Plasma interleukin-8 as a predictive biomarker for tyrosine kinase inhibitor response in advanced hepatocellular carcinoma

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) is the predominant primary liver cancer and third leading cause of cancer-related death. Despite improvements in overall survival (OS), those in HCC-specific survival rates remain modest. Tyrosine kinase inhibitors (TKIs) are crucial first-line HCC treatments when immune checkpoint inhibitors are unsuitable. To identify plasma biomarkers predicting the therapeutic response to TKIs in patients with HCC. METHODS: Pre-treatment plasma samples from 60 patients with advanced HCC treated with sorafenib or lenvatinib were analyzed using targeted proteomics. Differentially expressed proteins were identified based on the treatment response (P < 0.05). RESULTS: Plasma levels of metalloproteinase 12 and vascular endothelial growth factor A were elevated in patients with progressive disease compared with those with partial response or stable disease, correlating with shorter progression-free survival (PFS) and OS. Among patients with PFS ≥ 12 months, C-C motif chemokine ligand 20, C-X-C motif chemokine ligand 1, C-X-C motif chemokine ligand 5, fibroblast growth factor 2, interleukin (IL)-7, IL-8, IL-18, latency-associated peptide transforming growth factor beta 1, and mucin 16 expressions were significantly upregulated, with IL-8 (CXCL8) levels demonstrating the highest predictive accuracy (area under the receiver operating characteristic = 0.91) and prognostic power for PFS (hazard ratio; HR = 2.97, P = 0.0015) and OS (HR = 3.64, P = 0.001). CXCL8 expression was predominantly localized in tumor-associated myeloid cells and enriched in epithelial-mesenchymal transition- and immune modulation-related pathways, highlighting its importance in the tumor microenvironment. CONCLUSIONS: Elevated plasma IL-8 levels are strongly associated with poor outcomes in patients with advanced HCC undergoing TKI treatment, suggesting a potential role for IL-8 in guiding TKI therapeutic decisions and identifying high-risk patients.