and specificity than the two existing models. The homeobox A1 gene exhibited significantly higher methylation levels in LUAD than in 10 other cancers, showing potential as a LUAD-specific diagnostic biomarker.

Conclusions

Our findings provided insights into DNA methylation alterations in LUAD and established LUAD-specific diagnostic biomarkers.

中文翻译：

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鉴定早期肺腺癌特有的诊断性DNA甲基化生物标志物。

背景

DNA高甲基化是肿瘤发生中的关键事件，并且可以作为早期检测肺腺癌（LUAD）的生物标记。在这里，我们旨在鉴定LUAD特异性甲基化诊断生物标志物，并探索了使用数据挖掘的潜在机制。

方法

使用癌症基因组图谱（TCGA）LUAD和GSE83842数据集，我们鉴定出重叠的常见差异甲基化位置（DMP），甲基化与基因表达之间呈负相关。将TCGA LUAD样品的甲基化谱图与185个血液样品和370个肺鳞状细胞癌（LUSC）样品进行比较，以建立逻辑回归模型。使用独立的数据集评估诊断性能。

结果

自第一阶段以来，LUAD中有160个基因异常甲基化；这些基因富含DNA结合转录因子活性，多种胚胎发育过程和细胞信号传导。基于10 CpG的诊断预测模型可以区分LUAD和LUSC（曲线下面积：0.943）。衍生的模型显示出比两个现有模型更高的敏感性和特异性。同源盒A1基因在LUAD中的甲基化水平明显高于其他10种癌症，显示出作为LUAD特异性诊断生物标记物的潜力。

结论

我们的发现提供了对LUAD中DNA甲基化改变的见解，并建立了LUAD特异性诊断生物标记物。