Abstract
We endeavored to identify objective blood biomarkers for pain, a subjective sensation with a biological basis, using a stepwise discovery, prioritization, validation, and testing in independent cohorts design. We studied psychiatric patients, a high risk group for co-morbid pain disorders and increased perception of pain. For discovery, we used a powerful within-subject longitudinal design. We were successful in identifying blood gene expression biomarkers that were predictive of pain state, and of future emergency department (ED) visits for pain, more so when personalized by gender and diagnosis. MFAP3, which had no prior evidence in the literature for involvement in pain, had the most robust empirical evidence from our discovery and validation steps, and was a strong predictor for pain in the independent cohorts, particularly in females and males with PTSD. Other biomarkers with best overall convergent functional evidence for involvement in pain were GNG7, CNTN1, LY9, CCDC144B, and GBP1. Some of the individual biomarkers identified are targets of existing drugs. Moreover, the biomarker gene expression signatures were used for bioinformatic drug repurposing analyses, yielding leads for possible new drug candidates such as SC-560 (an NSAID), and amoxapine (an antidepressant), as well as natural compounds such as pyridoxine (vitamin B6), cyanocobalamin (vitamin B12), and apigenin (a plant flavonoid). Our work may help mitigate the diagnostic and treatment dilemmas that have contributed to the current opioid epidemic.
Highlights
We bioinformatically identified which of our individual biomarkers are modulated by existing drugs and can be used for pharmacogenomic population stratification and measuring of response to treatment, as well as used the gene expression signatures of the top predictive biomarkers to interrogate the Connectivity Map database from Broad/MIT to identify drugs and natural compounds that could be repurposed for treating pain
A longitudinal within-subject design is orders of magnitude more powerful than a cross-sectional case-control design. Some of these candidate gene expression biomarkers are increased in expression in high pain states, and others are decreased in expression
We prioritized this list of candidate biomarkers with a Bayesian-like Convergent Functional Genomics approach [13, 14], comprehensively integrating previous published human and animal model evidence in the field for involvement in pain, and directly citing it
Summary
Psychiatric patients may have an increased perception of pain, as well as increased physical health reasons for pain, due to their often adverse life trajectory [3] As such, they may be a suitable population in which to try to identify peripheral blood biomarkers for pain, that may be complementary to genetic findings in the field [4]. We validated our top biomarkers from discovery and prioritization in an independent cohort of psychiatric subjects with a clinical diagnosis of a pain disorder and with high scores on pain severity and functional impact ratings. We tested if the candidate biomarkers from the first three steps are able to predict high pain state, and future emergency department (ED) visits for pain, in another independent cohort of psychiatric subjects. We bioinformatically identified which of our individual biomarkers are modulated by existing drugs and can be used for pharmacogenomic population stratification and measuring of response to treatment, as well as used the gene expression signatures of the top predictive biomarkers to interrogate the Connectivity Map database from Broad/MIT to identify drugs and natural compounds that could be repurposed for treating pain
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