ArticleAnalytica chimica acta2025
Online monitoring and stable isotope tracing of cancer associated volatiles in murine model captures tumor associated markers in vivo.
Article in Analytica chimica acta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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5 authors.
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Abstract
backgroundThe imperative need for early cancer detection, which is crucial for improved survival rates in many severe cancers such as lung cancer, remains challenging due to the lack of reliable early-diagnosis technologies and robust biomarkers. To address this gap, innovative screening platforms are essential to unveil the chemical signatures of lung cancer and its treatments. It is established that the oxidative tumor environment induces alterations in host metabolic processes and influences endogenous volatile synthesis. Despite efforts, consensus on unique volatile markers for cancer detection has been elusive, partly due to genetic variation leading to metabolic heterogeneity in humans and the lack of standardized procedures for analytical analyses.
resultsIn this study, we utilized advanced secondary electrospray ionization (SESI) technique coupled with a high-resolution mass spectrometer (HRMS) to non-invasively monitor lung cancer volatiles in a pre-clinical mouse model in real time. Our findings revealed 651 dysregulated volatile features upon cancer onset and identified 36 features correlated with tumor size. Endogenous tracing of glucose metabolism highlighted the γ-glutamyl cycle as a downstream pathway implicated in lung cancer, driven by an imbalance in glutathione metabolism due to reactive oxygen species (ROS) accumulation. Notably, our study unveiled unique volatile changes associated with gemcitabine and cisplatin treatment, which significantly abrogated tumor growth in vivo. Furthermore, we identified 5-oxoproline as a volatile metabolite indicative of lung cancer response to treatment. SIGNIFICANCE: In conclusion, our SESI-HRMS based analysis of pre-clinical model systematically explores the volatile signatures of lung cancer, and provides a novel non-invasive platform that possess great potential for the real-time, confident, and sensitive detection and monitoring of lung cancer.
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