Literature DB >> 33672144

Serum Free Amino Acid Profiling in Differential Diagnosis of Ovarian Tumors-A Comparative Study with Review of the Literature.

Agnieszka Horala1, Szymon Plewa2, Pawel Derezinski2, Agnieszka Klupczynska2, Jan Matysiak2, Ewa Nowak-Markwitz1, Zenon J Kokot3.   

Abstract

Proper preoperative ovarian cancer (OC) diagnosis remains challenging. Serum free amino acid (SFAA) profiles were investigated to identify potential novel biomarkers of OC and assess their performance in ovarian tumor differential diagnosis. Serum samples were divided based on the histopathological result: epithelial OC (n = 38), borderline ovarian tumors (n = 6), and benign ovarian tumors (BOTs) (n = 62). SFAA profiles were evaluated using aTRAQ methodology based on high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). Levels of eleven amino acids significantly differed between OC+borderline and BOTs. The highest area under the receiver operating characteristic curve (AUC of ROC) (0.787) was obtained for histidine. Cystine and histidine were identified as best single markers for early stage OC/BOT and type I OC. For advanced stage OC, seven amino acids differed significantly between the groups and citrulline obtained the best AUC of 0.807. Between type II OC and BOTs, eight amino acids differed significantly and the highest AUC of 0.798 was achieved by histidine and citrulline (AUC of 0.778). Histidine was identified as a potential new biomarker in differential diagnosis of ovarian tumors. Adding histidine to a multimarker panel together with CA125 and HE4 improved the differential diagnosis between OC and BOTs.

Entities:  

Keywords:  amino acids; biomarker; metabolic profiling; metabolomics; ovarian cancer; ovarian neoplasm; ovarian tumour

Mesh:

Substances:

Year:  2021        PMID: 33672144      PMCID: PMC7926859          DOI: 10.3390/ijerph18042167

Source DB:  PubMed          Journal:  Int J Environ Res Public Health        ISSN: 1660-4601            Impact factor:   3.390


  27 in total

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Authors:  Robert J Kurman; Ie-Ming Shih
Journal:  Am J Surg Pathol       Date:  2010-03       Impact factor: 6.394

2.  Integrative Systemic and Local Metabolomics with Impact on Survival in High-Grade Serous Ovarian Cancer.

Authors:  Anna Bachmayr-Heyda; Stefanie Aust; Katharina Auer; Samuel M Meier; Klaus G Schmetterer; Sabine Dekan; Christopher Gerner; Dietmar Pils
Journal:  Clin Cancer Res       Date:  2016-10-19       Impact factor: 12.531

3.  Diagnosis of early stage ovarian cancer by 1H NMR metabonomics of serum explored by use of a microflow NMR probe.

Authors:  Erwin Garcia; Chris Andrews; Jia Hua; Hyung L Kim; Dinesh K Sukumaran; Thomas Szyperski; Kunle Odunsi
Journal:  J Proteome Res       Date:  2011-02-22       Impact factor: 4.466

4.  Rapid mass spectrometric metabolic profiling of blood sera detects ovarian cancer with high accuracy.

Authors:  Manshui Zhou; Wei Guan; L DeEtte Walker; Roman Mezencev; Benedict B Benigno; Alexander Gray; Facundo M Fernández; John F McDonald
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2010-08-10       Impact factor: 4.254

5.  Large-scale profiling of metabolic dysregulation in ovarian cancer.

Authors:  Chaofu Ke; Yan Hou; Haiyu Zhang; Lijun Fan; Tingting Ge; Bing Guo; Fan Zhang; Kai Yang; Jingtao Wang; Ge Lou; Kang Li
Journal:  Int J Cancer       Date:  2014-06-17       Impact factor: 7.396

6.  Plasma free amino acid profiling of five types of cancer patients and its application for early detection.

Authors:  Yohei Miyagi; Masahiko Higashiyama; Akira Gochi; Makoto Akaike; Takashi Ishikawa; Takeshi Miura; Nobuhiro Saruki; Etsuro Bando; Hideki Kimura; Fumio Imamura; Masatoshi Moriyama; Ichiro Ikeda; Akihiko Chiba; Fumihiro Oshita; Akira Imaizumi; Hiroshi Yamamoto; Hiroshi Miyano; Katsuhisa Horimoto; Osamu Tochikubo; Toru Mitsushima; Minoru Yamakado; Naoyuki Okamoto
Journal:  PLoS One       Date:  2011-09-07       Impact factor: 3.240

7.  Survival analysis of revised 2013 FIGO staging classification of epithelial ovarian cancer and comparison with previous FIGO staging classification.

Authors:  E Sun Paik; Yoo-Young Lee; Eun-Jung Lee; Chel Hun Choi; Tae-Joong Kim; Jeong-Won Lee; Duk-Soo Bae; Byoung-Gie Kim
Journal:  Obstet Gynecol Sci       Date:  2015-03-16

8.  Usefulness of Amino Acid Profiling in Ovarian Cancer Screening with Special Emphasis on Their Role in Cancerogenesis.

Authors:  Szymon Plewa; Agnieszka Horała; Paweł Dereziński; Agnieszka Klupczynska; Ewa Nowak-Markwitz; Jan Matysiak; Zenon J Kokot
Journal:  Int J Mol Sci       Date:  2017-12-16       Impact factor: 5.923

9.  Combination of serum histidine and plasma tryptophan as a potential biomarker to detect clear cell renal cell carcinoma.

Authors:  Hyung-Ok Lee; Robert G Uzzo; Debra Kister; Warren D Kruger
Journal:  J Transl Med       Date:  2017-04-06       Impact factor: 5.531

10.  The application of metabolomics in ovarian cancer management: a systematic review.

Authors:  Yousra Ahmed-Salim; Nicolas Galazis; Timothy Bracewell-Milnes; David L Phelps; Benjamin P Jones; Maxine Chan; Maria D Munoz-Gonzales; Tomoko Matsuzono; James Richard Smith; Joseph Yazbek; Jonathan Krell; Sadaf Ghaem-Maghami; Srdjan Saso
Journal:  Int J Gynecol Cancer       Date:  2020-10-26       Impact factor: 3.437

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  2 in total

1.  Targeted Microchip Capillary Electrophoresis-Orbitrap Mass Spectrometry Metabolomics to Monitor Ovarian Cancer Progression.

Authors:  Samyukta Sah; Sylvia R Yun; David A Gaul; Andro Botros; Eun Young Park; Olga Kim; Jaeyeon Kim; Facundo M Fernández
Journal:  Metabolites       Date:  2022-06-09

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

Authors:  Eiji Hishinuma; Muneaki Shimada; Naomi Matsukawa; Daisuke Saigusa; Bin Li; Kei Kudo; Keita Tsuji; Shogo Shigeta; Hideki Tokunaga; Kazuki Kumada; Keigo Komine; Hidekazu Shirota; Yuichi Aoki; Ikuko N Motoike; Jun Yasuda; Kengo Kinoshita; Masayuki Yamamoto; Seizo Koshiba; Nobuo Yaegashi
Journal:  Toxins (Basel)       Date:  2021-06-30       Impact factor: 4.546

  2 in total

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