Development of a Nomogram Using the Inflammatory Risk Score for Prognostication in Moderate and Advanced Hepatocellular Carcinoma Associated with Hepatitis B Virus.

Background: The changes in risk scores of inflammatory markers among patients diagnosed with hepatocellular carcinoma (HCC) remain unknown. Aims: To investigate the relationship between the inflammation risk score and other contributing factors and the prognostic outcomes in patients with moderate and advanced hepatitis B virus (HBV)-related HCC. Study Design: A retrospective cohort study. Methods: A total of 174 patients with moderate and advanced HBV related HCC were recruited to investigate the impact of stratified inflammatory risk scores and other associated risk factors on disease prognosis. Based on the optimal cut-off values calculated by the Youden index, the patients were divided into high-risk and low-risk groups based on their inflammation risk scores. Results: The study found a significant difference in median survival time between the low-risk and high-risk groups based on the inflammation risk score. Furthermore, the inflammation risk score, alpha-fetoprotein levels, transarterial chemoembolization treatment, and Barcelona Clinic Liver Cancer stage were identified as independent prognostic factors. The four variables were used to construct a prognostic nomogram for HCC. Subsequent evaluations using time-dependent receiver operating characteristic analysis and calibration curve tests revealed the nomogram's commendable discriminatory ability. As a result, the nomogram proved to be an effective tool for predicting survival at 2- to 4-years. Conclusion: The inflammation risk score has been identified as a significant prognostic factor for HBV-related HCC. The development of nomogram models has provided a practical and effective tool for determining the prognosis of patients affected by HBV-related HCC.

Causal relationships between immune cells, inflammatory factors, serum metabolites, and hepatic cancer: A two-sample Mendelian randomization study.

Background: Observational studies and clinical trials suggest associations between immune cells, inflammatory factors, serum metabolites, and hepatic cancer. However, the causal relationships between these factors and hepatic cancer remain to be established. Objective: To explore the causal relationships between immune cells, inflammatory factors, serum metabolites, and hepatic cancer. Methods: This study employed comprehensive two-sample Mendelian randomization (MR) utilizing publicly available genetic data (GWAS) to analyze causal relationships between 731 immune cell traits, 91 inflammatory factors, 1400 serum metabolites, and hepatic cancer. The primary analysis used inverse variance-weighted (IVW) MR, with additional sensitivity tests to assess the validity of causal relationships. Results: After correction for heterogeneity and horizontal pleiotropy, in exploring the causal relationships between immune cell groups and hepatic cancer, we found that Terminally Differentiated CD4-CD8- T cell %T cell was negatively associated with hepatic cancer, serving as a protective factor, while Effector Memory CD4-CD8- T cell %CD4-CD8- T cell was positively associated with hepatic cancer, acting as a risk factor. In investigating the causal relationships between inflammatory factors and hepatic cancer, C-C motif chemokine 19 levels were positively associated with hepatic cancer, representing a risk factor, while Interleukin-10 levels were negatively associated with hepatic cancer, acting as a protective factor. Regarding the causal relationships between serum metabolites and hepatic cancer, (N(1) + N(8))-acetylspermidine levels were negatively associated with hepatic cancer, serving as a protective factor, while 1-(1-enyl-palmitoyl)-GPC (p-16:0) levels were positively associated with hepatic cancer, acting as a risk factor. Conclusion: Our MR analysis indicates causal relationships between immune cells, inflammatory factors, serum metabolites, and hepatic cancer. However, further validation is needed to assess the potential of these immune cells, inflammatory factors, and serum metabolites as preventive or therapeutic targets for hepatic cancer.

Construction and validation of a nomogram for hepatocellular carcinoma patients based on HCC-GRIm score.

PURPOSE: To construct a nomogram for hepatocellular carcinoma (HCC) patients base on HCC-GRIm score. METHODS: Clinical cases of HCC patients diagnosed at Hunan Integrated Traditional Chinese and Western Medicine Hospital were included, and these were randomly divided into the training cohort (n = 219) and the validation cohort (n = 94), and those patients were divided into low GRIm-Score group (scores 0, 1, and 2) and high GRIm-Score group (scores 3, 4, and 5). In the training cohort, independent risk factors were determined by Cox regression analysis, and a nomogram was constructed by independent risk factors. The efficiency and the clinical applicability of nomograms were evaluated using ROC curves, calibration plot, and the decision curve (DCA), and the patients were divided into high-risk, middle-risk, and low-risk groups according to total score of nomogram. RESULTS: Compared to low HCC-GRIm score group, high HCC-GRIm score group with BCLC stage is more advanced (P < 0.001), and fewer patients received TACE (P = 0.005) and surgical treatment (P = 0.001). There was higher rate of the presence of vascular invasion (P < 0.001) and distant metastasis (P < 0.001). Multivariate Cox regression analysis screened 4 independent risk factors to construct a nomogram of HCC patients, including HCC-GRIm score, BCLC stage, albumin-to-globulin ratio (AGR), and glutamyl trans-peptidase (GGT). The consistency index (C-index) of the nomogram of the training was 0.843 (0.832-0.854) and the validation was 0.870 (0.856-0.885). The time-dependent parameter showed the AUC values of the training cohort at 1, 3, and 5 years were 0.954 (95% CI 0.929-0.980), 0.952 (95% CI 0.919-0.985), and 925 (95% CI 0.871-0.979), while the AUC values of validation cohort at 1, 3, and 5 years were 0.974 (95% CI 0.950-0.998), 0.965 (95% CI 0.931-0.999), and 0.959 (95% CI 0.898-1.021). The calibration plot showed the nomogram fits well onto perfect curves, and the DCA curve showed the net benefit of the nomogram at a certain probability threshold is significantly higher than the net benefit of the BCLC stage at the same threshold probability. Finally, all patients were divided into high-risk, middle-risk, and low-risk groups based on the total score of nomogram, and it showed effectively to identify high-risk patients. CONCLUSION: The nomogram constructed by the independent risk factors can predict the prognosis of HCC patients, providing an effective tool with clinical workers to evaluate the prognosis and survival time of HCC patients.