Journal of Hepatocellular Carcinoma最新文献

Background & aims: Hepatocellular carcinoma (HCC) may develop in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) even in the absence of cirrhosis. Whether the risk of HCC in non-cirrhotic MASLD is substantial to justify surveillance, and which patients may benefit, remains unclear.

Methods: Post-hoc analysis conducted on a prospective MASLD cohort. All participants underwent baseline liver stiffness measurement (LSM) using SuperSonic Imagine (SSI) two-dimensional shear wave elastography (2D-SWE) and were surveilled every 6-12 months. Exclusion criteria were less than 6 months follow-up, unavailable LSM-SSI, prior HCC. Primary outcome was HCC, with hepatic decompensation and portal vein thrombosis (PVT) as competing risks. To improve risk stratification, LSM-SSI optimized cut-offs were applied: <7.4 kPa to rule-out advanced fibrosis, ≥15.6 kPa to rule-in cirrhosis, based on recent meta-analytic data, and were integrated in different risk stratification algorithms.

Results: Among 352 patients with a median follow-up of 31 (14.1-57.8) months, 257 (73%) had LSM-SSI <7.4 kPa, 67 (19%) between 7.4-15.6 kPa, and 28 (8%) ≥15.6 kPa. During follow-up, 9 (2.6%) developed HCC, 6 (1.7%) decompensation, 2 (0.6%) PVT. No events occurred in patients with LSM-SSI <7.4 kPa. In the 7.4-15.6 kPa group, HCC and decompensation occurred in 3 (4.5%) and 1 (1.5%), respectively. For non-cirrhotic patients (LSM-SSI <15.6 kPa), LSM-SSI was significantly associated with HCC risk (HR 1.542, p<0.0001). Following multivariate analysis, independent HCC predictors were: LSM-SSI (HR 1.052, 95% CI 1.030-1.075, p<0.001), type 2 diabetes mellitus (HR 4.555, 95% Ci 1.091-19.012, p=0.038), and gamma-glutamyl transferase (HR 1.004, 95% CI 1.001-1.006, p=0.003). A two-step non-invasive algorithm combining LSM-SSI and the PLEASE score yielded 100% negative predictive value and 89.5% accuracy in identifying patients for HCC surveillance.

Conclusion: HCC is the leading liver-related complication in non-cirrhotic MASLD. LSM-SSI <7.4 kPa effectively excludes high-risk patients. A two-step algorithm further enhances risk stratification and surveillance precision.

Purpose: By far, non-invasive methods assessing Ki-67 are still scarce. This study was performed to evaluate the capability of radiomics based on contrast-enhanced CT in predicting expression of Ki-67 in hepatocellular carcinoma (HCC).

Patients and methods: HCC patients who underwent curative hepatectomy were included. The optimal Ki-67 cutoff value for prognostic stratification was determined using maximum selection rank statistics. The Least Absolute Selection and Shrinkage Operator (LASSO) regression analysis was used for dimension reduction and data screening to obtain radiomics features. The radiomics model, clinical model and combined model integrating radiomics features and clinical indicators for predicting Ki-67 were constructed. The predictive efficacy among the models was compared using C-index, and further verified through DeLong test, decision curves and clinical impact curves.

Results: The optimal cutoff value for Ki-67 was 0.25. A total of 2553 radiomics features were obtained and after stability testing (by intraclass correlation coefficient ≥0.75) and feature selection (LASSO), four radiomics features that were highly correlated and stable with the expression of Ki-67 were included in the model construction. Multivariate analyses revealed that alpha-fetoprotein and intratumoral necrosis or radiomics features were independent predictors of Ki-67. The clinical model, radiomics model and combined model were constructed, respectively. The C-indices of Ki-67 for clinical model, radiomics model and the combined model were 0.75, 0.82 and 0.88. DeLong test, decision curves and clinical impact curves further confirmed that the inclusion of radiomics features improved the predictive efficacy of the model.

Conclusion: The comprehensive model based on contrast-enhanced CT radiomics could non-invasively and effectively predict the expression of Ki-67, suggesting its value in clinical decision-making.

Purpose: Progression to liver cirrhosis (LC) and hepatocellular carcinoma (HCC) is a severe epidemiological risk factor for chronic hepatitis B (CHB); therefore, effective monitoring strategies are urgently required. Dysregulation of bile acids (BAs) metabolism is crucial for aggravating the pathological processes of liver diseases. In this study, we aimed to develop a risk model based on the BAs signature to predict the likelihood of LC and HCC occurrence in CHB patients.

