Based on Soluble Immune Checkpoints Constructing a Random Survival Forest Model to Predict the Prognosis of Hepatitis B Virus-Associated Hepatocellular Carcinoma.

BACKGROUND

Nowadays, immune checkpoint blockade (ICB) therapy has become a milestone in immunotherapy for hepatocellular carcinoma (HCC). However, its clinical effectiveness remains low. Soluble (s) immune checkpoints (ICs), functional components of membrane ICs, are novel physiological immunomodulators. We investigated the prognostic value of sICs in patients of hepatitis B virus-associated hepatocellular carcinoma (HBV-HCC) and provided clinical clues for potential new targets for future immunotherapy.

METHODS

A total of 256 participants were included in this study. We compared the plasma levels of 14 sICs in healthy controls (HC), chronic hepatitis B (CHB), hepatitis B-related liver cirrhosis (HBV-LC), and HBV-HCC groups. COX and random survival forest (RSF) were used to select variables and construct a model to predict overall survival of patients with HBV-HCC. We evaluated the predictive efficacy and analyzed the correlations between sICs, clinical parameters, and membrane ICs.

RESULTS

The levels of 14 sICs in HBV-HCC were elevated compared to that in HC. The areas under the receiver operating characteristic values of 1-, 2-, and 3-year survival predicted by the RSF model were 0.96, 0.85, and 0.81 in the training set, and 0.91, 0.80, and 0.71 in the validation set. The model could adapt to different event distributions and clinical staging systems. Soluble glucocorticoid-induced tumor necrosis factor receptor (sGITR), soluble programmed cell death-ligand 1 (sPD-L1) and soluble T cell immunoglobulin and mucin domain-containing protein 3 (sTIM-3) were closely associated with the prognosis of patients. Soluble PD-L1 was negatively correlated with HGB and positively correlated with AST and NLR ( < 0.05). Soluble TIM-3 was negatively correlated with ALB and CD8+ T cells and positively correlated with HBV-DNA, AST, LDH and mTIM-3 expression in CD8+ T cells (<0.05).

CONCLUSION

We constructed a predictive model based on sICs to predict different survival times in HBV-HCC patients. The risk stratification effectively identified potentially critical patients. Soluble GITR, sPD-L1 and sTIM-3 were important immunological indicators which could dynamically monitor patients' immune status.