| Ovarian cancer is one of the malignent female cancers in the reproductive system with the top mortality rate. Its progression is mostly asymptomatic, which results in its first diagnosis in a late stage of the disease. Currently there are no reliable diagnostic methods for early detection of ovarian cancer. Clinical staging of ovarian cancer may provide an accurate gist for the clinical treatment. It is essential to determine the state of ovarian cancer, formulate of treatment options and estimate the prognosis of ovarian cancer.Metabolomics is a new discipline of systems biology following genomics, transcriptomics and proteomics, which mainly studies the small molecule metabolites (molecular weight less than1000) when biological systems are stimulated or disturbed by a change. Metabolomics has unique advantages and broad applications in searching tumor metabolic markers and clarifing the pathogenesis of tumor. Because clinical staging can make a clear definition of state of ovarian cancer, so in this study metabolomic technologies were used to analyse the correlation between metabolite composition in urine of patients with ovarian cancer and clinical stage, in order to find clues for establishing simple and convenient methods used for screening patients with ovarian cancer and early detection of ovarian cancer.Methods1. Urine samples of15normal persons and56patients with primary epithelial ovarian cancer were collected in the morning, and metabolites in them were extracted with methanol.2. Metabolites were detected with high performance liquid chromatography-mass spectrometry (HPLC-MS), and then the three-dimensional data were extracted.3. Mass spectrometry data were analyzed with principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA).4. Databases were used to identify the metabolites screened by PCA and PLS-DA. One-way ANOVA and the least significant difference (LSD)t-test were done.ResultsUrine specimens were analysised by HPLC-MS. PCA results showed that samples of health people and patients with ovarian cancer in different clinical stages presented a separation trend. The improved classification results could be got with PLS-DA. The points selected with the combination of VIP value and correlation index were calculated using unpaired two-tailed student’s t-test, and then the statistically significant points were identified with databases. The metabolites in urine that could reflect the metabolic differences among health people and patients with ovarian cancer in different clinical stages were screened out, which were N-acetylneuraminate-9-phosphate,5’-methylthioadenosine, urate-3-ribonucleoside, pseudouridine, L-vainer, succinic acid, L-proline, and β-nicotinamide mononucleotide. The contents of these metabolites in urine were significantly different among health people and patients with ovarian cancer in different clinical stages (P<0.05or0.01), and increased with the progression of ovarian cancer.ConclusionThe urinary metabolite composition is different among patients with ovarian cancer in different clinical stages. There is a correlation between urinary metabolite composition and clinical stage of ovarian cancer. Cervical cancer is the second common cause of death caused by cancer for women in the world and seriously affects women’s health. Common treatments for cervical cancer are surgery, radiotherapy, chemotherapy, targeted therapy and immunotherapy. Chemotherapy occupies an important position in cervical cancer treatment. Paclitaxel (PTX), a compound extracted from yew bark, is one of the preferred chemotherapy drugs for cervical cancer and has a precise effect on cervical cancer.PTX can bind tubulin, enhance the stability of tubulin, impede cell mitosis, and thus exert anti-tumor effects. Moreover, PTX can also regulate the immune system and affect the metabolisms of cancer cells.Metabolomics is the science that studies the dynamic changes of metabolites when the living systems are stimulated or their genes change. Metabolomics seeks to analysis all of the metabolites of organisms quantitatively and qualitatively. This study used metabolomics techniques to observe the effects of PTX on the metabolisms of HeLa cells and probed the anti-cancer mechanisms of PTX at the metabolic level.Methods1. We analyze the cytotoxicity of PTX on HeLa cells with MTT test in order to determine the appropriate PTX concentration and treatment time for metabolomics study.2. HeLa cells were treated with PTX and collected, and intracellular metabolites were extracted. We detected metabolites with rapid resolution liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (RRLC-Q-TOF/MS).3. The differences of metabolites were analysed with the combination of principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Statistical analysis of different metabolites were performed using unpaired two-tailed t test, and with ap-value of<0.01was considered statistically significant.4. Agilent METLIN individual compounds database and library (PCDL) and KEGG databases were used to compare and retrieve statistically significant metabolites and determine the related metabolic pathways and key enzymes.Results1. PTX inhibited the proliferation of HeLa cells in the time-effect and concentration-effect relationships. When the drug concentration was100nmol/L and the treatment time was48h, the viability of HeLa cells was65%. This concentration of PTX and the treatment time were selected for the cell metabolic study.2. PTX could obviously decrease the contents of chitin, Nl-acetylspermine, L-tyrosine, nicotinamide adenine dinucleotide (NADH), the tripeptide composed by glutamine and two cysteines and the dipeptide composed by glycine and lysine in HeLa cells. It could also increase the contents of guanosine, ornithine, urea, lactic acid, serine, proline and the tripeptide composed by proline, lysine and glycine or proline, lysine and alanine markedly.ConclusionsPTX can significantly affect metabolisms of tyrosine, nucleotide, glutathione, pyrimidine, pyruvate, polyamine, amino sugar and nucleotide sugar in HeLa cells. The glycine serine and threonine metabolism and the arginine and proline metabolism were also significantly affected. It can also change contents of NADH and small peptides in HeLa cells. |
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