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目的通过代谢组学技术研究卵巢癌的早期诊断和疾病发病机制。方法应用GC-MS技术对21例卵巢癌和20例健康者对照血清样本的代谢组学进行观察,比较卵巢癌患者与健康者血清样本代谢的区别。结果结合多维数理统计分析技术与传统的单维数理统计分析技术,找到表征与卵巢癌相关生物信息变化的36个重要差异代谢物。卵巢癌患者血清中的差异代谢物主要涉及的受扰动的代谢通路包括:糖酵解、三羧酸循环、尿素循环、谷氨酰胺与谷氨酸代谢、脂肪酸代谢和脯氨酸代谢等。β-丙氨酸、棕榈酸和脯氨酸拟合为一个综合变量,在区分卵巢癌患者或健康者的ROC曲线分析的曲线下面积AUC值为0.995。结论利用卵巢癌血清代谢组学研究,可以为卵巢癌的早期诊断寻找潜在的生物标志物。
Objective To study the early diagnosis and pathogenesis of ovarian cancer through metabonomics. Methods Metabolomics of serum samples from 21 cases of ovarian cancer and 20 healthy controls were observed by GC-MS. The differences of serum samples between ovarian cancer patients and healthy controls were compared. Results Combined with the multidimensional mathematical statistics and analysis techniques and the traditional one-dimensional mathematical statistics and analysis techniques, we found 36 important differential metabolites that characterize the changes of biological information related to ovarian cancer. Differential Metabolites in Ovarian Cancer Patients Disturbed metabolic pathways primarily involved include glycolysis, tricarboxylic acid cycling, urea cycling, glutamine and glutamate metabolism, fatty acid metabolism, and proline metabolism. β-alanine, palmitic acid and proline were fitted into one comprehensive variable. The AUC value under the curve of ROC curve analysis for distinguishing ovarian cancer patients from healthy subjects was 0.995. Conclusions Using ovarian cancer serum metabolomics research, we can find potential biomarkers for the early diagnosis of ovarian cancer.