A novel three-CpG signature for lung adenocarcinoma diagnosis: integrating multi-omics, machine learning, and clinical validation.

Background: Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality worldwide. The identification of non-invasive biomarkers for early detection is critical to improving patient outcomes. Integrative analysis of DNA methylation and gene expression represents a promising approach for identifying cancer-specific diagnostic markers. This study aimed to identify and validate a LUAD-specific DNA methylation signature for the early detection of LUAD. Methods: An integrated analysis of DNA methylation data from the Illumina HumanMethylation450K array, including 455 tumor samples, 32 normal samples, and RNA-sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) LUAD cohort was performed. Differential methylation and expression were identified, and LUAD-specific markers were selected by excluding probes altered in 10 other cancer types. All combinations of five candidate cytosine-phosphate-guanine (CpG) sites (excluding single-site combinations) were constructed using four machine learning algorithms, generating 104 models. A stringent two-step filtering strategy required each model to achieve an area under the curve (AUC) >0.8, F1 score >0.8, and coefficient of variation (CV) <0.1 in at least four of the five validation datasets [including TCGA test set and four independent Gene Expression Omnibus (GEO) cohorts] and required each CpG combination to be validated by all four algorithms. Candidate methylation markers were experimentally validated by MethyLight quantitative polymerase chain reaction (qPCR) in 97 paired LUAD and 14 benign lung lesion samples. Results: We identified 6,840 differentially methylated sites (DMSs) and 3,020 differentially expressed genes (DEGs). Integration of multi-omics data revealed 132 genes with correlated methylation-expression patterns. After filtering for LUAD specificity, 36 methylation markers were retained. A three-CpG eXtreme Gradient Boosting (XGBoost) model (cg00817598 in Conclusions: A three-CpG methylation signature was identified that may serve as a highly specific and sensitive diagnostic biomarker for LUAD, supported by rigorous model selection, multi-dataset validation, and experimental confirmation. The ability of the signature to distinguish LUAD from other cancer types and benign lesions highlights its strong potential for clinical translation as a non-invasive assay for detection.