Association of Plasma Selenium and Its Untargeted Metabolomic Profiling with Cervical Cancer Prognosis

Abstract

Selenium is an essential trace element that shows beneficial or adverse health effects depending on the dose. However, its role in the prognosis of cervical cancer (CC) has been less reported. We aimed to explore the association between selenium status and prognosis in CC patients with different prognoses and to elucidate the underlying mechanism of selenium in CC prognosis. This cross-sectional observational study had a case-control design at the Harbin Medical University Cancer Hospital and was conducted using 29 CC cases with poor prognosis and 29 CC cases with good prognosis. Plasma selenium levels were measured using an atomic fluorescence spectrometer. Untargeted metabolomics was used to identify metabolites. Plasma selenium levels of the poor prognosis group (49.90 ± 13.81 µg/L) were lower than that of the good prognosis group (59.38 ± 13.00 µg/L, t = 2.69, P = 0.009). In the logistic regression analysis, plasma selenium levels were associated with lower poor prognosis risk [odds ratio (OR) = 0.952, 95% CI: 0.909-0.998]. Receiver operating characteristic curve analysis revealed an optimal cut-off point of plasma selenium levels ≤ 47.68 µg/L for poor prognosis of CC. Based on the cut-off selenium levels, patients with different prognoses were divided into high and low selenium groups. Metabolomic analysis revealed six differential metabolites among different prognoses with low and high selenium levels, and the glycerophospholipid (GPL) metabolism was enriched. Plasma selenium levels were positively correlated with metabolite levels. Our findings provided evidence that low plasma selenium levels may associate with a poor prognosis of CC. Low plasma selenium levels might suppress GPL metabolism and influence the prognosis of CC. This finding requires confirmation in future prospective cohort studies.

Keywords: Cervical cancer; Glycerophospholipid metabolism; Plasma selenium; Prognosis; Untargeted metabolomics analysis.