Abstract
Pain is the most common complication of sickle cell disease (SCD). The underlying biology of SCD pain is not well understood, which is a barrier to novel, effective analgesic and preventive therapies. A wide variability in the phenotypic expression of pain exists among individuals with SCD, despite the inheritance of a similar defective hemoglobin gene. This interindividual pain variability further complicates the ability to understand the biology and effectively treat pain. We sought to discover a biological signature comprising differentially expressed genes unique to SCD that could differentiate between individuals with varied pain frequency. We conducted plasma-induced transcription analysis from 149 individuals with SCD and 60 Black individuals without SCD from multiple sites. We discovered 3028 differentially expressed genes that underwent weighted gene coexpression network analysis to distinguish gene modules significantly associated with pain frequency. We identified 524 genes, significantly associated with pain frequency (≥|0.3| and P < .05), that were further analyzed using the "database for annotation, visualization, and integrated discovery" (DAVID) tool to delineate the biological pathways associated with these genes. The highest ranked Gene Ontology process from DAVID was inflammatory response (P = 1.67E-12) and many related pathways were enriched (eg, response to lipopolysaccharide, and chemokine and cytokine signaling). The top 10 hub genes identified within our biological signature were TNF, CCL2, ITGAM, ITGAX, ICAM1, CCR5, CXCL2, IFNG, CCR1, and CXCL3. Future work should focus on further validating this signature and investigating the potential targets uncovered for their mechanistic and potentially therapeutic role in SCD pain.