Advances in post-operative prognostic models for hepatocellular carcinoma

Deep learning-based radiomics allows for a more accurate assessment of sarcopenia as a prognostic factor in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies and is a major cause of cancer-related mortalities worldwide (Forner et al., 2018; He et al., 2023). Sarcopenia is a syndrome characterized by an accelerated loss of skeletal muscle (SM) mass that may be age-related or the result of malnutrition in cancer patients (Cruz-Jentoft and Sayer, 2019). Preoperative sarcopenia in HCC patients treated with hepatectomy or liver transplantation is an independent risk factor for poor survival (Voron et al., 2015; van Vugt et al., 2016). Previous studies have used various criteria to define sarcopenia, including muscle area and density. However, the lack of standardized diagnostic methods for sarcopenia limits their clinical use. In 2018, the European Working Group on Sarcopenia in Older People (EWGSOP) renewed a consensus on the definition of sarcopenia: low muscle strength, loss of muscle quantity, and poor physical performance (Cruz-Jentoft et al., 2019). Radiological imaging-based measurement of muscle quantity or mass is most commonly used to evaluate the degree of sarcopenia. The gold standard is to measure the SM and/or psoas muscle (PM) area using abdominal computed tomography (CT) at the third lumbar vertebra (L3), as it is linearly correlated to whole-body SM mass (van Vugt et al., 2016). According to a "North American Expert Opinion Statement on Sarcopenia," SM index (SMI) is the preferred measure of sarcopenia (Carey et al., 2019). The variability between morphometric muscle indexes revealed that they have different clinical relevance and are generally not applicable to broader populations (Esser et al., 2019).

Targeting LRRC41 as a potential therapeutic approach for hepatocellular carcinoma

Introduction: Hepatocellular carcinoma (HCC) is the most common primary liver cancer, characterized by high mortality rate. In clinical practice, several makers of liver cancer, such as VEGFR1, FGFR1 and PDGFRα, were identified and their potentials as a therapeutic target were explored. However, the unsatisfied treatment results emphasized the needs of new therapeutic targets. Methods: 112 HCC patients samples were obtained to evaluate the expression of LRRC41, SOX9, CD44, and EPCAM in HCC, combined with prognosis analysis. A DEN-induced HCC rat model was constructed to verify the expression of LRRC41 and SOX9 in HCC and lung metastasis tissues. Immune score evaluation was analysized by bioinformatics methods. Network pharmacology was performed to explored the potential FDA-approved drugs targeting LRRC41. Results: Through analysis of the Timer database and tissue micro-array, we confirmed that LRRC41 was over-expressed in HCC and exhibited a significant positive correlation with recurrence and metastasis. Immunohistochemistry staining of human HCC tissue samples revealed significant upregulation of LRRC41, SOX9, CD44, and EPCAM, with LRRC41 showing a positive correlation with SOX9, CD44, and EPCAM expression. UALCAN database analysis indicated that LRRC41 and SOX9 contribute to poor prognosis whereas CD44 and EPCAM did not demonstrate the same significance. Furthermore, analysis of a DEN-induced HCC rat model confirmed the significantly elevated expression of LRRC41 and SOX9 in HCC and lung metastasis tissues. Drug sensitivity analysis and molecular docking targeting LRRC41 identified several FDA-approved drugs, which may have potential antitumor effects on HCC by targeting LRRC41. Conclusion: Our findings highlight the role of LRRC41 overexpression in promoting HCC progression and its association with a poor prognosis. Drug sensitivity analysis and molecular docking shows several FDA-approved drugs may be potential therapeutic targets for HCC. Targeting LRRC41 may hold promise as a potential therapeutic strategy for HCC.

Development and Validation of a Prognostic Model for Transarterial Chemoembolization in Unresectable Hepatocellular Carcinoma Based on Preoperative Serum Prealbumin

Purpose

We aimed to develop a prognostic nomogram utilizing preoperative serum prealbumin levels to predict the overall survival (OS) in patients undergoing transarterial chemoembolization (TACE) for unresectable hepatocellular carcinoma (HCC).

