Literature DB >> 28990506

Metabolomic Heterogeneity of Urogenital Tract Cancers Analyzed by Complementary Chromatographic Techniques Coupled with Mass Spectrometry.

Arlette Yumba-Mpanga1, Wiktoria Struck-Lewicka1, Renata Wawrzyniak1, Marcin Markuszewski2, Marek Roslan2, Roman Kaliszan1, Michał Jan Markuszewski1.   

Abstract

BACKGROUND: In regard to urogenital tract cancer studies, an estimated 340,650 new cases and 58,360 deaths from genital system cancer and about 141,140 new cases and 29330 deaths from urinary system were projected to occur in the United States in 2012. The main drawbacks of currently available diagnostic tests constitute the low specificity, costliness and quite high invasiveness.
OBJECTIVE: The main goal of this pilot study was to determine and compare urine metabolic fingerprints in urogenital tract cancer patients and healthy controls.
METHOD: A comparative analysis of the metabolic profile of urine from 30 patients with cancer of the genitourinary system (bladder (n=10), kidney (n=10) and prostate (n=10)) and 30 healthy volunteers as a control group was provided by LC-TOF/MS and GCQqQ/ MS. The data analysis was performed by the use of U-Mann Whitney test or Student's t-test, principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA).
RESULTS: As a result, 33, 43, and 22 compounds were identified as statistically significant in bladder, prostate and kidney cancer, respectively, compared to healthy groups.
CONCLUSION: Diverse compounds such as purine, sugars, amino acids, nucleosides, organic acids which play a role in purine metabolism, in tricarboxylic acid cycle, in amino acid metabolism or in gut microbiota metabolism were identified. Only two metabolites namely glucocaffeic acid and lactic acid were found to be in common in studied three types of cancer. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Entities:  

Keywords:  GC-MS; LC-MS; bladder cancer; kidney cancer; metabolomics; prostate cancer.

Mesh:

Year:  2019        PMID: 28990506     DOI: 10.2174/0929867324666171006150326

Source DB:  PubMed          Journal:  Curr Med Chem        ISSN: 0929-8673            Impact factor:   4.530


  3 in total

1.  System level characterization of small molecule drugs and their affected long noncoding RNAs.

Authors:  Haixiu Yang; Yanan Jiang; Yunpeng Zhang; Yanjun Xu; Chunlong Zhang; Junwei Han; Fei Su; Xiaoqi Liu; Kai Mi; Bing Liu; Desi Shang
Journal:  Aging (Albany NY)       Date:  2019-12-18       Impact factor: 5.682

Review 2.  Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches.

Authors:  Nuria Gómez-Cebrián; José Luis Poveda; Antonio Pineda-Lucena; Leonor Puchades-Carrasco
Journal:  Cancers (Basel)       Date:  2022-01-25       Impact factor: 6.639

Review 3.  Advances and Perspectives in Prostate Cancer Biomarker Discovery in the Last 5 Years through Tissue and Urine Metabolomics.

Authors:  Ana Rita Lima; Joana Pinto; Filipa Amaro; Maria de Lourdes Bastos; Márcia Carvalho; Paula Guedes de Pinho
Journal:  Metabolites       Date:  2021-03-19
  3 in total

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