Applying Serum Cytokine Levels to Predict Pain Severity in Cancer Patients
Received 14 August 2019
Accepted for publication 20 January 2020
Published 7 February 2020 Volume 2020:13 Pages 313—321
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Michael Schatman
Jennifer Fazzari,1 Jesse Sidhu,1 Shreya Motkur,1 Mark Inman,2 Norman Buckley,3 Mark Clemons,4,5 Lisa Vandermeer,5 Gurmit Singh1,3
1Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada; 2Department of Medicine, McMaster University, Hamilton, Ontario, Canada; 3Michael G. DeGroote Institute for Pain Research and Care, McMaster University, Hamilton, Ontario, Canada; 4Department of Medicine, Division of Medical Oncology, The Ottawa Hospital, Ottawa, Canada; 5Cancer Research Program, Ottawa Hospital Research Institute and University of Ottawa, Ottawa, Canada
Correspondence: Gurmit Singh
Department of Pathology & Molecular Medicine, McMaster University, 1280 Main Street West, Hamilton, ON, L8N 3Z5, Canada
Tel +1 905 525 9140 Ext 28144
Background and Aim: Cancers originating in the breast, lung and prostate often metastasize to the bone, frequently resulting in cancer-induced bone pain that can be challenging to manage despite conventional analgesic therapy. This exploratory study’s aim was to identify potential biomarkers associated with cancer-induced pain by examining a sample population of breast cancer patients undergoing bisphosphonate therapy.
Methods: A secondary analysis of the primary study was performed to quantify serum cytokine levels for correlation to pain scores. Cytokines with statistically significant correlations were then input into a stepwise regression analysis to generate a predictive equation for a patient’s pain severity. In an effort to find additional potential biomarkers, correlation analysis was performed between these factors and a more comprehensive panel of cytokines and chemokines from breast, lung, and prostate cancer patients.
Results: Statistical analysis identified nine cytokines (GM-CSF, IFNγ, IL-1β, IL-2, IL-4, IL-5, IL-12p70, IL-17A, and IL-23) that had significant negative correlations with pain scores and they could best predict pain severity through a predictive equation generated for this specific evaluation. After performing a correlation analysis between these factors and a larger panel of cytokines and chemokines, samples from breast, lung and prostate patients showed distinct correlation profiles, highlighting the clinical challenge of applying pain-associated cytokines related to more defined nociceptive states, such as arthritis, to a cancer pain state.
Conclusion: Exploratory analyses such as the ones presented here will be a beneficial tool to expand insights into potential cancer-specific nociceptive mechanisms and to develop novel therapeutics.
Keywords: biomarkers, cancer-induced pain, nociception, immune factors
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