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Study of molecular and metabolic changes in skeletal muscle in response to cancer Open Access


Other title
skeletal muscle
Type of item
Degree grantor
University of Alberta
Author or creator
Stretch, Cynthia
Supervisor and department
Baracos, Vickie (Oncology)
Examining committee member and department
Baracos, Vickie (Oncology)
Ball, Ron (Agricultural, Food & Nutritional Science)
McCargar, Linda (Agricultural, Food & Nutritional Science)
Damaraju, Sambasivarao (Oncology)
Sawyer, Michael (Oncology)
Jagoe, Thomas (Oncology)
Department of Oncology

Date accepted
Graduation date
Doctor of Philosophy
Degree level
Cancer cachexia is a multifactorial syndrome characterized by involuntary weight loss, wasting of skeletal muscle driven by reduced food intake and abnormal metabolism. Cachexia has a negative impact on quality of life, response to chemotherapy and survival. Cachexia research is undeveloped with respect to understanding molecular changes involved and its classification / diagnostic criteria (there are no clinically useful predictors and diagnostic tests). The purpose of this research was to take advantage of gene expression (transcriptomic) and metabolite (metabolomic) profiling to address these gaps. Patients with cancer consented to provide skeletal muscle biopsy (n=134) for gene expression array or plasma and urine (n=93) for nuclear magnetic resonance spectroscopy and mass spectrometry. Omic data output was examined in relation to different dimensions of cachexia phenotype; gene expression was examined in relation to weight loss, muscle mass and muscle radiation attenuation and metabolites were examined in relation to muscle loss, muscle and fat mass, metabolic rate and food intake. Statistical analysis included standard statistical tests and machine learning methods. Muscle gene expression varied strongly in relation to muscle attenuation, and to a much lesser degree with weight loss and muscle mass. Differential expression suggests low attenuation muscle has persistent inflammation, increased degradation, altered energy metabolism, increased extracellular matrix components and altered growth signalling. Urinary metabolites reflected muscle mass and to a lesser extent fat mass, and could be used to predict muscle mass and rate of muscle loss with 98% and 82% accuracy, respectively. Urinary metabolites related to muscle mass and muscle loss were associated with amino acid and ATP synthesis. Overall, transcriptomics work revealed a molecular signature for low muscle attenuation, which parallels many gene expression changes observed during aberrant muscle repair and metabolic syndrome. This explorative transcriptomic study provides multiple potentially crucial pathways that have yet to be studied in detail in cachexia. Metabolomics work revealed that urine metabolites are most reflective of muscle mass and its change. This work suggests that it may be possible to develop a metabolomics-based tool to assess skeletal muscle mass in cancer. Validation of urine metabolomics to predict muscle mass loss is warranted.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of these terms. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
Cynthia Stretch; Sheehan Khan; Nasimeh Asgarian; Roman Eisner; Saman Vaisipour; Sambasivarao Damaraju; Kathryn Graham; Oliver F. Bathe; Helen Steed; Russell Greiner; Vickie E. Baracos. Effects of sample size on differential gene expression, rank order and prediction accuracy of a gene signature. PLoS One. 2013 Jun 3;8(6):e65380Stretch C, Eastman T, Mandal R, Eisner R, Wishart DS, Mourtzakis M, Prado CM, Damaraju S, Ball RO, Greiner R, Baracos VE. Prediction of skeletal muscle and fat mass in patients with advanced cancer using a metabolomic approach. J Nutr. 2012 Jan;142(1):14-21.Roman Eisner†, Cynthia Stretch†, Thomas Eastman, Jianguo Xia, David Hau, Sambasivarao Damaraju, Russell Greiner, David S. Wishart, Vickie Baracos. Learning to predict cancer-associated skeletal muscle wasting from ¹H-NMR profiles of urinary metabolites. Metabolomics. 2011;7(1):25-34 † Contributed equally to this work

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