A circular network of coregulated sphingolipids dictates lung cancer growth and progression

BACKGROUND: Sphingolipid metabolism is among the top dysregulated pathways in non-small cell lung carcinomas (NSCLC). However, the molecular control of sphingolipid metabolic reprogramming in cancer progression remains unclear. METHODS: We first determined the correlation between sphingolipid metabolic gene expression and patient prognosis. We then carried out sphingolipidomics analysis of health individual and NSCLC patient sera as well as B3GNT5 and GAL3ST1 genetically perturbed NSCLC cell lines. We used these cell lines to perform tumorigenesis study to determine the cellular role of B3GNT5 and GAL3ST1 in cancer growth and progression. FINDINGS: The expression of B3GNT5 and GAL3ST1 among sphingolipid metabolic enzymes is most significantly associated with patient prognosis, whilst sphingolipidomics analysis of healthy individual and NSCLC patient sera identifies their metabolites, lacto/neolacto-series glycosphingolipid and sulfatide species, as potential biomarkers that were more effective than current clinical biomarkers for staging patients. Further network analysis of the sphingolipidomes reveals a circular network of coregulated sphingolipids, indicating that the lacto/neolacto-series glycosphingolipid/sulfatide balance functions as a checkpoint to determine sphingolipid metabolic reprograming during patient progression. Sphingolipidomics analysis of B3GNT5/GAL3ST1 genetically perturbed NSCLC cell lines confirms their key regulatory role in sphingolipid metabolism, while B3GNT5 and GAL3ST1 expression has an opposite role on tumorigenesis. INTERPRETATION: Our results provide new insights whereby B3GNT5 and GAL3ST1 differentially regulate sphingolipid metabolism in lung cancer growth and progression. FUNDING: This work was supported by the Natural Science Foundation of China (81872142, 81920108028); Guangzhou Science and Technology Program (201904020008); Guangdong Science and Technology Department (2020A0505100029, 2019A1515011802, 2020A1515011280, 2020B1212060018, 2020B1212030004); China Postdoctoral Science Foundation (2019M650226, 2019M650227).