Pharmacogenomics of Sorafenib in Hepatocellular Carcinoma (HCC): A LncRNA-Expression Guided Approach Using UCA1 and...

Background/Objectives: Hepatocellular carcinoma (HCC) presents limited therapeutic options for advanced disease, and sorafenib therapy is hampered by significant interpatient heterogeneity in response. This necessitates biomarker-guided strategies to personalize treatment. This study investigated the long noncoding RNAs UCA1 and MALAT1 as pharmacogenomic biomarkers for personalizing sorafenib therapy in advanced HCC. Methods: In a prospective cohort of 154 HCC patients receiving first-line sorafenib (400 mg twice daily), serum lncRNA levels were quantified by RT-qPCR at baseline, Week 4, and Week 12. Expression levels were correlated with treatment response (mRECIST), time-to-progression (TTP), and overall survival (OS). Statistical analyses included Kaplan-Meier estimates, Cox proportional hazards models, and ROC curve analysis. Results: High baseline expression of UCA1 (77.9% of patients) and MALAT1 (73.4%) was associated with aggressive disease. High UCA1 correlated with reduced 12-month survival (60.8% vs. 73.5%, p = 0.026) and shorter median Time-to-Progression (TTP) (18.0 vs. 21.9 weeks, p = 0.002). High MALAT1 was associated with significantly shorter median TTP (18.0 vs. 25.2 weeks, p = 0.003). In multivariable analysis, both lncRNAs were independent prognostic factors for shorter TTP (UCA1: HR = 1.52, p = 0.014; MALAT1: HR = 1.61, p = 0.006). Serial monitoring revealed that a ≥10% rise in either lncRNA by Week 4 predicted a five-fold higher progression risk by Week 12 (52% vs. 10%, p < 0.001), providing a median lead time of 7.0 weeks before radiological confirmation of progression. Conclusions: These findings demonstrate that UCA1 and MALAT1 enable early identification of sorafenib resistance. Baseline stratification and serial monitoring can provide early detection of treatment resistance, informing clinical decision-making and supporting their potential utility for personalizing therapy in advanced HCC.