Title : Identification of prognostic biomarkers and potential therapeutic targets for lung cancer by metabolomics analysis
Abstract:
Rationale: Metabolic reprogramming is a hallmark of lung cancer and a source of druggable vulnerabilities. Bridging mechanistic metabolic signatures with clinical prognostication may reveal targetable pathways that inform precision therapy and biomarker guided trials. However, there is no metabolic study of lung cancer prognosis up to date.
Objectives: We aim to identify serum metabolites with prognostic value that are embedded in targetable mechanism pathways, quantify the additional prognostic value of serum metabolites, and explore potential pathways through mediation analysis and multi-omics integrative analysis.
Methods: We applied a two-phase analytic strategy to identify metabolites associated with lung cancer overall survival (OS) time, which having either main effects or metabolite-age interactions. In the discovery phase, we performed a metabolome-wide association study (MWAS) of lung cancer OS for 2,134 non-targeted serum metabolites on 327 patients from the Boston Lung Cancer Study (BLCS). Significant metabolites whose false discovery rate ≤ 5% were screened out. In the validation phase, they were again tested in 218 patients from BLCS.
Network and mediation analyses revealed cross-regulatory mechanisms between exogenous and endogenous metabolites. Using UK Biobank omics data and summary-level molecular quantitative trait loci (xQTL) data, we performed multi-omics integration to identify potential metabolite-based prognostic regulatory pathways.
Measurements and Main Results: Totally, 13 metabolites with main effects and 6 ones with metabolite-age interactions were reliably validated, demonstrating strong prognostic predictive value. The anti-inflammatory lipid mediator prostaglandin J2 served as a core metabolic hub, mediating the effects of specific exogenous metabolites. Multi-omics analysis further revealed trans-omics regulatory axis, e.g., carnosine metabolite - CNDP1 protein – AGAP3 gene expression – CDK5 DNA methylation.
Conclusions: This study identified metabolic biomarkers associated with lung cancer prognosis and their underlying pathways, highlighting the anti-inflammatory PGJ2 axis and CNDP1- carnosine pathway as potential therapeutic targets. These findings provide a mechanistic foundation for precision metabolic interventions and drug development in lung cancer.
