Identification of Lung Cancer Metabolomics Profile and Molecular Interactions Using Bioinformatic Methods

Lung cancer remains a major public health concern and a leading cause of cancer-related deaths worldwide. despite its prevalence, existing diagnostic approaches for early detection face significant challenges, including limited clinical resources and insufficient screening techniques. as a result, many cases are diagnosed at advanced stages, delaying critical treatment. advances in omics technologies—such as metabolomics, proteomics, and genomics—have shown promise in improving early lung cancer detection. metabolomics, in particular, provides a detailed analysis of cellular and tissue metabolism, offering valuable insights into disease mechanisms. by examining endogenous metabolites in biological systems, metabolomics has demonstrated strong potential for early cancer detection and personalized therapy. in this study, we conducted an extensive review of online metabolomic databases, including the metabolomics workbench, to identify critical metabolites associated with various forms of lung cancer. additionally, using network analysis tools like metagenes, we established links between metabolomic genes and 43 genes involved in lung cancer progression. our integrated analysis reveals a comprehensive metabolic and molecular profile of lung cancer, highlighting 10 key metabolic pathways—particularly amino acid metabolism—that play a role in disease development. these findings contribute to a deeper understanding of lung cancer biology and may guide future research and clinical strategies for improved diagnosis and treatment.