Patients and methods: A retrospective analysis was conducted using the clinical data of 609 patients diagnosed with CHB, HBV-related LC, and HCC. Patients were randomly assigned to a training or validation set. Logistic regression analyses were employed in the training set to identify key variables and establish a nomogram risk model for predicting the progression of CHB to LC and HCC. Accuracy, calibration, and clinical utility of the model were assessed using a validation set.

Results: Taurocholic acid level and age were independent risk factors, and serum albumin level was a protective factor against the progression of CHB to LC and HCC. A Nomogram risk model was developed using these three indicators, demonstrating a highly accurate and reliable ability to predict the progression from CHB to LC and HCC with good clinical validity and utility.

Conclusion: This study developed a nomogram incorporating BA markers, enabling precise prediction of LC and HCC in patients with CHB. This provided an accurate and accessible method for early screening and prevention.

Purpose: Transarterial chemoembolization (TACE) is the primary treatment for unresectable hepatocellular carcinoma (HCC). Given the poor prognosis of liver cancer patients with diabetes, identifying indicators of response to TACE and methods to reverse non-response is crucial.

Methods: Patients were classified as TACE-responsive or non-responsive in the GSE104580 dataset. We conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to identify the differentially expressed genes (DEGs). Univariate and multivariate Cox regression analyses were performed on genes in the insulin resistance signaling pathway to screen for those associated with poor prognosis in TACE. We developed a polygenic signature in GSE14520 using LASSO Cox regression, conducted molecular docking of four genes with drugs, and validated the results using drug sensitivity tests. The Connectivity Map (CMap) database was used to identify potential drugs for reversing TACE.

Results: We constructed a prognostic signature consisting of four genes (DUSP9, ENO2, NTS, and SERPINE1) and validated it using drug-sensitivity tests. Classifying TACE-treated patients into high- and low-risk groups using risk scores revealed that the high-risk group had significantly lower overall survival than the low-risk group. In patients undergoing TACE, the risk score independently predicted overall survival. Using the CMap database, we speculated that PD-98059 is a potential drug for reversing TACE unresponsiveness. We detected the docking sites of PD-98059 in four genes. Cell experiments confirmed that PD-98059 synergistically enhanced the inhibitory effect of lobaplatin on HCC cell proliferation.

Conclusion: The insulin resistance model tailored for TACE effectively predicts patient prognosis. Via the effect of MEK inhibitor PD-98059, the efficacy of TACE in patients can be improved.

Tripartite motif-containing protein 21 (TRIM21), an E3 ubiquitin ligase of the TRIM superfamily, modulates critical cellular processes including ubiquitination, autophagy, and oxidative stress response. Accumulating evidence highlights its context-dependent regulatory roles in hepatocellular carcinoma (HCC)-the most prevalent primary liver malignancy with high mortality and limited therapeutic efficacy. This review systematically summarizes the core mechanisms by which TRIM21 orchestrates HCC progression: ① Autophagy regulation: TRIM21 modulates HCC autophagy via multiple axes, including CCR4-NOT complex (TNKS1BP1/CNOT4)-mediated substrate ubiquitination, ATG14-dependent autophagosome initiation, and RETREG1-driven reticulophagy, with context-dependent effects on tumor proliferation. ② Drug resistance: TRIM21 enhances oxaliplatin sensitivity by ubiquitinating and degrading G6PD (the rate-limiting enzyme of the pentose phosphate pathway), while its role in sorafenib resistance involves dual pathways-the MST1/YAP axis and the ApoE/cholesterol/PI3K-AKT cascade. ③ Metastasis suppression: TRIM21 restricts HCC invasion and metastasis by ubiquitinating key oncoproteins, preserving epithelial integrity and inhibiting mesenchymal transition. ④ Reactive oxygen species (ROS) balance: TRIM21 regulates oxidative stress in HCC via the SQSTM1/p62-Keap1-NRF2 axis, coordinating with HIF1α to modulate antioxidant responses and tumor cell survival. Additionally, we discuss the regulatory significance of TRIM21 in HCC associated with hepatitis B virus (HBV) infection (via HBx/DNA polymerase ubiquitination) and nonalcoholic steatohepatitis (NASH) (via suppressing lipogenic enzymes to reduce steatosis-driven carcinogenesis). This review provides a theoretical basis for TRIM21 as a potential diagnostic marker and therapeutic target for HCC.

Background: Serum lipid levels have been associated with the prognosis of various malignancies.

