Combined prognostic nutritional index and albumin-bilirubin grade to predict the postoperative prognosis of HBV-associated hepatocellular carcinoma patients

Prognostic Nutritional Index as a Prognostic Factor for Very Early-Stage Hepatocellular Carcinoma

INTRODUCTION

Field factors play more important roles in predicting the outcomes of patients compared with tumor factors in early-stage hepatocellular carcinoma (HCC). However, the prognostic ability of noninvasive serum marker scores for hepatic fibrosis and liver functional reserve on very early-stage HCC is still not yet determined. We aimed to investigate the performance of these serum marker scores in predicting the prognoses of patients with very early-stage HCC.

METHODS

A total of 446 patients with very early-stage HCC from 2012 to 2022 were retrospectively enrolled. Serum biomarkers and prognostic scores determining overall survival (OS) were analyzed by Cox proportional hazards model. We compared the Akaike information criterion among the prognostic nutritional index (PNI), aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin (ALBI) score, EZ (easy)-ALBI score, modified ALBI score, fibrosis-4 score, and lymphocyte-to-monocyte ratio to determine the predictability on the OS.

RESULTS

After a median follow-up of 41.0 months (interquartile range 36.9-45.1 months), 81 patients died, with a 5-year OS rate of 71.0%. Among the noninvasive serum marker scores, PNI had the best performance in predicting the OS with the lowest Akaike information criterion (846.407) compared with other scores. Moreover, we stratified the patients into high-risk (PNI <45) and low-risk (PNI ≥45) groups. It showed that the 5-year OS rates were 83.4% and 60.8% in the low-risk and high-risk PNI groups, respectively ( P < 0.001).

DISCUSSION

PNI had the best performance in predicting the OS for patients with very early-stage HCC.

Construction and validation of a machine learning-based nomogram to predict the prognosis of HBV associated hepatocellular carcinoma patients with high levels of hepatitis B surface antigen in primary local treatment: a multicenter study

Background

Hepatitis B surface antigen (HBsAg) clearance is associated with improved long-term outcomes and reduced risk of complications. The aim of our study was to identify the effects of levels of HBsAg in HCC patients undergoing TACE and sequential ablation. In addition, we created a nomogram to predict the prognosis of HCC patients with high levels of HBsAg (≥1000U/L) after local treatment.

Method

This study retrospectively evaluated 1008 HBV-HCC patients who underwent TACE combined with ablation at Beijing Youan Hospital and Beijing Ditan Hospital from January 2014 to December 2021, including 334 patients with low HBsAg levels and 674 patients with high HBsAg levels. The high HBsAg group was divided into the training cohort (N=385), internal validation cohort (N=168), and external validation cohort (N=121). The clinical and pathological features of patients were collected, and independent risk factors were identified using Lasso-Cox regression analysis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts. Patients were classified into high-risk and low-risk groups based on the risk scores of the nomogram.

Result

After PSM, mRFS was 28.4 months (22.1-34.7 months) and 21.9 months (18.5-25.4 months) in the low HBsAg level and high HBsAg level groups (P<0.001). The content of the nomogram includes age, BCLC stage, tumor size, globulin, GGT, and bile acids. The C-index (0.682, 0.666, and 0.740) and 1-, 3-, and 5-year AUCs of the training, internal validation, and external validation cohorts proved good discrimination of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classification of patients with high HBsAg levels into low-risk and high-risk groups according to the risk of recurrence. There was a statistically significant difference in RFS between the two groups in the training, internal validation, and external validation cohorts (P<0.001).

Conclusion

High levels of HBsAg were associated with tumor progression. The nomogram developed and validated in the study had good predictive ability for patients with high HBsAg levels.

Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence

BACKGROUND

Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed.

METHODS

In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered.

RESULTS

Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF).

CONCLUSIONS

The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.

Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks

OBJECTIVE

To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment.

METHODS

Genes related to HBV-HCC were selected through literature screening and protein-protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan-Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors.

RESULTS

The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan-Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level.

CONCLUSION

PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.

