Karmanos Cancer Institute Mclaren Flint, 4100 Beecher Road, 48532, Flint, MI, USA.
Department of Obstetrics and Gynecology, William Beaumont Health, Royal Oak, MI, USA.
Metabolomics. 2018 Nov 24;14(12):154. doi: 10.1007/s11306-018-1448-3.
Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy.
The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer.
A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls.
Using partial least squares-discriminant analysis, we observed significant separation between all groups (p < 0.05) following cross validation. We identified which metabolites were significantly perturbed in each EOC grade as compared with controls and report the biochemical pathways which were perturbed due to the disease. Among these metabolic pathways, ascorbate and aldarate metabolism was identified, for the first time, as being significantly altered in both LG and HG serous cancers. Further, we have identified potential biomarkers of EOC and generated predictive algorithms with AUC (CI) = 0.940 and 0.929 for HG and LG, respectively.
These previously unreported biochemical changes provide a framework for future metabolomic studies for the development of EOC biomarkers. Finally, pharmacologic targeting of the key metabolic pathways identified herein could lead to novel and effective treatments of EOC.
上皮性卵巢癌（EOC）仍然是妇科恶性肿瘤死亡的主要原因，其在全球的死亡率令人震惊。除了发病机制的差异外，区分高级别（HG）和低级别（LG）EOC 对于预测疾病进展和对化疗的反应性至关重要。
本研究旨在研究与 HG 和 LG 浆液性上皮性卵巢癌相关的组织代谢组。
采用一维质子磁共振（1D H NMR）光谱和靶向质谱（MS）相结合的方法对 HG、LG 浆液性 EOC 和对照组织的代谢组进行分析。
通过偏最小二乘判别分析，我们观察到所有组之间在交叉验证后均有显著分离（p < 0.05）。我们确定了与对照组相比，每个 EOC 级别中哪些代谢物明显受到干扰，并报告了由于疾病而受到干扰的生化途径。在这些代谢途径中，首次发现抗坏血酸和醛酸盐代谢在 LG 和 HG 浆液性癌症中均明显改变。此外，我们还鉴定了 EOC 的潜在生物标志物，并生成了预测算法，HG 和 LG 的 AUC（CI）分别为 0.940 和 0.929。
这些以前未报道的生化变化为 EOC 生物标志物的进一步代谢组学研究提供了框架。最后，针对本文确定的关键代谢途径进行药物靶向治疗可能会为 EOC 的治疗带来新的有效方法。
