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04 April 2017 : Laboratory Research  

Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors

Bo Li1ABCDEF, Boan Li1ADG, Tongsheng Guo1ACEF, Zhiqiang Sun1ACD, Xiaohan Li12BC, Xiaoxi Li1BC, Han Wang1CD, Weijiao Chen1B, Peng Chen1BF, Mengran Qiao1F, Lifang Xia1F, Yuanli Mao12AG*

DOI: 10.12659/MSM.901064

Med Sci Monit 2017; 23:1636-1644


BACKGROUND: Differentiation of malignant from benign liver tumors remains a challenging problem. In recent years, mass spectrometry (MS) technique has emerged as a promising strategy to diagnose a wide range of malignant tumors. The purpose of this study was to establish classification models to distinguish benign and malignant liver tumors and identify the liver cancer-specific peptides by mass spectrometry.

MATERIAL AND METHODS: In our study, serum samples from 43 patients with malignant liver tumors and 52 patients with benign liver tumors were treated with weak cation-exchange chromatography Magnetic Beads (MB-WCX) kits and analyzed by the Matrix-Assisted Laser Desorption Time of Flight Mass Spectrometry (MALDI-TOF-MS). Then we established genetic algorithm (GA), supervised neural networks (SNN), and quick classifier (QC) models to distinguish malignant from benign liver tumors. To confirm the clinical applicability of the established models, the blinded validation test was performed in 50 clinical serum samples. Discriminatory peaks associated with malignant liver tumors were subsequently identified by a qTOF Synapt G2-S system.

RESULTS: A total of 27 discriminant peaks (p<0.05) in mass spectra of serum samples were found by ClinPro Tools software. Recognition capabilities of the established models were 100% (GA), 89.38% (SNN), and 80.84% (QC); cross-validation rates were 81.67% (GA), 81.11% (SNN), and 86.11% (QC). The accuracy rates of the blinded validation test were 78% (GA), 84% (SNN), and 84% (QC). From the 27 discriminatory peptide peaks analyzed, 3 peaks of m/z 2860.34, 2881.54, and 3155.67 were identified as a fragment of fibrinogen alpha chain, fibrinogen beta chain, and inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), respectively.

CONCLUSIONS: Our results demonstrated that MS technique can be helpful in differentiation of benign and malignant liver tumors. Fibrinogen and ITIH4 might be used as biomarkers for the diagnosis of malignant liver tumors.

Keywords: Biological Markers, Mass Spectrometry



01 January 2023 : Editorial  

Editorial: Current Status of Two Adjuvanted Malaria Vaccines and the World Health Organization (WHO) Strategy to Eradicate Malaria by 2030

Dinah V. Parums
Science Editor, Medical Science Monitor, International Scientific Information, Inc., Melville, NY, USA

DOI: 10.12659/MSM.939357

Med Sci Monit 2023; 29:e939357


26 January 2023 : Editorial  

Editorial: The XBB.1.5 (‘Kraken’) Subvariant of Omicron SARS-CoV-2 and its Rapid Global Spread

Med Sci Monit In Press; DOI: 10.12659/MSM.939580  

19 January 2023 : Clinical Research  

Evaluation of Health-Related Quality of Life and Mental Health in 729 Medical Students in Indonesia During ...

Med Sci Monit In Press; DOI: 10.12659/MSM.938892  

27 December 2022 : Clinical Research  

Effect of Physiotherapy to Correct Rounded Shoulder Posture in 30 Patients During the COVID-19 Pandemic in ...

Med Sci Monit 2022; 28:e938926

In Press

27 Jan 2023 : Database Analysis  

Association Between Neutrophil-Lymphocyte Ratio and All-Cause Mortality in Critically Ill Patients with Chr...

Med Sci Monit In Press; DOI: 10.12659/MSM.938554  

27 Jan 2023 : Clinical Research  

Proposal for a Simple Equation for Limb Muscle Weight Calculation

Med Sci Monit In Press; DOI: 10.12659/MSM.938606  

26 Jan 2023 : Editorial  

Editorial: The XBB.1.5 (‘Kraken’) Subvariant of Omicron SARS-CoV-2 and its Rapid Global Spread

Med Sci Monit In Press; DOI: 10.12659/MSM.939580  

20 Jan 2023 : Clinical Research  

A Study of 42 Partially Edentulous Patients with Single-Crown Restorations and Implants to Compare Bone Los...

Med Sci Monit In Press; DOI: 10.12659/MSM.939225  

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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750