Identification of Lung Cancer metabolomics profile and molecular interactions using bioinformatic methods

Abstract

Lung cancer represents a significant public health challenge, representing a considerable contributor to cancer-related mortality. However, the current diagnostic methods for early detection of lung cancer are constrained by various limitations, such as inadequate clinical resources and effective screening modalities. Consequently, diagnosis frequently occurs at advanced stages, impeding timely treatment interventions. However, the emerging field of omics, including metabolomics, proteomics, and genomics, has shown some improvement in facilitating the early diagnosis of lung cancer. Metabolomics methodologies offer a comprehensive insight into the metabolic processes of cells and tissues, enabling a deeper understanding of the biological state. By analyzing endogenous metabolites within biological systems, metabolomics has exhibited substantial potential for early detection and personalized treatment of diverse cancers. In this study, we extensively explored online metabolomic databases, such as Metabolomics Workbench, to pinpoint key metabolites linked to all types of lung cancer. Furthermore, connections between metabolomic genes and 43 genes implicated in lung cancer progression have been established by employing network analysis tools like Metagenes. This integrated approach offers a comprehensive overview of the metabolic and molecular landscape of lung cancer, highlighting 10 metabolic pathways, particularly amino acid metabolism, involved in the pathogenesis of lung cancer. These findings provide valuable insights for further research and potential clinical applications in the diagnosis and management of this disease.