[en] Colorectal cancer globally affects more than one million new persons each year, and kills more than 700.000. Nevertheless, its diagnosis is still largely based on invasive tissue sampling and gaps remain in the understanding of its pathogenesis, with complex combinations between lifestyle, genetics, epigenetics, chronic inflammation (IBD) and microbiota. Untargeted metabolomics is one of the approaches that can be used to solve these issues.
To do so, an optimized and validated (NIST SRM 1950) comprehensive GC×GC-(HR)TOFMS method we developed was used, that also included an in-house QC system and data processing based on multiple statistical techniques.
Practically, we analyzed serum samples from patients affected by colorectal cancer (CRC, n = 18) and by colorectal cancer in remission (R-CRC, n = 17), and samples from healthy patients matched for biases (HC, n = 19 and R-HC, n = 17).
We highlighted candidate biomarkers able to discriminate between matched HC and CRC or R-CRC, which discrimination potential was assessed using supervised and unsupervised models, discriminant analysis and ROC curves. Overfitting was avoided by re-sampling and test validation testing.
Annotation used full mass spectrum, linear retention indices and accuracte mass provided by state-of-the-art high-resolution (HR) time-of-flight mass spectrometry. Finally, we studied the main metabolic pathways altered in the disease, whether in active or in remission state.
In addition, newly developed GC-MS Orbitrap was applied, using the same global method, to biological replicates to determine the capacity of this technology to perform in untargeted metabolomics as well as to validate the results previously obtained.
Research center :
Organic and Biological Analytical Chemistry Group, MS Lab
Disciplines :
Chemistry
Author, co-author :
Di Giovanni, Nicolas ; Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique, organique et biologique
Cojocariu, Cristian; Thermo Fisher Scientific > Runcorn, Cheshire, United Kingdom
Silcock, Paul; Thermo Fisher Scientific > Runcorn, Cheshire, United Kingdom
MEUWIS, Marie-Alice ; Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Recherche translationnelle en gastroentérologie
LOUIS, Edouard ; Centre Hospitalier Universitaire de Liège - CHU > Département de médecine interne > Service de gastroentérologie, hépatologie, onco. digestive
Focant, Jean-François ; Université de Liège - ULiège > Département de chimie (sciences) > Chimie analytique, organique et biologique
Language :
English
Title :
GC×GC-(HR)TOFMS in colorectal cancer metabolomics
Publication date :
18 December 2018
Event name :
Metabomeeting 2018
Event organizer :
Metabolic Profiling Forum
Event place :
Nottingham, United Kingdom
Event date :
18-20/12/2018
By request :
Yes
Audience :
International
References of the abstract :
Colorectal cancer globally affects more than one million new persons each year, and kills more than 700.000. Nevertheless, its diagnosis is still largely based on invasive tissue sampling and gaps remain in the understanding of its pathogenesis, with complex combinations between lifestyle, genetics, epigenetics, chronic inflammation (IBD) and microbiota. Untargeted metabolomics is one of the approaches that can be used to solve these issues.
To do so, an optimized and validated (NIST SRM 1950) comprehensive GC×GC-(HR)TOFMS method we developed was used, that also included an in-house QC system and data processing based on multiple statistical techniques.
Practically, we analyzed serum samples from patients affected by colorectal cancer (CRC, n = 18) and by colorectal cancer in remission (R-CRC, n = 17), and samples from healthy patients matched for biases (HC, n = 19 and R-HC, n = 17).
We highlighted candidate biomarkers able to discriminate between matched HC and CRC or R-CRC, which discrimination potential was assessed using supervised and unsupervised models, discriminant analysis and ROC curves. Overfitting was avoided by re-sampling and test validation testing.
Annotation used full mass spectrum, linear retention indices and accuracte mass provided by state-of-the-art high-resolution (HR) time-of-flight mass spectrometry. Finally, we studied the main metabolic pathways altered in the disease, whether in active or in remission state.
In addition, newly developed GC-MS Orbitrap was applied, using the same global method, to biological replicates to determine the capacity of this technology to perform in untargeted metabolomics as well as to validate the results previously obtained.
