The Modulation of Fibrosis in Vocal Fold Repair: A Study on c-Met Agonistic Antibodies and Hepatocyte Growth in Animal Studies.

: Damage to the vocal folds frequently results in fibrosis, which can degrade vocal quality due to the buildup of collagen and modifications in the extracellular matrix (ECM). Conventional treatments have shown limited success in reversing fibrotic changes. Hepatocyte growth factor (HGF) and c-Met-targeting antibodies are promising due to their potential to inhibit fibrosis and promote regeneration. This research examines the effectiveness of injections containing c-Met agonistic antibodies relative to HGF in reducing fibrosis within a rat model of vocal fold injury. : Forty-five Sprague Dawley rats were divided into three groups, which were HGF, c-Met agonistic antibody, and the control (PBS). The right vocal folds were injured and treated with HGF or c-Met agonistic antibody injections. RNA isolation and quantitative real-time PCR were performed to assess mRNA levels of fibrosis-related markers at 1 and 2 weeks post-injury. Histopathological analysis was conducted at 3 weeks to evaluate collagen and hyaluronic acid (HA) deposition. : Both the HGF and c-Met groups demonstrated reduced type III collagen mRNA expression compared to the PBS group. The c-Met group uniquely maintained fibronectin levels closer to normal. Additionally, the c-Met group showed significantly upregulated expression of hyaluronan synthase (HAS) 1 and HAS 3 at 2 weeks post-injury, indicating enhanced HA synthesis. Histological analysis showed significantly lower collagen deposition and higher HA in the c-Met group than in PBS, confirming superior anti-fibrotic effects and ECM restoration. : c-Met agonistic antibody injections outperformed HGF in reducing fibrosis, upregulating HAS expression, and promoting HA deposition in injured vocal folds, highlighting its potential as a superior therapeutic approach for preventing fibrosis and enhancing ECM quality in vocal fold injuries. Further research on functional outcomes in larger models is recommended to validate these findings.