Dataset Information

Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer.

ABSTRACT: Epithelial ovarian cancer (EOC) is a fatal gynecologic cancer, and its poor prognosis is mainly due to delayed diagnosis. Therefore, biomarker identification and prognosis prediction are crucial in EOC. Altered cell metabolism is a characteristic feature of cancers, and metabolomics reflects an individual's current phenotype. In particular, plasma metabolome analyses can be useful for biomarker identification. In this study, we analyzed 624 metabolites, including uremic toxins (UTx) in plasma derived from 80 patients with EOC using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). Compared with the healthy control, we detected 77 significantly increased metabolites and 114 significantly decreased metabolites in EOC patients. Especially, decreased concentrations of lysophosphatidylcholines and phosphatidylcholines and increased concentrations of triglycerides were observed, indicating a metabolic profile characteristic of EOC patients. After calculating the parameters of each metabolic index, we found that higher ratios of kynurenine to tryptophan correlates with worse prognosis in EOC patients. Kynurenine, one of the UTx, can affect the prognosis of EOC. Our results demonstrated that plasma metabolome analysis is useful not only for the diagnosis of EOC, but also for predicting prognosis with the variation of UTx and evaluating response to chemotherapy.

SUBMITTER: Hishinuma E

PROVIDER: S-EPMC8309959 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer.

Hishinuma Eiji E Shimada Muneaki M Matsukawa Naomi N Saigusa Daisuke D Li Bin B Kudo Kei K Tsuji Keita K Shigeta Shogo S Tokunaga Hideki H Kumada Kazuki K Komine Keigo K Shirota Hidekazu H Aoki Yuichi Y Motoike Ikuko N IN Yasuda Jun J Kinoshita Kengo K Yamamoto Masayuki M Koshiba Seizo S Yaegashi Nobuo N

Toxins 20210630 7

Epithelial ovarian cancer (EOC) is a fatal gynecologic cancer, and its poor prognosis is mainly due to delayed diagnosis. Therefore, biomarker identification and prognosis prediction are crucial in EOC. Altered cell metabolism is a characteristic feature of cancers, and metabolomics reflects an individual's current phenotype. In particular, plasma metabolome analyses can be useful for biomarker identification. In this study, we analyzed 624 metabolites, including uremic toxins (UTx) in plasma de ...[more]

PMID: 34209281

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Project description:Epithelial ovarian cancer (EOC) is one of the malignancies in women, which has the highest mortality. However, the microlevel mechanism has not been discussed in detail. The expression profiles GSE27651, GSE38666, GSE40595, and GSE66957 including 188 tumor and 52 nontumor samples were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were filtered using R software, and we performed functional analysis using the clusterProfiler. Cytoscape software, the molecular complex detection plugin and database STRING analyzed DEGs to construct protein-protein interaction network. We identified 116 DEGs including 81 upregulated and 35 downregulated DEGs. Functional analysis revealed that they were significantly enriched in the extracellular region and biosynthesis of amino acids. We next identified four bioactive compounds (vorinostat, LY-294002,trichostatin A, and tanespimycin) based on ConnectivityMap. Then 114 nodes were obtained in protein-protein interaction. The three most relevant modules were detected. In addition, according to degree ≥ 10, 14 core genes including FOXM1, CXCR4, KPNA2, NANOG, UBE2C, KIF11, ZWINT, CDCA5, DLGAP5, KIF15, MCM2, MELK, SPP1, and TRIP13 were identified. Kaplan-Meier analysis, Oncomine, and Gene Expression Profiling Interactive Analysis showed that overexpression of FOXM1, SPP1, UBE2C, KIF11, ZWINT, CDCA5, UBE2C, and KIF15 was related to bad prognosis of EOC patients. CDCA5, FOXM1, KIF15, MCM2, and ZWINT were associated with stage. Receiver operating characteristic (ROC) curve showed that messenger RNA levels of these five genes exhibited better diagnostic efficiency for normal and tumor tissues. The Human Protein Atlas database was performed. The protein levels of these five genes were significantly higher in tumor tissues compared with normal tissues. Functional enrichment analysis suggested that all the hub genes played crucial roles in citrate cycle tricarboxylic acid cycle. Furthermore, the univariate and multivariate Cox proportional hazards regression showed that ZWINT was independent prognostic indictor among EOC patients. The genes and pathways discovered in the above studies may open a new direction for EOC treatment.

Project description:BackgroundOvarian cancer is the second most common gynecologic cancer with high mortality rate and generally diagnosed in advanced stages. The 5-year disease-free survival is below 40%. MicroRNAs, subset of the non-coding RNA molecules, regulate the translation in post transcriptional level by binding to specific mRNAs to promote or degrade the target oncogenes or tumor suppressor genes. Abnormal expression of miRNAs were found in numerous human cancer, including ovarian cancer. Investigating the miRNAs derived from the peripheral blood samples can be used as a marker in the diagnose, treatment and prognosis of ovarian cancer. We aimed to find biological markers for early diagnosis of ovarian cancer by investigating BRCA1 gene mutation carrier monozygotic discordant twins and their high risk healthy family individual's miRNAs.MethodsThe study was conducted on monozygotic twins discordant for ovarian cancer, and the liquid biopsy exploration of miRNAs was performed on mononuclear cells that were isolated from the peripheral blood samples. The miRNA expression profile changes in the study were found by using microarray analysis. miRNA isolation procedure performed from the lymphocyte in accordance with the kit protocol. The presence and quality of the isolated miRNAs screened by electrophoresis. Raw data logarithmic analysis was studied by identifying the threshold, normalization, correlation, mean and median values. Target proteins were detected for each miRNA by using different algorithms.ResultsAfter the comparison of monozygotic discordant twins for epithelial ovarian carcinoma upregulation of the 4 miRNAs, miR-6131, miR-1305, miR-197-3p, miR-3651 and downregulation of 4 miRNAs, miR-3135b, miR-4430, miR-664b-5p, miR-766-3p were found statically significant.ConclusionsThe detected 99 miRNAs out of 2549 miRNAs might be used in the clinic as new biological indicators in the diagnosis and follow up of epithelial ovarian cancer with complementary studies. The miRNA expression profiles were identified to be statistically significant in the evaluation of ovarian cancer etiology, BRCA1 mutation status, and ovarian cancer risk in accordance with the obtained data. There is a need for validation of the miRNAs which were particularly detected between monozygotic twins and its association with ovarian cancer was emphasized in our study in wider cohorts including ovarian cancer patients, and healthy individuals.

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Dataset Information

Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer.

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Wide-Targeted Metabolome Analysis Identifies Potential Biomarkers for Prognosis Prediction of Epithelial Ovarian Cancer.

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