Naples Prognostic Score is an Independent Prognostic Factor in Patients Undergoing Hepatectomy for Hepatocellular Carcinoma

Background

Nutritional and inflammatory status has been reported to be associated with the prognosis of hepatocellular carcinoma (HCC), but many studies did not include all biomarkers simultaneously. The present study aimed to determine the impact of Naples prognostic score (NPS) on the long-term survival in patients undergoing hepatectomy for HCC.

Methods

Patients with HCC after curative resection were eligible. Then, all patients were stratified into three groups according to the NPS. Clinical features and survival outcomes were compared among the three groups. Independent prognostic factors were determined by COX analysis. The time dependent receiver operating characteristic (ROC) curves were used to compare prognostic performance with other immunonutrition scoring systems.

Results

A total of 476 patients were enrolled eventually. Baseline characteristics showed that patients with higher NPS had a higher proportion of poor liver function and advanced tumor features. Accordingly, Kaplan-Meier survival curves showed that patients with higher NPS had a lower rate of overall survival (OS) and recurrence-free survival (RFS). Multivariable COX analysis demonstrated that NPS was an independent risk factor of OS (NPS group 2 vs 1: HR=1.958, 95% CI: 1.038-3.369, p = 0.038; NPS group 3 vs 1: HR=2.608, 95% CI: 1.358-5.008, p=0.004, respectively) and RFS (NPS group 2 vs 1: HR=2.014, 95% CI: 1.299-2-3.124, p=0.002; NPS group 3 vs 1: HR=2.002, 95% CI: 1.262-3.175, p=0.003, respectively). The time-dependent ROC curve showed that NPS was superior to other models in prognostic performance and discriminatory power for long-term survival (median AUC 0.675, 95% CI: 0.586-0.712, P < 0.05).

Conclusion

The NPS is a simple tool strongly associated with long-term survival in patients undergoing curative hepatectomy for HCC.

Prognostic Significance of Serum Albumin Level and Albumin-Based Mono- and Combination Biomarkers in Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the predominant form of primary liver cancer. Although many surgical and nonsurgical therapeutic options have been established for treating HCC, the overall prognosis for HCC patients receiving different treatment modalities remains inadequate, which causes HCC to remain among the most life-threatening human cancers worldwide. Therefore, it is vitally important and urgently needed to develop valuable and independent prognostic biomarkers for the early prediction of poor prognosis in HCC patients, allowing more time for more timely and appropriate treatment to improve the survival of patients. As the most abundant protein in plasma, human serum albumin (ALB) is predominantly expressed by the liver and exhibits a wide variety of essential biological functions. It has been well recognized that serum ALB level is a significant independent biomarker for a broad spectrum of human diseases including cancer. Moreover, ALB has been commonly used as a potent biomaterial and therapeutic agent in clinical settings for the treatment of various human diseases. This review provides a comprehensive summary of the evidence from the up-to-date published literature to underscore the prognostic significance of serum ALB level and various ALB-based mono- and combination biomarkers in the prediction of the prognosis of HCC patients after treatment with different surgical, locoregional, and systemic therapies.

Prognostic value of combined inflammatory and nutritional biomarkers in HCC within the Milan criteria after hepatectomy

Aims

This study aimed to evaluate the predictive value of the combined prognostic nutritional index (PNI) and GGT/ALT for the postoperative prognosis of patients with hepatocellular carcinoma (HCC) within Milan criteria undergoing radical hepatectomy.

Methods

This single-center retrospective study included 283 patients with HCC within the Milan criteria who underwent hepatectomy. The receiver operating characteristic (ROC) curve was used to calculate the optimal PNI and GGT/ALT cut-off values. Pre-treatment PNI, GGT/ALT, and PNI-GGT/ALT grades were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated using the Kaplan–Meier method, and multivariate analysis was used to identify prognostic factors.

Results

Multivariate Cox regression analysis identified that the PNI, GGT/ALT, tumor number were significant prognostic markers for OS, and that the GGT/ALT, tumor number were significant prognostic markers for OS. The survival curves showed that low PNI, high GGT/ALT ratio, and high PNI-GGT/ALT grade were associated with poorer OS and DFS. With an area under the curve (AUC) of 0.690, PNI-GGT/ALT outperformed each individual score.

Conclusion

PNI-GGT/ALT, a new prognostic scoring model, qualifies as a novel prognostic predictor for patients with HCC within the Milan criteria after curative resection.