LC-MS/MS-based clinical value exploration of the methionine cycle in luminal breast cancer
Wenxuan Wu1, Yifeng Tu2, Han Yao3
1Department of Clinical Medicine, First Clinical Medicine College, Nanjing Medical University, Nanjing, Jiangsu 211166, P.R. China.
Abstract:
A group of patients with luminal breast cancer progresses to advanced stages because of endocrine therapy failure. While palbociclib improves the disease prognosis in the clinic, resistance after treatment remains a prominent issue. As a core systemic metabolic component, the methionine cycle currently lacks systematic research on luminal breast cancer and palbociclib resistance. The present study enrolled 146 patients with luminal breast cancer and 36 HCs from Jiangsu Cancer Hospital (Nanjing, China). Liquid chromatography-tandem mass spectrometry was used to detect the methionine cycle-related metabolites in the plasma. The Mann-Whitney U test was used to compare intergroup differences and diagnostic efficacy was evaluated using receiver operating characteristic curve analysis. Least absolute shrinkage and selection operator was applied to screen resistance-related variables, after which a nomogram prediction model was constructed. Plasma levels of methionine, S-adenosylmethionine (SAM), S-adenosylhomocysteine (SAH) and homocysteine were lower in patients with luminal breast cancer when compared with HCs, with an AUC of 0.8130 for the combined metabolites in diagnosis. Late-stage patients had lower methionine, SAM, SAH, as well as the SAM/SAH ratio than early-stage patients, with combined metabolites achieving an AUC of 0.9228. Additionally, palbociclib-resistant patients had lower SAM, SAH and homocysteine levels, with combined metabolites showing an AUC of 0.9229 for resistance identification. Finally, five variables were screened and used to construct a nomogram prediction model, with an AUC >0.9000 in the training set and 0.8000 in the internal validation sets. The present results suggest that methionine cycle-related metabolites are promising potential biomarkers for the diagnosis and progression assessment of luminal breast cancer. Furthermore, the constructed nomogram prediction model provides a new strategy for timely clinical treatment and intervention.
