Kutilin D S, Guskova O N, Filippov F E, Maksimov A Yu
National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Rostov-on-Don, 344037 Russia.
Mol Biol (Mosk). 2025 Jan-Feb;59(1):80-116.
A search for efficient biomarkers of ovarian cancer is one of the current trends in gynecologic oncology. Metabolic profiling by ultra high-performance liquid chromatography and mass spectrometry (UHPLC-MS) yields information about the total set of low-molecular-weight metabolites of a patient's biological fluid sample. The metabolites may provide potential disease markers, and their combination with microRNA level data significantly increases the diagnostic value. To identify the potential noninvasive diagnostic markers of serous ovarian adenocarcinoma, the metabolomic profile and microRNA transcript levels were studied in urine samples of patients. The study included 60 patients diagnosed with serous ovarian adenocarcinoma and 20 women without a cancer history. Chromatographic separation of metabolites was performed on a Vanquish Flex UHPLC system coupled to an Orbitrap Exploris 480 mass spectrometer. A search for gene regulators of metabolites and microRNA regulators of genes was carried out using the Random forest machine learning method. The microRNA transcript levels in the urine were determined by real-time PCR (qPCR). LASSO-penalized logistic regression was used to build predictive models. In total, 26 compounds showed abnormal concentrations in the ovarian cancer (OC) patients compared with the control group, the set including kynurenine, phenylalanyl-valine, lysophosphatidylcholines (18:3, 18:2, 20:4, and 14:0), alanylleucine, L-phenylalanine, phosphatidylinositol (34:l), 5-methoxytryptophan, 2-hydroxymyristic acid, 3-oxocholic acid, indoleacrylic acid, lysophosphatidylserine (20:4), L-β-aspartyl-L-phenylalanine, myristic acid, decanoylcarnitine, aspartyl-glycine, malonylcarnitine, 3-hydroxybutyrylcarnitine, 3-methylxanthine, 2,6-dimethylheptanoylcarnitine, 3-oxododecanoic acid, N-acetylproline, L-octanoylcarnitine, and capryloylglycine. Metabolite-gene regulator (47 genes) and metabolite-microRNA regulator (613 unique microRNAs) relationships were established by the Random forest method. Levels of 85 microRNAs were validated by qPCR. Changes in transcript levels in the OC patients compared with the controls were observed for miR-382-5p, miR-593-3p, miR-29a-5p, miR-2110, miR-30c-5p, miR-181a-5p, let-7b-5p, miR-27a-3p, miR-370-3p, miR-6529-5p, miR-653-5p, miR-4742-5p, miR-2467-3p, miR-1909-5p, miR-6743-5p, miR-875-3p, miR-19a-3p, miR-208a-5p, miR-330-5p, miR-1207-5p, miR-4668-3p, miR-3193, miR-23a-3p, miR-12132, miR-765, miR-181b-5p, miR-4529-3p, miR-33b-5p, miR-17-5p, miR-6866-3p, miR-4753-5p, miR-103a-3p, miR-423-5p, miR-491-5p, miR-196b-5p, miR-6843-3p, miR-423-5p and miR-3184-5p. Thus, significant metabolomic imbalance in the urine was observed in the OC patients and was associated with changes in the levels of microRNAs that regulate the signaling pathways of the metabolites. The 26 compounds with abnormal concentrations and the levels of the microRNAs miR-33b-5p, miR-423-5p, miR-6843-3p, miR-4668-3p, miR-30c-5p, miR-6743-5p, miR-4742-5p, miR-1207-5p, and miR-17-5p in the urine were considered to be suitable as noninvasive diagnostic markers of OC.
寻找卵巢癌的有效生物标志物是妇科肿瘤学当前的研究趋势之一。通过超高效液相色谱和质谱联用（UHPLC-MS）进行代谢谱分析，可获取患者生物流体样本中低分子量代谢物的整体信息。这些代谢物可能提供潜在的疾病标志物，并且它们与微小RNA水平数据相结合可显著提高诊断价值。为了确定浆液性卵巢腺癌潜在的非侵入性诊断标志物，对患者尿液样本中的代谢组学特征和微小RNA转录水平进行了研究。该研究纳入了60例被诊断为浆液性卵巢腺癌的患者和20名无癌症病史的女性。代谢物的色谱分离在与Orbitrap Exploris 480质谱仪联用的Vanquish Flex UHPLC系统上进行。使用随机森林机器学习方法对代谢物的基因调节因子和基因的微小RNA调节因子进行了搜索。通过实时定量聚合酶链反应（qPCR）测定尿液中的微小RNA转录水平。采用LASSO惩罚逻辑回归构建预测模型。与对照组相比，共有26种化合物在卵巢癌（OC）患者中显示出异常浓度，包括犬尿氨酸、苯丙氨酰缬氨酸、溶血磷脂酰胆碱（18:3、18:2、20:4和14:0）、丙氨酰亮氨酸、L-苯丙氨酸、磷脂酰肌醇（34:1）、5-甲氧基色氨酸、2-羟基肉豆蔻酸、3-氧代胆酸、吲哚丙烯酸、溶血磷脂酰丝氨酸（20:4）、L-β-天冬氨酰-L-苯丙氨酸、肉豆蔻酸、癸酰肉碱、天冬氨酰甘氨酸、丙二酰肉碱、3-羟基丁酰肉碱、3-甲基黄嘌呤、2,6-二甲基庚酰肉碱、3-氧代十二烷酸、N-乙酰脯氨酸、L-辛酰肉碱和辛酰甘氨酸。通过随机森林方法建立了代谢物-基因调节因子（47个基因）和代谢物-微小RNA调节因子（613个独特的微小RNA）之间的关系。通过qPCR验证了85种微小RNA的水平。与对照组相比，在OC患者中观察到miR-382-5p、miR-593-3p、miR-29a-5p、miR-2110、miR-30c-5p、miR-181a-5p、let-7b-5p、miR-27a-3p、miR-370-3p、miR-6529-5p、miR-653-5p、miR-4742-5p、miR-2467-3p、miR-1909-5p、miR-6743-5p、miR-875-3p、miR-19a-3p、miR-208a-5p、miR-330-5p、miR-1207-5p、miR-4668-3p、miR-3193、miR-23a-3p、miR-12132、miR-765、miR-181b-5p、miR-4529-3p、miR-33b-5p、miR-17-5p、miR-6866-3p、miR-4753-5p、miR-103a-3p、miR-423-5p、miR-491-5p、miR-196b-5p、miR-6843-3p、miR-423-5p和miR-3184-5p的转录水平发生了变化。因此，在OC患者尿液中观察到显著的代谢组学失衡，并且这与调节代谢物信号通路的微小RNA水平变化相关。尿液中26种浓度异常的化合物以及微小RNA miR-33b-5p、miR-423-5p、miR-6843-3p、miR-4668-3p、miR-30c-5p、miR-6743-5p、miR-4742-5p、miR-1207-5p和miR-17-5p的水平被认为适合作为OC的非侵入性诊断标志物。
