Lung cancer remains a major public health concern and a leading cause of cancer-re-lated deaths worldwide. Despite its prevalence, existing diagnostic approaches for earlydetection face significant challenges, including limited clinical resources and insuffi-cient 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 de-tection. Metabolomics, in particular, provides a detailed analysis of cellular and tissuemetabolism, offering valuable insights into disease mechanisms. By examining endog-enous metabolites in biological systems, metabolomics has demonstrated strong poten-tial for early cancer detection and personalized therapy. In this study, we conducted anextensive review of online metabolomic databases, including the Metabolomics Work-bench, to identify critical metabolites associated with various forms of lung cancer. Ad-ditionally, using network analysis tools like Metagenes, we established links betweenmetabolomic genes and 43 genes involved in lung cancer progression. Our integratedanalysis reveals a comprehensive metabolic and molecular profile of lung cancer, high-lighting 10 key metabolic pathways—particularly amino acid metabolism—that playa role in disease development. These findings contribute to a deeper understanding oflung cancer biology and may guide future research and clinical strategies for improveddiagnosis and treatment.
Rafiepoor et al. (Sun,) studied this question.