Aim: To develop a novel nomogram based on serum lipid parameters to predict overall survival in patients with intrahepatic cholangiocarcinoma.

Methods: Serum lipid profiles and survival data were collected prior to the initiation of chemotherapy combined with immunotherapy. Survival analysis was performed to identify prognostic factors associated with ICC. Independent prognostic factors were used to construct a nomogram. The predictive performance of the nomogram was evaluated. External validation of the survival analysis and nomogram for serum lipids was conducted using a validation cohort.

Results: Low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and apolipoprotein A1 were selected for further analysis. Survival analysis demonstrated that patients with low LDL-C, high HDL-C, and high ApoA1 levels exhibited significantly longer OS and PFS. A nomogram incorporating LDL-C and HDL-C was constructed to predict 1-, 2-, and 3-year survival probabilities. The nomogram exhibited favorable predictive performance.

Discussion: Pre-treatment serum levels of LDL-C, HDL-C, and ApoA1 exhibited significant prognostic value for advanced ICC. The nomogram constructed based on LDL-C and HDL-C effectively predicted survival outcomes, providing a theoretical basis to support treatment decision-making and individualized prognostic assessment in clinical practice.

Background: Hepatocellular carcinoma (HCC) remains poor, and inflammatory markers have emerged as potential predictors. This study aimed to develop and validate a nomogram for predicting overall survival (OS) in patients with HCC after radical hepatectomy by integrating inflammatory markers with clinicopathological factors.

Methods: We retrospectively analyzed patients with HCC who underwent radical hepatectomy at the Guangdong Provincial People's Hospital between 2014 and 2018. The patients were randomly assigned (2:1 ratio) to the training and validation cohorts. Independent prognostic factors were identified using univariate and multivariate Cox regression analyses to construct a nomogram. The performance of the model was assessed using ROC, calibration, and decision curve analysis (DCA) and compared with established staging systems (AJCC 8th edition TNM, BCLC, and CNLC).

Results: The training and validation cohorts included 242 and 121 patients, respectively. Aspartate aminotransferase-to-platelet ratio index (APRI), systemic inflammation response index (SIRI), and microvascular invasion (MVI) were identified as independent prognostic factors (P < 0.05). In the training cohort, the nomogram achieved AUCs of 0.837, 0.778, and 0.793 for the 1-, 3-, and 5-year OS, respectively. The corresponding AUCs in the validation cohort were 0.712, 0.746, and 0.746, respectively. The calibration curves and DCA confirmed the robust predictive ability of the model. The nomogram AUCs were significantly higher than those of all staging systems (P < 0.05).

Conclusion: The proposed nomogram, incorporating APRI, SIRI, and MVI, effectively predicts OS in patients with HCC following radical resection and outperforms conventional staging systems.

Objective: This study aimed to evaluate the diagnostic performance of individual serum biomarkers [alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II (PIVKA-II)] and composite models (GALAD, ASAP) for hepatocellular carcinoma (HCC) across two immunoassay platforms.

Methods: From 2011 to 2021, 518 serum samples were selected from a liver-related disease biobank at Peking Union Medical College Hospital (Beijing, China), including 102 HCC patients, 117 with benign liver disease, 38 with cholangiocarcinoma, 96 with colorectal cancer, 65 with metastatic hepatic carcinoma, and 100 healthy controls. AFP and PIVKA-II levels were measured on both the Hotgen and Abbott ARCHITECT platforms. The GALAD and ASAP scores were calculated based on the data from each platform. Receiver operating characteristic (ROC) curve analysis and the corresponding areas under the curves (AUCs) were used to evaluate and compare the diagnostic value of the individual biomarkers and the two composite models.

Results: For HCC diagnosis, AFP exhibited comparable efficacy between Hotgen (AUC: 0.821) and Abbott (AUC: 0.846), whereas PIVKA-II performed better on Abbott (AUC: 0.863) than Hotgen (AUC: 0.787). GALAD and ASAP models exhibited significantly better diagnostic performance than individual serum biomarkers on both platforms (P < 0.05): on Hotgen, both models achieved an AUC of 0.872, while on Abbott, ASAP (AUC: 0.913) was marginally superior to GALAD (AUC: 0.901, P = 0.0569). Notably, both models performed better on Abbott than Hotgen (GALAD: 0.901 vs 0.872, P = 0.0001; ASAP: 0.913 vs 0.872, P = 0.0003). Spearman correlation analysis showed moderate inter-platform correlations for AFP (r = 0.573) and PIVKA-II (r = 0.460). Bland-Altman analysis indicated poor inter-platform consistency, with mean biases of 44.32% (AFP) and -92.02% (PIVKA-II).

