A novel nomogram based on GD for predicting prognosis in hepatocellular carcinoma.

Purpose: The prognosis of liver cancer remains unfavorable nowadays, making the search for predictive biomarkers of liver cancer prognosis of paramount importance to guide clinical diagnosis and treatment. This study was conducted to explore more prognostic markers for most HCC. Patients and methods: A total of 330 patients were enrolled in this study according to the inclusion and exclusion criteria. Follow-up data were collected for all patients until the cutoff date of the study, February 2023. In addition, patient outcomes were assessed with progression-free survival (PFS) and overall survival (OS). All statistical analysis was conducted using R 4.2.0 software. Results: Univariate analysis illustrated that the GD [the product of gamma-glutamyl transpeptidase (GGT) concentration and D-dimer concentration, GD=GGT*D-dimer] levels were related to PFS (p<0.05) and OS (p<0.05). Kaplan-Meier survival curves and log-rank tests indicated a significant difference among different levels of GD (p<0.001). Multivariate analysis demonstrated GD as an independent prognostic factor for HCC. The C-indexes of nomogram were 0.77 and 0.76 in the training or validation cohort, respectively. Area Under the Curve (AUC) of 1-, 2-, 3-, and 4-year OS showed satisfactory accuracy, and the calibration curve illustrated brilliant consistence between the ideal and predicted values. Conclusions: Herein, it was demonstrated that GD was an independent prognostic factor for HCC and revealed the potential to predict the PFS and OS in patients with HCC. Moreover, the nomogram based on GD illustrated a satisfactory prediction ability in comparison to other models without GD.