Development and Validation of Nomograms Based on Gamma-Glutamyl Transpeptidase to Platelet Ratio for Hepatocellular Carcinoma Patients Reveal Novel Prognostic Value and the Ratio Is Negatively Correlated With P38MAPK Expression

Value of GPR, APPRI and FIB-4 in the early diagnosis of hepatocellular carcinoma: a prospective cohort study

OBJECTIVE

There is an urgent need for novel biomarkers that are inexpensive, effective and easily accessible to complement the early diagnosis of hepatocellular carcinoma. This study aimed to analyze the relationship between serum gamma-glutamate-transpeptidase to platelet ratio, alkaline phosphatase-to-platelet ratio index, fibrosis index based on four factors and the risk of hepatocellular carcinoma, and to determine the optimal cut-offs for predicting hepatocellular carcinoma.

METHODS

Based on a prospective cohort study, 44 215 participants who were cancer-free at baseline (2011-13) were included in the study. Cox proportional hazard models and receiver operating characteristics curves were used to analyze the diagnostic value and optimal cut-off value of gamma-glutamyl-transpeptidase to platelet ratio, alkaline phosphatase-to-platelet ratio index and fibrosis index based on four factors in predicting hepatocellular carcinoma patients.

RESULTS

Gamma-glutamyl-transpeptidase to platelet ratio, alkaline phosphatase-to-platelet ratio index and fibrosis index based on four factors can be used as early independent predictors of hepatocellular carcinoma risk. The risk of hepatocellular carcinoma in the fourth quantile of gamma-glutamyl-transpeptidase to platelet ratio and alkaline phosphatase-to-platelet ratio index was 4.04 times (hazard ratio = 4.04, 95% confidence interval: 2.09, 7.80) and 2.59 times (hazard ratio = 2.59, 95% confidence interval: 1.45, 4.61), respectively, compared with the first quantile. With fibrosis index based on four factors first quantile as a reference, fibrosis index based on four factors fourth quantile had the highest risk (hazard ratio = 18.58, 95% confidence interval: 7.55, 45.72). Receiver operating characteristic results showed that fibrosis index based on four factors had a stronger ability to predict the risk of hepatocellular carcinoma (area under curve = 0.81, 95% confidence interval: 0.80, 0.81), and similar results were shown for gender stratification. In the total population, the optimal cut-off values of gamma-glutamyl-transpeptidase to platelet ratio, alkaline phosphatase-to-platelet ratio index and fibrosis index based on four factors were 0.208, 0.629 and 1.942, respectively.

CONCLUSIONS

Gamma-glutamyl-transpeptidase to platelet ratio, alkaline phosphatase-to-platelet ratio index and fibrosis index based on four factors were independent predictors of hepatocellular carcinoma risk. Amongst them, fibrosis index based on four factors shows a stronger predictive ability for hepatocellular carcinoma risk, and gamma-glutamyl-transpeptidase to platelet ratio and alkaline phosphatase-to-platelet ratio index can be used as complementary indicators.

Novel Nomograms Based on Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio Predict Prognosis of Hepatocellular Carcinoma Patients After Hepatectomy

Background

The prediction of prognosis of hepatocellular carcinoma (HCC) is of great significance in improving disease outcome and optimizing clinical management, while reliable prognostic indicators are lacking. This study was conducted to develop readily-to-use nomograms for prognosis prediction of HCC after hepatectomy.

Materials and Methods

Data of eligible patients were collected and analyzed retrospectively. Independent prognostic factors were identified by Cox regression, and nomograms for the prediction of disease-free survival (DFS) and overall survival (OS) were developed. The performance of the nomograms was evaluated by receiver operating characteristics (ROC) curves, C-indexes and calibration curves and was verified by the validation cohort. The predictive value of the nomograms was also compared with the 8th edition of American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) and the Barcelona Clinic Liver Cancer (BCLC) staging systems.