Conclusion: GALAD and ASAP models demonstrate superior diagnostic efficacy for HCC compared to individual biomarkers, and their performance is significantly influenced by the immunoassay platform employed.

Objective: To analyse the influencing factors for textbook outcome (TO) achievement in laparoscopic hepatectomy for hepatocellular carcinoma (HCC) and to construct and validate a nomogram model.

Methods: A total of 200 patients with HCC who underwent laparoscopic hepatectomy in our hospital between January 2022 and December 2024 were retrospectively analysed and divided into a TO group (n = 116) and a non-TO group (n = 84) according to the TO in liver surgery criteria. Twenty clinical parameters were collected, and after univariate analysis to screen variables, multivariate logistic regression was performed to determine independent influencing factors and establish nomograms. Model discrimination, calibration and clinical benefit were assessed using receiver operating characteristic (ROC) curves, calibration curves and decision curves.

Results: Multivariate logistic analysis showed that no malnutrition before operation (odds ratio [OR] = 0.051; 95% confidence interval [CI]: 0.014-0.179), intraoperative blood loss <225 mL (OR = 0.096; 95% CI: 0.030-0.310) and postoperative hospital stay <12.5 days (OR = 0.061; 95% CI: 0.021-0.182) were independent protective factors for TO (all P < 0.05). The nomogram C-index was 0.931; the area under the ROC curve was 0.983 (95% CI: 0.971-0.995), sensitivity was 0.948 and specificity was 0.929; the calibration curve fitted well with the ideal curve; and the decision curve showed that the model had a significant positive net benefit. Subgroup analysis based on resection extent (minor vs major hepatectomy) confirmed the model's robust performance, with AUCs of 0.984 and 0.976 respectively, demonstrating consistent predictive accuracy across different surgical complexities.

Conclusion: Preoperative nutritional status, intraoperative blood loss and postoperative hospital stay are independent factors for achieving TO in laparoscopic resection of HCC. The constructed nomogram has excellent predictive performance and can be used to identify high-risk patients in the early clinical stage and guide individualised intervention.

Purpose: Early postoperative recurrence of hepatocellular carcinoma (HCC) significantly impairs patient quality of life and shortens survival. However, existing models rely on single-center or single-dimensional data, making accurate detection of early postoperative HCC recurrence challenging. Thus, designing/evaluating a reliable, non-invasive, comprehensive tool to predict HCC recurrence risk is crucial for guiding postoperative individualized antitumor treatment and improving prognosis.

Patients and methods: We retrospectively enrolled patients with HCC (n=1424) receiving curative-intent hepatectomy at the First Affiliated Hospital of Army Medical University of China between December 2012 and December 2022. Patients were randomly stratified into training and testing cohorts in a 7:3 ratio. Using least absolute shrinkage and selection operator (LASSO) logistic and multivariate logistic regression, we screened optimal predictors and subsequently developed a nomogram alongside an online calculator. The prediction model was externally validated at two other medical institutions (n = 218). The area under the curve (AUC) of the receiver operating characteristic, calibration, and decision curves were used to evaluate model performance.

Results: The nomogram intuitively showed nine independent risk factors in the prediction model for short-term recurrence in patients with HCC: Edmondson Steiner III-IV, tumor satellite nodules, vascular invasion, largest tumor > 5 cm, alpha-fetoprotein (AFP) level ≥ 400 μg/L, DeRitis ratio ≥ 1.49, gamma-glutamyl transferase (GGT) level ≥ 63.5 U/L, prognostic nutritional index (PNI) < 46.18, and neutrophil-to-lymphocyte ratio (NLR) ≥ 1.91. The AUCs of the training, testing, and validation cohorts were 0.760 (95% CI: 0.731-0.790), 0.784 (95% CI: 0.741-0.828), and 0.787 (95% CI: 0.728-0.846), respectively, indicating good predictive performance. The calibration and decision curves indicated that the model could be translated into tangible clinical benefits.

Conclusion: We constructed and evaluated a nomogram based on inflammation-immunity-nutrition biomarker scores to predict early postoperative recurrence of HCC, offering a free, user-friendly online calculator for quick access to results. This calculator empowers clinicians to convert complex clinical data into actionable insights, enabling the design of risk-stratified postoperative management strategies.