Abstract

BackgroundEarly prediction of recurrence and death risks is significant to the treatment of hepatocellular carcinoma (HCC) patients. We aimed to develop and validate prognosis nomogram models based on the gamma-glutamyl transpeptidase (GGT)-to-platelet (PLT) ratio (GPR) for HCC and to explore the relationship between the GPR and inflammation-related signaling pathways.MethodsAll data were obtained from 2000 to 2012 in the Affiliated Hospital of Qingdao University. In the training cohort, factors included in the nomograms were determined by univariate and multivariate analyses. In the training and validation cohorts, the concordance index (C-index) and calibration curves were used to assess predictive accuracy, and receiver operating characteristic curves were used to assess discriminative ability. Clinical utility was evaluated using decision curve analysis. Moreover, improvement of the predictive accuracy of the nomograms was evaluated by calculating the decision curve analysis, the integrated discrimination improvement, and the net reclassification improvement. Finally, the relationship between the GPR and inflammation-related signaling pathways was evaluated using the independent-samples t-test.ResultsA larger tumor size and higher GPR were common independent risk factors for both disease-free survival (DFS) and overall survival (OS) in HCC (P < 0.05). Good agreement between our nomogram models’ predictions and actual observations was detected by the C-index and calibration curves. Our nomogram models showed significantly better performance in predicting the HCC prognosis compared to other models (P < 0.05). Online webserver and scoring system tables were built based on the proposed nomogram for convenient clinical use. Notably, including the GPR greatly improved the predictive ability of our nomogram models (P < 0.05). In the validation cohort, p38 mitogen-activated protein kinase (P38MAPK) expression was significantly negatively correlated with the GPR (P < 0.01) and GGT (P = 0.039), but was not correlated with PLT levels (P = 0.063). And we found that P38MAPK can regulate the expression of GGT by quantitative real-time PCR and Western blotting experiments.ConclusionsThe dynamic nomogram based on the GPR provides accurate and effective prognostic predictions for HCC, and P38MAPK-GGT may be a suitable therapeutic target to improve the prognosis of HCC patients.

Highlights

  • Hepatocellular carcinoma (HCC) is one of the most common fatal cancers in the world

  • We found that only male sex (hazard ratio (HR): 1.52, 95% CI: 1.09–2.13, P = 0.015), a larger tumor size (HR: 1.12, 95% CI: 1.03–1.21, P = 0.008), a larger tumor margin (HR: 0.98, 95% CI: 0.97–1.00, P = 0.038), and a higher GPR (HR: 1.53, 95% CI: 1.40–1.68, P < 0.001) were independent risk factors for disease-free survival (DFS) (Figure 2A-right)

  • A nomogram model based on GPR was developed, which provides accurate and effective prognostic prediction for HCC

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Summary

Introduction

Hepatocellular carcinoma (HCC) is one of the most common fatal cancers in the world. In China, HCC has the 4th highest cancer incidence rate and the 3rd highest mortality rate [1]. In the past several years, more and more studies have focused on the gamma-glutamyl transpeptidase (GGT)-to-platelet (PLT) ratio (GPR), an inflammatory indicator, for early prediction of the prognosis of liver diseases. We aimed to develop and validate excellent and effective nomogram models based on the GPR for early, personalized prediction of the prognosis of HCC patients and to identify potential therapeutic targets to improve patient survival. Prediction of recurrence and death risks is significant to the treatment of hepatocellular carcinoma (HCC) patients. We aimed to develop and validate prognosis nomogram models based on the gamma-glutamyl transpeptidase (GGT)-to-platelet (PLT) ratio (GPR) for HCC and to explore the relationship between the GPR and inflammationrelated signaling pathways

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