Results

In total, 599 patients were enrolled in the analysis: 420 in the training cohort and 179 in the validation cohort. The optimal cut-off value of Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio (GLR) was 19.5. GLR contributed significantly to the nomograms with good predictive power. In ROC analyses, the areas under curve (AUCs) of the nomograms for 1-, 3- and 5-year DFS and OS prediction were 0.758, 0.756, 0.734 and 0.810, 0.799, 0.758, respectively. The C-indexes of the DFS nomogram were 0.697 (95% CI 0.665-0.729) in the training cohort and 0.710 (95% CI 0.664-0.756) in the validation cohort. For OS prediction, the C-indexes were 0.741 (95% CI 0.704-0.778) and 0.758 (95% CI 0.705-0.811) in the training and validation cohorts, respectively. The calibration curves demonstrated satisfactory agreement between nomogram predictions and actual observations. The nomograms demonstrated superior predictive performance to the TNM and the BCLC staging systems.

Conclusion

Our novel nomograms showed adequate performance in the prediction of HCC prognosis after hepatectomy, which may facilitate the risk stratification and individualized management of HCC patients.

Construction and validation of a predictive model for hepatocellular carcinoma based on serum markers

Background

Early hepatocellular carcinoma (HCC) detection with non-invasive biomarkers remains an unmet clinical need. We aimed to construct a predictive model based on the pre-diagnostic levels of serum markers to predict the early-stage onset of HCC.

Methods

A total of 339 HCC patients (including 157 patients from Changzhou cohort and 182 patients from Wuxi cohort) were enrolled in our retrospective study. Levels of 25 baseline serum markers were collected. Propensity score matching (PSM) analysis was conducted to balance the distributions of patients’ gender, age, and the surveillance time between HCC group and control group. Then, Receiver operating characteristic (ROC) and Logistic regression analysis were performed to screen the independent predictive variables and construct a non-invasive predictive model. Subsequently, ROC curve and Kaplan–Meier (K–M) curve were used to evaluate the predictive values of the model. Clinical net benefit of the model was demonstrated by decision curve analysis (DCA) and clinical impact curve.

Results

Five independent predictive variables for HCC onset and two general characteristics of patients (age and gender) were incorporated into the score model. ROC and DCA curves showed that the score model had better predictive performance in discrimination and clinical net benefit compared with single variable or other score systems, with the area under the curve (AUC) of 0.890 (95% CI 0.856–0.925) in Changzhou cohort and 0.799 (95% CI 0.751–0.849) in Wuxi cohort. Meanwhile, stratification analysis indicated that the score model had good predictive values for patients with early tumor stage (AJCC stage I) or small tumors (< 2 cm). Moreover, the score of HCC patient began to increase at 30 months before clinical diagnosis and reach a peak at 6 months.

Conclusion

Based on this model, we could optimize the current risk stratification at an early stage and consider further intensive surveillance programs for high-risk patients. It could also help clinicians to evaluate the progression and predict the prognosis of HCC patients.

Screening of Hepatocellular Carcinoma Patients with High Risk of Early Recurrence After Radical Hepatectomy Using a Nomogram Model Based on the γ-Glutamyl Transpeptidase-to-Albumin Ratio

BACKGROUND AND PURPOSE

The present study aimed to establish a γ-glutamyl transpeptidase-to-albumin ratio (GAR)-based nomogram model to predict early recurrence of hepatocellular carcinoma (HCC) after radical surgery.

METHODS

Patients enrolled in this study were randomly allocated into a train and validation cohort in a ratio of 7:3. The Least Absolute Shrinkage and Selection Operator (LASSO) proportional hazards model and cox regression model were combined to identify independent risk factors related to HCC recurrence. Based on these risk factors, a predictive nomogram was constructed and validated in both inner and outer test cohorts. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), the calibration curve and decision curve analysis.

RESULTS

The tumor size, tumor number, BCLC stage, microvascular invasion (MVI) and GAR value were identified as independent risk factors related to HCC recurrence and used to construct the predictive nomogram. AUC of the nomogram showed satisfactory accuracy in predicting 1-, 3- and 5-year disease-free survival. The calibration curve showed agreement between the ideal and predicted values. The risk score more than 72 as calculated by the nomogram was related to early recurrence of HCC after radical surgery. DCA plots showed better clinical usability of the nomogram as compared with the BCLC staging system in all three included cohorts.

CONCLUSION

The nomogram based on the GAR value may provide a new option for screening of the target HCC cohort of patients who need anti-recurrence therapy after surgery.

