Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors
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 dist...
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| Veröffentlicht in: | Medical science monitor 2017-04, Vol.23, p.1636-1644 |
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| creator | Li, Bo Li, Boan Guo, Tongsheng Sun, Zhiqiang Li, Xiaohan Li, Xiaoxi Wang, Han Chen, Weijiao Chen, Peng Qiao, Mengran Xia, Lifang Mao, Yuanli |
| description | 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 |
| doi_str_mv | 10.12659/msm.901064 |
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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.</description><identifier>ISSN: 1643-3750</identifier><identifier>ISSN: 1234-1010</identifier><identifier>EISSN: 1643-3750</identifier><identifier>DOI: 10.12659/msm.901064</identifier><identifier>PMID: 28376075</identifier><language>eng</language><publisher>United States: International Scientific Literature, Inc</publisher><subject>Adult ; Aged ; Aged, 80 and over ; Biomarkers, Tumor - blood ; Carcinoma, Hepatocellular - blood ; Carcinoma, Hepatocellular - diagnostic imaging ; Carcinoma, Hepatocellular - pathology ; Female ; Humans ; Liver Neoplasms - blood ; Liver Neoplasms - diagnosis ; Liver Neoplasms - diagnostic imaging ; Liver Neoplasms - pathology ; Male ; Middle Aged ; Molecular Biology ; Peptides - blood ; Proteomics - methods ; Reproducibility of Results ; Sequence Analysis, Protein - methods ; Software ; Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods</subject><ispartof>Medical science monitor, 2017-04, Vol.23, p.1636-1644</ispartof><rights>Med Sci Monit, 2017 2017</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c489t-831a45b5a00b2218df47b8bb7d7ac7ab4cc47ad8cbcfef37459b2ec6770b50a43</citedby><cites>FETCH-LOGICAL-c489t-831a45b5a00b2218df47b8bb7d7ac7ab4cc47ad8cbcfef37459b2ec6770b50a43</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388305/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5388305/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28376075$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Bo</creatorcontrib><creatorcontrib>Li, Boan</creatorcontrib><creatorcontrib>Guo, Tongsheng</creatorcontrib><creatorcontrib>Sun, Zhiqiang</creatorcontrib><creatorcontrib>Li, Xiaohan</creatorcontrib><creatorcontrib>Li, Xiaoxi</creatorcontrib><creatorcontrib>Wang, Han</creatorcontrib><creatorcontrib>Chen, Weijiao</creatorcontrib><creatorcontrib>Chen, Peng</creatorcontrib><creatorcontrib>Qiao, Mengran</creatorcontrib><creatorcontrib>Xia, Lifang</creatorcontrib><creatorcontrib>Mao, Yuanli</creatorcontrib><title>Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors</title><title>Medical science monitor</title><addtitle>Med Sci Monit</addtitle><description>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.</description><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biomarkers, Tumor - blood</subject><subject>Carcinoma, Hepatocellular - blood</subject><subject>Carcinoma, Hepatocellular - diagnostic imaging</subject><subject>Carcinoma, Hepatocellular - pathology</subject><subject>Female</subject><subject>Humans</subject><subject>Liver Neoplasms - blood</subject><subject>Liver Neoplasms - diagnosis</subject><subject>Liver Neoplasms - diagnostic imaging</subject><subject>Liver Neoplasms - pathology</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Molecular Biology</subject><subject>Peptides - blood</subject><subject>Proteomics - methods</subject><subject>Reproducibility of Results</subject><subject>Sequence Analysis, Protein - methods</subject><subject>Software</subject><subject>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods</subject><issn>1643-3750</issn><issn>1234-1010</issn><issn>1643-3750</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVUUlLAzEYDaLYupy8S46CtCaTZJJehFpXaPHgcg1JJtNGZpIxmSn47x2tFj19D763wQPgBKMxznI2uahTPZ4gjHK6A4Y4p2REOEO7f_AAHKT0hlAmcsT2wSAThOeIsyHQ06apnFGtCx6-qqqzMJRwoVKCT401bQy1beMHdB62KwuvXVnaaH3rNoqee2W9W3qofNHLqh4q38K5W9sIn7s6xHQE9kpVJXv8cw_By-3N8-x-NH-8e5hN5yNDxaQdCYIVZZophHSWYVGUlGuhNS-4MlxpagzlqhBGm9KWhFM20Zk1OedIM6QoOQSXG9-m07UtTN8yqko20dUqfsignPz_8W4ll2EtGRGCINYbnP0YxPDe2dTK2iVjq0p5G7oksRCU5pjjr6zzDdXEkFK05TYGI_m9ilw8LeRmlZ59-rfZlvs7A_kE4KWKqA</recordid><startdate>20170404</startdate><enddate>20170404</enddate><creator>Li, Bo</creator><creator>Li, Boan</creator><creator>Guo, Tongsheng</creator><creator>Sun, Zhiqiang</creator><creator>Li, Xiaohan</creator><creator>Li, Xiaoxi</creator><creator>Wang, Han</creator><creator>Chen, Weijiao</creator><creator>Chen, Peng</creator><creator>Qiao, Mengran</creator><creator>Xia, Lifang</creator><creator>Mao, Yuanli</creator><general>International Scientific Literature, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20170404</creationdate><title>Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors</title><author>Li, Bo ; Li, Boan ; Guo, Tongsheng ; Sun, Zhiqiang ; Li, Xiaohan ; Li, Xiaoxi ; Wang, Han ; Chen, Weijiao ; Chen, Peng ; Qiao, Mengran ; Xia, Lifang ; Mao, Yuanli</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c489t-831a45b5a00b2218df47b8bb7d7ac7ab4cc47ad8cbcfef37459b2ec6770b50a43</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biomarkers, Tumor - blood</topic><topic>Carcinoma, Hepatocellular - blood</topic><topic>Carcinoma, Hepatocellular - diagnostic imaging</topic><topic>Carcinoma, Hepatocellular - pathology</topic><topic>Female</topic><topic>Humans</topic><topic>Liver Neoplasms - blood</topic><topic>Liver Neoplasms - diagnosis</topic><topic>Liver Neoplasms - diagnostic imaging</topic><topic>Liver Neoplasms - pathology</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Molecular Biology</topic><topic>Peptides - blood</topic><topic>Proteomics - methods</topic><topic>Reproducibility of Results</topic><topic>Sequence Analysis, Protein - methods</topic><topic>Software</topic><topic>Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods</topic><toplevel>online_resources</toplevel><creatorcontrib>Li, Bo</creatorcontrib><creatorcontrib>Li, Boan</creatorcontrib><creatorcontrib>Guo, Tongsheng</creatorcontrib><creatorcontrib>Sun, Zhiqiang</creatorcontrib><creatorcontrib>Li, Xiaohan</creatorcontrib><creatorcontrib>Li, Xiaoxi</creatorcontrib><creatorcontrib>Wang, Han</creatorcontrib><creatorcontrib>Chen, Weijiao</creatorcontrib><creatorcontrib>Chen, Peng</creatorcontrib><creatorcontrib>Qiao, Mengran</creatorcontrib><creatorcontrib>Xia, Lifang</creatorcontrib><creatorcontrib>Mao, Yuanli</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Medical science monitor</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Bo</au><au>Li, Boan</au><au>Guo, Tongsheng</au><au>Sun, Zhiqiang</au><au>Li, Xiaohan</au><au>Li, Xiaoxi</au><au>Wang, Han</au><au>Chen, Weijiao</au><au>Chen, Peng</au><au>Qiao, Mengran</au><au>Xia, Lifang</au><au>Mao, Yuanli</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors</atitle><jtitle>Medical science monitor</jtitle><addtitle>Med Sci Monit</addtitle><date>2017-04-04</date><risdate>2017</risdate><volume>23</volume><spage>1636</spage><epage>1644</epage><pages>1636-1644</pages><issn>1643-3750</issn><issn>1234-1010</issn><eissn>1643-3750</eissn><abstract>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.</abstract><cop>United States</cop><pub>International Scientific Literature, Inc</pub><pmid>28376075</pmid><doi>10.12659/msm.901064</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Aged Aged, 80 and over Biomarkers, Tumor - blood Carcinoma, Hepatocellular - blood Carcinoma, Hepatocellular - diagnostic imaging Carcinoma, Hepatocellular - pathology Female Humans Liver Neoplasms - blood Liver Neoplasms - diagnosis Liver Neoplasms - diagnostic imaging Liver Neoplasms - pathology Male Middle Aged Molecular Biology Peptides - blood Proteomics - methods Reproducibility of Results Sequence Analysis, Protein - methods Software Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods |
| title | Application Value of Mass Spectrometry in the Differentiation of Benign and Malignant Liver Tumors |
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