SCN9A Gene Expression and Inflammatory Cytokines

Voltage-gated sodium channels, especially Nav1.7 encoded by the SCN9A gene, are key regulators of nociceptive transmission. Upregulation of SCN9A has been associated with increased neuronal excitability and heightened pain perception. In parallel, inflammatory cytokines such as interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and interleukin-1 beta (IL-1β) are known to sensitize peripheral nociceptors and reduce the efficacy of local anesthetics by modifying tissue environment and ion channel activity. However, the combined influence of SCN9A expression and inflammatory cytokines on anesthetic success in SIP has not been fully elucidated. This prospective case-control study aims to evaluate the association between SCN9A gene expression and inflammatory cytokine levels with the clinical success of IANB in patients with SIP affecting mandibular molars. Approximately 90-100 patients will be recruited and categorized into two groups based on anesthetic outcome: successful anesthesia and failed anesthesia. All patients will receive a standardized IANB using 2% lidocaine with 1:100,000 epinephrine. Anesthetic success will be determined based on the absence of pain during access cavity preparation and instrumentation. Following access and pulp extirpation, pulpal tissue samples will be collected. SCN9A gene expression will be assessed using quantitative real-time polymerase chain reaction (RT-qPCR), with relative expression calculated using the 2\^-ΔΔCt method. Inflammatory cytokine levels (IL-6, TNF-α, IL-1β) will be quantified using enzyme-linked immunosorbent assay (ELISA). The primary outcome will be the difference in SCN9A expression between failed and successful anesthesia groups. Secondary outcomes will include comparison of cytokine levels and evaluation of correlations between SCN9A expression and inflammatory markers. Statistical analysis will include group comparisons, correlation analysis, logistic regression, and receiver operating characteristic (ROC) curve analysis to assess the predictive value of these biomarkers.