RESUMO
Successful eradication of the hepatitis C virus (HCV) cannot eliminate the risk of hepatocellular carcinoma (HCC). Next-generation RNA sequencing provides comprehensive genomic insights into the pathogenesis of HCC. Artificial intelligence has opened a new era in precision medicine. This study integrated clinical features and genetic biomarkers to establish a machine learning-based HCC model following viral eradication. A prospective cohort of 55 HCV patients with advanced fibrosis, who achieved a sustained virologic response after antiviral therapy, was enrolled. The primary outcome was the occurrence of HCC. The genomic signatures of peripheral blood mononuclear cells (PBMC) were determined by RNA sequencing at baseline and 24 weeks after end-of-treatment. Machine learning algorithms were implemented to extract the predictors of HCC. HCC occurred in 8 of the 55 patients, with an annual incidence of 2.7%. Pretreatment PBMC DEFA1B, HBG2, ADCY4, and posttreatment TAS1R3, ABCA3, and FOSL1 genes were significantly downregulated, while the pretreatment ANGPTL6 gene was significantly upregulated in the HCC group compared to that in the non-HCC group. A gene score derived from the result of the decision tree algorithm can identify HCC with an accuracy of 95.7%. Gene score = TAS1R3 (≥0.63 FPKM, yes/no = 0/1) + FOSL1 (≥0.27 FPKM, yes/no = 0/1) + ABCA3 (≥2.40 FPKM, yes/no = 0/1). Multivariate Cox regression analysis showed that this gene score was the most important predictor of HCC (hazard ratio = 2.38, 95% confidence interval [CI] = 1.06-5.36, P = 0.036). Combining the gene score and fibrosis-4 index, a nomogram was constructed to predict the probability of HCC with an area under the receiver operating characteristic curve up to 0.950 (95% CI = 0.888-1.000, P = 7.0 × 10-5). Decision curve analysis revealed that the nomogram had a net benefit in HCC detection. The calibration curve showed that the nomogram had optimal concordance between the predicted and actual HCC probabilities. In conclusion, down-regulated posttreatment PBMC TAS1R3, ABCA3, and FOSL1 expression were significantly correlated with HCC development after HCV eradication. Decision-tree-based algorithms can refine the assessment of HCC risk for personalized HCC surveillance.
RESUMO
Lenvatinib has been effective not only as a first-line but also as a later-line systemic therapy for unresectable hepatocellular carcinoma (uHCC) in real-world clinical practice. How to predict the efficacy of lenvatinib and guide appropriate therapy selection in patients with uHCC have become important issues. This study aimed to investigate the impact of serum biomarkers on the treatment outcomes of patients with uHCC treated with lenvatinib in a real-world setting using an artificial intelligence algorithm. We measured serum biomarkers, including alpha-fetoprotein (AFP), albumin-bilirubin (ALBI) grade, and circulating angiogenic factors (CAFs [i.e., vascular endothelial growth factor, angiopoietin-2, fibroblast growth factor-19 [FGF19], and FGF21]) and analyzed treatment outcomes, including objective response rate (ORR), progression-free survival (PFS), and overall survival (OS) in patients with uHCC treated with lenvatinib. The results of this study demonstrated that an AFP reduction ≥ 40% from baseline within 8 weeks after lenvatinib induction was associated with a higher ORR. With baseline biomarkers using a decision tree-based model, we identified patients with high, intermediate, and low ORRs (84.6%, 21.7% and 0%, respectively; odds ratio, 53.04, P < 0.001, high versus intermediate/low groups). Based on the decision tree-based survival predictive model, baseline AFP was the most important factor for OS, followed by ALBI grade and FGF21.