TROP2 as Patient-Tailoring but Not Prognostic Biomarker for Breast Cancer

Purpose

Methods

Results

Conclusion

A Potential Prognostic Marker for Recognizing VEGF-Positive Hepatocellular Carcinoma Based on Magnetic Resonance Radiomics Signature

Objectives

The objective of our project is to explore a noninvasive radiomics model based on magnetic resonance imaging (MRI) that could recognize the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma before operation.

Methods

202 patients with proven single HCC were enlisted and stochastically distributed into a training set (n = 142) and a test set (n = 60). Arterial phase, portal venous phase, balanced phase, delayed phase, and hepatobiliary phase images were used to radiomics features extraction. We retrieved 1906 radiomic features from each phase of every participant’s MRI images. The F-test was applied to choose the crucial features. A logistic regression model was adopted to generate a radiomics signature. By combining independent risk indicators from the fusion radiomics signature and clinico-radiological features, we developed a multivariable logistic regression model that could predict the VEGF status preoperatively through calculating the area under the curve (AUC).

Results

The entire group comprised 108 VEGF-positive individuals and 94 VEGF-negative patients. AUCs of 0.892 (95% confidence interval [CI]: 0.839 - 0.945) in the training dataset and 0.800 (95% CI: 0.682 - 0.918) in the test dataset were achieved by utilizing radiomics features from two phase images (8 features from the portal venous phase and 5 features from the hepatobiliary phase). Furthermore, the nomogram relying on a combined model that included the clinical factors α-fetoprotein (AFP), irregular tumor margin, and the fusion radiomics signature performed well in both the training (AUC = 0.936, 95% CI: 0.898-0.974) and test (AUC = 0.836, 95% CI: 0.728-0.944) datasets.

Conclusions

The combined model acquired from two phase (portal venous and hepatobiliary phase) pictures of gadolinium-ethoxybenzyl-diethylenetriamine-pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI could be considered as a credible prognostic marker for the level of VEGF in HCC.

Gamma-glutamyl transpeptidase-platelet ratio, systemic immune inflammation index, and system inflammation response index in invasive Aspergillosis

OBJECTIVE

Gamma-glutamyl transpeptidase-platelet ratio, system inflammation response index, and systemic immune inflammation index are three systemic immune and inflammation indexes that were investigated for their diagnostic and prognostic proficiencies in cardiovascular diseases and cancers. However, their predictive values for invasive aspergillosis have not yet been studied. The aim of this study was to evaluate Gamma-glutamyl transpeptidase-platelet ratio, system inflammation response index, and systemic immune inflammation index levels and their diagnostic values in invasive aspergillosis.

METHODS

A total of 23 patients with invasive aspergillosis and 23 sex- and age-matched healthy participants were included in this study. Complete blood count parameters and liver function tests were studied. Gamma-glutamyl transpeptidase-platelet ratio, system inflammation response index, and systemic immune inflammation index were calculated.

RESULTS

Leukocyte, neutrophil, lymphocyte, and monocyte levels were statistically significantly higher in IA group (p=0.031, p=0.027, p=0.033, and p=0.001, respectively). In invasive aspergillosis group, platelets were numerically lower; Aspartate transaminase, alanine aminotransferase, and lactic dehydrogenase levels were numerically higher than those in control group but differences between levels were not statistically significant (p>0.05). The γ-glutamyl transpeptidase levels of patients were statistically significantly higher (p=0.007), and in addition, statistically significant differences were found between groups in terms of gamma-glutamyl transpeptidase-platelet ratio, system inflammation response index, and systemic immune inflammation index (p<0.001, p=0.037, p=0.001, respectively). Receiver operating characteristic analysis was performed, and areas under the curves were evaluated. gamma-glutamyl transpeptidase-platelet ratio had the higher area under the curve than systemic immune inflammation index and system inflammation response index (AUC 0.849, 0.798, 0.693, respectively). The results from receiver operating characteristic analysis of the data suggested that the use of a cutoff value of 0.15 for gamma-glutamyl transpeptidase-platelet ratio would be optimum for clinical use to confirm independent predictors of patients with invasive aspergillosis.

