Lung cancer is a common
malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood
biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of
lung cancer. Integration of multiple
biomarkers or trans-omics results can improve the accuracy and reliability for
lung cancer diagnosis. As metabolic reprogramming is a hallmark of
lung cancer, metabolites, specifically
lipids might be useful for
lung cancer detection, yet systematic characterizations of metabolites in
lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of
lung cancer patients to construct an inclusive metabolomic atlas of
lung cancer. A comprehensive analysis of
lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma
lipid metabolites were compared and analyzed among different
lung cancer subtypes.
Alcohols,
amides, and
peptide metabolites were significantly increased in
lung cancer, while
carboxylic acids,
hydrocarbons, and
fatty acids were remarkably decreased.
Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with
lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in
lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision
therapy for
lung cancer.