Patients and Methods

A total of 768 individuals with unresectable HCC who underwent TACE at three medical facilities in Suzhou between January 2007 December 2018 were included. The patient cohort was assigned to a training set (n = 461) and a validation set (n = 307). Cox regression analysis identified independent prognostic factors, which were then used to construct a prognostic nomogram. Internal validation was performed in the testing group, and its effectiveness and capability were evaluated with reference to the concordance index (C-index), area under the curve (AUC), calibration curve, and decision curve analysis (DCA).

Results

Independent risk factors identified through Cox regression analyses included the BCLC stage, cirrhosis, invasion, tumor number, preoperative serum PALB, performance status (PS), and tumor size. The nomogram demonstrated a C-index of 0.734 (95% confidence interval (CI): 0.710-0.758) in the training set and 0.717 (95% CI: 0.678-0.756) in the validation set, indicating strong discriminatory ability. The nomogram also demonstrated favorable discriminatory performance with AUC values of 0.873, 0.820, and 0.833 for 1-, 2-, and 3-year OS, respectively, in the training set, and 0.854, 0.765, and 0.724 in the validation set. The AUC value of the nomogram (0.843) was significantly higher than that of the four conventional staging systems. Moreover, calibration graphs confirmed a strong concordance between the predicted and observed results. Furthermore, DCA underscored the significant clinical utility of the nomogram. Additionally, the low-risk group exhibited considerably superior rates of survival compared to the high-risk group.

Conclusion

The developed nomogram demonstrated excellent prognostic capability, which served as a valuable tool for personalized clinical decision-making for patients with HCC.

Beta2-Microglobulin as Predictive Biomarkers in the Prognosis of Hepatocellular Carcinoma and Development of a New Nomogram

Background

Accurate prognosis is crucial for improving hepatocellular carcinoma (HCC) patients, clinical management, and outcomes post-liver resection. However, the lack of reliable prognostic indicators poses a significant challenge. This study aimed to develop a user-friendly nomogram to predict HCC patients' post-resection prognosis.

Methods

We retrospectively analyzed the data from 1091 HCC patients, randomly split into training (n=767) and validation (n=324) cohorts. Receiver operating characteristic (ROC) curves determined the optimal cut-off value for alpha1-microglobulin (α1MG) and Beta2-microglobulin (β2MG). Kaplan-Meier analysis assessed microglobulin's impact on survival, followed by Cox regression to identify prognostic factors and construct a nomogram. The predictive accuracy and discriminative ability of the nomogram were measured by the concordance index (C-index), calibration curves, area under the ROC curve (AUC), and decision curve analysis (DCA), and were compared with the BCLC staging system, Edmondson grade, or BCLC stage plus Edmondson grade.

Results

Patients with high β2MG (≥2.395mg/L) had worse overall survival (OS). The nomogram integrated β2MG, BCLC stage, Edmondson grade, microvascular invasion (MVI), and serum carbohydrate antigen 199 (CA199) levels. C-index for training and validation cohorts (0.712 and 0.709) outperformed the BCLC stage (0.660 and 0.657), Edmondson grade (0.579 and 0.564), and the combination of BCLC stage with Edmondson grade (0.681 and 0.668), improving prognosis prediction. Calibration curves demonstrated good agreement between predicted and observed survival. AUC values exceeded 0.700 over time, highlighting the nomogram's discriminative ability. DCA revealed superior overall net income compared to other systems, emphasizing its clinical utility.

Conclusion

Our β2MG-based nomogram accurately predicts HCC patients' post-resection prognosis, aiding intervention and follow-up planning. Significantly, our nomogram surpasses existing prognostic indicators, including BCLC stage, Edmondson grade, and the combination of BCLC stage with Edmondson grade, by demonstrating superior predictive performance.