Genome-scale metabolic modeling and machine learning unravel metabolic reprogramming and mast cell role in lung cancer: a multi-level analysis.

Background: Lung cancer remains a leading cause of cancer-related deaths worldwide. Immune interactions, particularly involving mast cells, play a crucial role in cancer progression through their influence on immune modulation, angiogenesis, and tissue remodeling. Mast cells exhibit both pro-tumorigenic and anti-tumorigenic activities, but their metabolic adaptations in the lung cancer microenvironment remain poorly understood. The objective of this study is to elucidate the metabolic reprogramming in lung cancer cells and mast cells using genome-scale metabolic modeling (GSM) and machine learning through a multi-level approach, and to identify metabolic vulnerabilities and potential therapeutic targets. Methods: We conducted a comprehensive multi-level analysis of metabolic alterations in lung cancer using GSM and machine learning approaches. Forty-three paired lung tissue samples (healthy and cancerous) were used to develop metabolic models of lung cancer and mast cells. A random forest classifier was employed to distinguish between healthy and cancerous states and identify key metabolic signatures. We also developed a novel metabolic thermodynamic sensitivity analysis (MTSA) to assess metabolic vulnerabilities across physiological temperatures (36-40 ℃). Results: Our analysis revealed a significant reduction in resting mast cells in cancerous tissues. The random forest classifier accurately distinguished between healthy and cancerous states based on metabolic signatures. Lung cancer cells selectively upregulated valine, isoleucine, histidine, and lysine metabolism in the aminoacyl-tRNA pathway to support elevated energy demands. Mast cell metabolism exhibited enhanced histamine transport and increased glutamine consumption in the tumor microenvironment, suggesting a shift towards immunosuppressive activity. MTSA demonstrated impaired biomass production in cancerous mast cells across physiological temperatures, indicating specific metabolic vulnerabilities. Conclusions: Our study elucidates the metabolic adaptations of mast cells and lung cancer cells, highlighting their interplay in tumor progression. The identified metabolic signatures provide potential therapeutic targets and diagnostic markers for future investigation. The novel MTSA approach offers a framework for identifying temperature-dependent metabolic vulnerabilities in cancer cells that could be exploited for therapeutic interventions.