背景:卵巢癌是隐匿的,通常在疾病的晚期发现。因为卵巢是盆腔器官,其盆腔液代谢产物的变化可能与卵巢癌有关。
方法:使用液相色谱-串联质谱(LC-MS/MS)检测卵巢癌患者盆腔液的代谢组学变化,卵巢囊肿和子宫肌瘤。曲线下面积(AUC)分析用于评估脂质代谢产物和血液肿瘤指数的诊断性能。采用Pearson相关算法分析卵巢癌患者临床特征与脂质代谢产物的相关性。
结果:卵巢癌患者盆腔液中有24种脂质代谢产物明显改变(p<0.05)。棕榈酰肉碱,硫胺素,脂质代谢物,血液肿瘤指数(CA15-3和CA125)显示AUC>0.8,棕榈酰肉碱达到0.942。此外,我们发现一些脂质代谢产物与临床分期显著相关,腹腔水量,淋巴转移,复发(p<0.05,r>0.5)。
结论:特定脂质代谢产物水平是卵巢癌的潜在生物标志物,可能在卵巢癌的早期诊断和预后评估中起关键作用。
结论:我们的结果表明,盆腔代谢产物,尤其是一些脂质代谢产物,在卵巢癌的诊断中起着重要的作用。同时,脂质部分代谢产物与卵巢癌患者的临床表现和预后密切相关。我们相信我们的研究对文献做出了重大贡献,因为它提供了一种更有效的卵巢癌检测的潜在方法。
BACKGROUND: Ovarian cancer is insidious and usually detected in advanced stages of the disease. As the ovaries are pelvic organs, changes in their pelvic fluid metabolites may be associated with ovarian cancer.
METHODS: Metabolomic changes in the pelvic fluid were detected using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in patients with ovarian cancer, ovarian cysts and uterine fibroids. Area under the curve (AUC) analysis was used to assess the diagnostic performance of lipid metabolites and blood tumor indices. The Pearson correlation algorithm was used to analyze the correlation between clinical characteristics and lipid metabolites in ovarian cancer patients.
RESULTS: There were 24 lipid metabolites significantly changed in the pelvic fluid of ovarian cancer patients (p < 0.05). Palmitoylcarnitine, lipoamide, lipid metabolites, and blood tumor indices (CA15-3 and CA125) showed AUC > 0.8, with palmitoylcarnitine reaching a high of 0.942. In addition, we found that some lipid metabolites were significantly associated with the clinical stage, abdominal water volume, lymphatic metastasis, and recurrence (p < 0.05, r > 0.5).
CONCLUSIONS: Levels of specific lipid metabolites are potential biomarkers of ovarian cancer and may play a key role in the early diagnosis and prognostic assessment of ovarian cancer.
CONCLUSIONS: Our results showed that pelvic metabolites, especially some lipid metabolites, play an important role in the diagnosis of ovarian cancer. Meanwhile, partial lipid metabolites were closely associated with the clinical presentation and prognosis of patients with ovarian cancer. We believe that our study makes a significant contribution to the literature because it provides a potential approach that is more effective for ovarian cancer detection.