Plasma metabolomic analysis in Thai EGFR-mutated non-small cell lung cancer patients
1
Issued Date
2025-01-01
Resource Type
eISSN
20010370
Scopus ID
2-s2.0-105018173364
Journal Title
Computational and Structural Biotechnology Journal
Volume
27
Start Page
4321
End Page
4331
Rights Holder(s)
SCOPUS
Bibliographic Citation
Computational and Structural Biotechnology Journal Vol.27 (2025) , 4321-4331
Suggested Citation
Thamlikitkul L., Wanichthanarak K., Manocheewa S., Limjiasahapong S., Phonsatta N., Thangvichien S., Panya A., Sirivatanauksorn Y., Poungvarin N., Khoomrung S. Plasma metabolomic analysis in Thai EGFR-mutated non-small cell lung cancer patients. Computational and Structural Biotechnology Journal Vol.27 (2025) , 4321-4331. 4331. doi:10.1016/j.csbj.2025.10.010 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/112629
Title
Plasma metabolomic analysis in Thai EGFR-mutated non-small cell lung cancer patients
Corresponding Author(s)
Other Contributor(s)
Abstract
Lung cancer remains the leading cause of cancer-related mortality worldwide, underscoring the urgent need for non-invasive approaches to improve diagnosis, patient stratification, and therapeutic monitoring. Metabolic reprogramming driven by oncogenic alterations—particularly Epidermal Growth Factor Receptor (EGFR) mutations in non-small cell lung cancer (NSCLC)—creates distinctive plasma signatures with clinical relevance. In this study, plasma metabolomic profiling revealed that amino acid and sugar metabolism exhibited the strongest discriminatory patterns. NSCLC patients consistently showed elevated glycine and reduced tryptophan and inositol compared with healthy controls. Distinct amino acid and organic acid shifts further differentiated EGFR-mutated from wild-type NSCLC, while alterations in tryptophan, valine, and oxalic acid characterized patients with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs). These findings underscore biologically relevant metabolic alterations associated with EGFR mutation and TKI resistance, supporting the potential of plasma metabolite profiles as minimally invasive indicators for molecular classification and treatment response in NSCLC.