Abstract | BACKGROUND AND AIM: 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.
|
Authors | Jennifer Fazzari, Jesse Sidhu, Shreya Motkur, Mark Inman, Norman Buckley, Mark Clemons, Lisa Vandermeer, Gurmit Singh |
Journal | Journal of pain research
(J Pain Res)
Vol. 13
Pg. 313-321
( 2020)
ISSN: 1178-7090 [Print] New Zealand |
PMID | 32104053
(Publication Type: Journal Article)
|
Copyright | © 2020 Fazzari et al. |