Fan Zhang1, Chunbo Li2, Kui Deng3, Zhuozhong Wang3, Weiwei Zhao3, Kai Yang3, Chunyan Yang3, Zhiwei Rong3, Lei Cao3, Yaxin Lu3, Yue Huang3, Peng Han4, Kang Li5. 1. Laboratory of Hematology Center, The First Affiliated Hospital of Harbin Medical University, Harbin, 150086, China. 2. Department of Colorectal Surgery, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, 150086, China. 3. Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150086, China. 4. Department of Colorectal Surgery, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, 150086, China. leospiv@163.com. 5. Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150086, China. likang@ems.hrbmu.edu.cn.
Abstract
INTRODUCTION: Colorectal cancer (CRC) remains an incurable disease. Previous metabolomic studies show that metabolic signatures in plasma distinguish CRC patients from healthy controls. Chronic enteritis (CE) represents a risk factor for CRC, with a 20 fold greater incidence than in healthy individuals. However, no studies have performed metabolomic profiling to investigate CRC biomarkers in CE. OBJECTIVE: Our aims were to identify metabolomic signatures in CRC and CE and to search for blood-derived metabolite biomarkers distinguishing CRC from CE, especially early-stage biomarkers. METHODS: In this case-control study, 612 subjects were prospectively recruited between May 2015 and May 2016, and including 539 CRC patients (stage I, 102 cases; stage II, 259 cases; stage III, 178 cases) and 73 CE patients. Untargeted metabolomics was performed to identify CRC-related metabolic signatures in CE. RESULTS: Five pathways were significantly enriched based on 153 differential metabolites between CRC and CE. 16 biomarkers were identified for diagnosis of CRC from CE and for guiding CRC staging. The AUC value for CRC diagnosis in the external validation set was 0.85. Good diagnostic performances were also achieved for early-stage CRC (stage I and stage II), with an AUC value of 0.84. The biomarker panel could also stage CRC patients, with an AUC of 0.72 distinguishing stage I from stage II CRC and AUC of 0.74 distinguishing stage II from stage III CRC. CONCLUSIONS: The identified metabolic biomarkers exhibit promising properties for CRC monitoring in CE patients and are superior to commonly used clinical biomarkers (CEA and CA19-9).
INTRODUCTION:Colorectal cancer (CRC) remains an incurable disease. Previous metabolomic studies show that metabolic signatures in plasma distinguish CRCpatients from healthy controls. Chronic enteritis (CE) represents a risk factor for CRC, with a 20 fold greater incidence than in healthy individuals. However, no studies have performed metabolomic profiling to investigate CRC biomarkers in CE. OBJECTIVE: Our aims were to identify metabolomic signatures in CRC and CE and to search for blood-derived metabolite biomarkers distinguishing CRC from CE, especially early-stage biomarkers. METHODS: In this case-control study, 612 subjects were prospectively recruited between May 2015 and May 2016, and including 539 CRCpatients (stage I, 102 cases; stage II, 259 cases; stage III, 178 cases) and 73 CEpatients. Untargeted metabolomics was performed to identify CRC-related metabolic signatures in CE. RESULTS: Five pathways were significantly enriched based on 153 differential metabolites between CRC and CE. 16 biomarkers were identified for diagnosis of CRC from CE and for guiding CRC staging. The AUC value for CRC diagnosis in the external validation set was 0.85. Good diagnostic performances were also achieved for early-stage CRC (stage I and stage II), with an AUC value of 0.84. The biomarker panel could also stage CRCpatients, with an AUC of 0.72 distinguishing stage I from stage II CRC and AUC of 0.74 distinguishing stage II from stage III CRC. CONCLUSIONS: The identified metabolic biomarkers exhibit promising properties for CRC monitoring in CEpatients and are superior to commonly used clinical biomarkers (CEA and CA19-9).
Authors: David B Liesenfeld; Nina Habermann; Reka Toth; Robert W Owen; Eva Frei; Jürgen Staffa; Petra Schrotz-King; Karel D Klika; Cornelia M Ulrich Journal: Metabolomics Date: 2014-12-20 Impact factor: 4.290
Authors: Paul Dowling; David J Hughes; Anne Marie Larkin; Justine Meiller; Michael Henry; Paula Meleady; Vincent Lynch; Barbara Pardini; Alessio Naccarati; Miroslav Levy; Pavel Vodicka; Paul Neary; Martin Clynes Journal: Clin Chim Acta Date: 2014-12-23 Impact factor: 3.786
Authors: Beatriz Jiménez; Reza Mirnezami; James Kinross; Olivier Cloarec; Hector C Keun; Elaine Holmes; Robert D Goldin; Paul Ziprin; Ara Darzi; Jeremy K Nicholson Journal: J Proteome Res Date: 2013-01-16 Impact factor: 4.466
Authors: Lee Cheng Phua; Xiu Ping Chue; Poh Koon Koh; Peh Yean Cheah; Han Kiat Ho; Eric Chun Yong Chan Journal: Cancer Biol Ther Date: 2014-01-14 Impact factor: 4.742