CONCLUSIONS

Gamma-glutamyl transpeptidase-platelet ratio is an independent, a useful predictor, and is superior to other evaluated markers in the diagnosis of inflammation in invasive aspergillosis. Gamma-glutamyl transpeptidase-platelet ratio may also be a helpful biomarker for clinicians to follow-up the inflammatory process of these patients.

Gamma-Glutamyl Transpeptidase to Platelet Ratio: A New Inflammatory Marker Associated with Outcomes after Cardiac Arrest

Introduction

In recent years, gamma-glutamyl transpeptidase to platelet ratio (GPR) has been proposed as a new inflammatory marker. We aimed to evaluate the association between GPR and outcomes after cardiac arrest (CA).

Methods

A total of 354 consecutive patients with CA were included in this retrospective study. Patients were divided into three groups according to tertiles of GPR (low, n = 119; middle, n = 117; and high, n = 118). To determine the relationship between GPR and prognosis, a logistic regression analysis was performed. The ability of GPR to predict the outcomes was evaluated by receiver operating characteristic (ROC) curve analysis. Two prediction models were established, and the likelihood ratio test (LRT) and the Akaike Information Criterion (AIC) were utilized for model comparison.

Results

Among the 354 patients (age 62 [52, 74], 254/354 male) who were finally included in the analysis, those in the high GPR group had poor outcomes. Multivariate logistic regression analysis revealed that GPR was independently associated with the three outcomes, for ICU mortality (odds ratios (OR) = 1.738, 95% confidence interval (CI): 1.221-2.474, P = 0.002), hospital mortality (OR = 1.676[1.164 − 2.413], P = 0.005), and unfavorable neurologic outcomes (OR = 1.623[1.121 − 2.351], P = 0.010). The area under the ROC curve was 0.611 (95% Cl: 0.558-0.662) for ICU mortality, 0.600 (95% CI: 0.547-0.651) for hospital mortality, and 0.602 (95% CI: 0.549-0.653) for unfavorable neurologic outcomes. Further, the LRT analysis showed that compared with the model without GPR, the GPR-combined model had a higher likelihood ratio χ² score and smaller AIC.

Conclusion

GPR, as an inflammatory indicator, was independently associated with outcomes after CA. GPR is helpful in estimating the clinical outcomes of patients with CA.

A Novel Nomogram Based on Hepatic and Coagulation Function for Evaluating Outcomes of Intrahepatic Cholangiocarcinoma After Curative Hepatectomy: A Multi-Center Study of 653 Patients

Background and Aims

Hepatic and coagulation function are routine laboratory tests prior to curative hepatectomy. The prognostic value of gamma-glutamyl transpeptidase (GGT) to platelet ratio (GPR) and international normalized ratio (INR) in surgically treated patients with intrahepatic cholangiocarcinoma (ICC) remains unclear.

Methods

ICC patients received curative hepatectomy in two west China centers were included. Time-dependent ROC curves were conducted to compare established indexes with prognostic value for ICC. GPR-INR score was introduced and evaluated using the Time-dependent AUC curve and Kaplan-Meier survival analysis. A novel nomogram based on the GPR-INR score was proposed; Harrell’s C-index, calibration curve and decision curve analysis were used to assess this nomogram.

Results

A total of 653 patients were included. The areas under ROC curves of GPR and INR in OS and RFS were superior to other indexes. Patients with a high GPR-INR score (1,2) presented significantly decreased overall survival (OS) and recurrence-free survival (RFS); GPR-INR sore, along with several clinicopathological indexes were selected into the nomogram, the calibration curve for OS probability showed good coincidence between the nomogram and the actual surveillance. The C-index of the nomogram was 0.708 (derivation set) and 0.746 (validation set), which was more representative than the C-indexes of the GPR-INR score (0.597, 0.678). In decision curve analysis, the net benefits of the nomogram in derivation and validation set were higher than Barcelona Clinic Liver Cancer staging (BCLC) classification and American Joint Committee on Cancer (AJCC) TNM 8^th staging system.

Conclusions

The proposed nomogram generated superior discriminative ability to established staging systems; it is profitable to applicate this nomogram in clinical practice.