Open-Data Serum Metabolomics of Lung Adenocarcinoma for Equitable Early Detection under SDG Target 3.4
DOI:
https://doi.org/10.63230/jocsis.3.4.325Keywords:
Machine Learning, Metabolomics, SDG Target 3.4Abstract
Objective: Sustainable Development Goal (SDG) target 3.4 addresses premature non-communicable disease mortality, yet lung cancer leads cancer mortality among Indonesian men, and low-dose computed tomography screening presumes imaging capacity most low-income settings lack. This study asked which serum pathways are recoverable from open lung adenocarcinoma metabolomics and whether their enzymes are altered in tumor tissue. Method: Serum GC-TOF MS data from the ST000385 ADC2 cohort (43 cases, 43 controls, 152 compounds) were reanalysed in MetaboAnalyst 6.0 using PLS-DA with permutation testing, multivariate ROC, and KEGG enrichment. Enzymes were examined in TCGA-LUAD (483 tumor, 347 normal) with GEPIA2 and survival analysis, and the cohort ST000386 underwent metadata testing. Results: The one-component model separated the groups (R² = 0.492, Q² = 0.302, permutation p < 5 × 10⁻⁴) and reached an AUC of 0.882 on five metabolites, which a sensitivity analysis showed to rest on one correlated axis. Twenty metabolites passed a 5% FDR, and arginine biosynthesis was the most enriched pathway (FDR = 5.8 × 10⁻⁵), whereas the TCA cycle and taurine metabolism did not survive correction. LDHA, PKM and CDO1 predicted survival. ARG1, OTC and CPS1 medians fell in tumor while ASS1 and ARG2 rose modestly. ST000386 carried subject-level metadata that does not associate with its own metabolome. Novelty: The dominant pathway is arginine biosynthesis rather than the Warburg axis of earlier readings, and the enzyme layer shows a dysregulated rather than silenced urea cycle.
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