Exploring metabolomics biomarkers for evaluating the effectiveness of concurrent radiochemotherapy for cervical cancers

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

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.

Exploring Metabolic Signatures of Ex Vivo Tumor Tissue Cultures for Prediction of Chemosensitivity in Ovarian Cancer

Simple Summary

Women diagnosed with ovarian cancer have 5-year survival rates below 45%. Prediction of patient’s outcome and the onset of drug resistance are still major challenges. The patient’s drug response is influenced by the environment that surrounds the tumor cells. We previously showed that patient-derived tumor tissue can be kept in the lab, alive and retaining aspects of that environment. In this study, we exposed tumor tissue derived from ovarian cancer patients to the chemotherapy patients receive and identified metabolites released by the tumor tissue after treatment (metabolic footprint). Using machine learning, we uncovered metabolic signatures that discriminate tumor tissues with higher vs. lower drug sensitivity. We propose potential biomarkers involved in the production of specific building blocks of cells and energy generation processes. Overall, we established a platform to explore metabolic features of the complex environment of each patient’s tumor that can underpin the discovery of biomarkers of drug response.

Abstract

Predicting patient response to treatment and the onset of chemoresistance are still major challenges in oncology. Chemoresistance is deeply influenced by the complex cellular interactions occurring within the tumor microenvironment (TME), including metabolic crosstalk. We have previously shown that ex vivo tumor tissue cultures derived from ovarian carcinoma (OvC) resections retain the TME components for at least four weeks of culture and implemented assays for assessment of drug response. Here, we explored ex vivo patient-derived tumor tissue cultures to uncover metabolic signatures of chemosensitivity and/or resistance. Tissue cultures derived from nine OvC cases were challenged with carboplatin and paclitaxel, the standard-of-care chemotherapeutics, and the metabolic footprints were characterized by LC-MS. Partial least-squares discriminant analysis (PLS-DA) revealed metabolic signatures that discriminated high-responder from low-responder tissue cultures to ex vivo drug exposure. As a proof-of-concept, a set of potential metabolic biomarkers of drug response was identified based on the receiver operating characteristics (ROC) curve, comprising amino acids, fatty acids, pyrimidine, glutathione, and TCA cycle pathways. Overall, this work establishes an analytical and computational platform to explore metabolic features of the TME associated with response to treatment, which can leverage the discovery of biomarkers of drug response and resistance in OvC.

The role of selenoprotein P in the determining the sensitivity of cervical cancer patients to concurrent chemoradiotherapy: A metabonomics-based analysis

BACKGROUND

The effect of selenoprotein P (SELENOP) levels on the sensitivity of cervical cancer patients to concurrent chemoradiotherapy (CCRT) has not been reported. In this study, the effects of the variations in plasma SELENOP levels on the sensitivity of cervical cancer patients to CCRT were investigated using metabonomics.

METHODS

Two patient groups were evaluated, i.e., the case group: 11 patients with intermediate to advanced primary squamous cervical cancer, who developed resistance against CCRT, and the sensitivity group: 11 patients who did not develop resistance were matched in a 1:1 ratio (controls). Blood samples were collected before and after CCRT, and the plasma SELENOP levels were measured by ELISA. The different metabolites present in the plasma were analyzed by UPLC-MS-MS.

RESULTS

SELENOP levels exhibited a significant reduction in both the resistant and sensitive groups after CCRT (F = 50.705, P < 0.001), and interaction effects between sensitivity and pre-and post-treatment on SELENOP levels were observed (F = 7.414, P = 0.013). Further, a more significant reduction in the SELENOP levels was observed in the CCRT-resistant group (mean reduction, 0.028 µg/mL; P < 0.001) than in the sensitive group (mean reduction, 0.013 µg/mL; P = 0.006). Four metabolic biomarkers, i.e., C18, C19, C20 sphingomyelin, and phosphatidylcholine 20:2/22:6, were shown to be differentially expressed between the resistant and sensitive groups pre-and post-treatment. C20 sphingomyelin levels exhibited a significant correlation with SELENOP levels (r = -0.326, P = 0.031).

CONCLUSION

The levels of plasma SELENOP in the CCRT-resistant group decreased significantly, suggesting that SELENOP might affect the sensitivity by modulating lipid synthesis and metabolism.