Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma
1
Issued Date
2025-02-28
Resource Type
eISSN
24058440
Scopus ID
2-s2.0-85217404583
Journal Title
Heliyon
Volume
11
Issue
4
Rights Holder(s)
SCOPUS
Bibliographic Citation
Heliyon Vol.11 No.4 (2025)
Suggested Citation
Chitta P., Barrow T.M., Dawangpa A., Christiani D.C., Poungvarin N., Sae-Lee C. Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma. Heliyon Vol.11 No.4 (2025). doi:10.1016/j.heliyon.2025.e42581 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/105330
Title
Blood-based biomarkers derived from tumor-informed DNA methylation analysis for lung adenocarcinoma
Author's Affiliation
Corresponding Author(s)
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Abstract
Objective: To identify robust markers of lung adenocarcinoma (LUAD) using DNA methylation profiles from blood samples informed by tissue lung adenocarcinoma. Methods: This study analyzed 56 LUAD blood samples from patients attending clinic at Siriraj Hospital, Thailand and 51 samples from healthy participants, using 644 tumor and 59 normal tissue methylome datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases for candidate gene identification. We performed comparative analysis to identify DNA methylation (DNAm) changes present in tumors that are also observable in blood, to be taken forward for validation. Results: DNAm profiling of lung tumor datasets identified 59,639 differentially methylated positions (DMPs), of which 17,251 exhibited a negative correlation with gene expression. In blood samples, 46,680 DMPs were identified among LUAD patients, which were enriched in pathways associated with the ribosome, spliceosome, cell cycle, ubiquitin mediated proteolysis and nucleocytoplasmic transport. Comparative analysis revealed a two DMP epigenetic signature of matching changes in both tissue and blood. This signature offered high diagnostic performance in distinguishing LUAD from normal lung tissue (AUC: 0.77–0.91) and in blood samples from LUAD patients (AUC:0.92–0.96). Similarly high performance was observed in two independent tissue validation datasets (AUC:0.90–0.92). Conclusions: Our novel two DMP signatures offer robust performance in both lung tissue and blood for the identification of LUAD.