Lei Han

Objective: To develop and validate a nomogram for predicting massive ascites after hepatectomy in hepatocellular carcinoma (HCC) patients. Methods: A retrospective, single‑center study of 232 HCC patients who underwent hepatectomy (February 2021 – July 2025) was conducted. Patients with preoperative bile leakage or active hemorrhage were excluded. Patients were grouped by postoperative ascites status (massive, n=66; non-massive, n=166). Predictors were screened via univariate and multivariate logistic regression. A nomogram was built and internally validated using 1000 bootstrap samples. Performance was assessed via ROC analysis, calibration, and decision curve analysis (DCA), Clinical Impact Curve (CIC. Results: Multivariate analysis identified four independent predictors: platelet count (OR=0.985), AST (OR=1.027), portal hypertension (OR=5.288), and operative time (OR=5.011). The nomogram achieved an AUC of 0.837 (95% CI: 0.781– 0.892) with good calibration (H-L test, P=0.860). DCA showed clinical net benefit across thresholds [0.00– 0.71] and [0.86– 0.93]and the clinical impact curve showing good concordance at risk thresholds above 0.4. Conclusion: The nomogram accurately predicts massive ascites risk using four perioperative variables and demonstrates strong clinical utility for individualized management.

Purpose: We developed a machine learning framework to predict the presence of tertiary lymphoid structures (TLSs) within tumors in patients with hepatocellular carcinoma (HCC). This framework uses computed tomography (CT) imaging and clinical data collected before surgery, providing a noninvasive method for prediction. Methods: We conducted a retrospective analysis of HCC patients who underwent surgery at the General Hospital of the Northern Theater Command’s Hepatobiliary Surgery Department between January 2017 and October 2024. Using Python software, we extracted radiomic features from preoperative CT images (arterial and portal venous phases). We then selected features associated with intratumoral TLSs using statistical methods, including intraclass correlation coefficient (ICC), Pearson correlation, t-tests, and LASSO regression. Three models were developed—clinical, radiomics, and combined—using machine learning techniques and independent clinical predictors. A predictive nomogram was created and evaluated using the area under the ROC curve (AUC) and calibration analysis. Results: Our study included 171 HCC patients, with 80 showing negative and 91 showing positive expression of intratumoral TLSs. Multivariate analysis identified the albumin-bilirubin (ALBI) score as an independent predictor of intratumoral TLSs expression. The combined model demonstrated the highest predictive accuracy, with AUCs of 0.947 in the training set and 0.909 in the validation set, outperforming both the clinical (AUC: 0.709 training, 0.714 validation) and radiomics (AUC: 0.935 training, 0.890 validation) models. Conclusion: Our combined machine learning model, which integrates preoperative CT imaging and clinical data, provides an accurate, noninvasive method for assessing intratumoral TLSs expression in HCC. This tool has the potential to enhance clinical decision-making, guide therapeutic planning, and facilitate personalized treatment strategies for HCC patients.

BACKGROUND Hepatocellular carcinoma (HCC) is one of the most common malignant tumours of the digestive system worldwide. The expression of Ki-67 is crucial for the diagnosis, treatment, and prognostic evaluation of HCC. AIM To construct a machine learning model for the preoperative evaluation of Ki-67 expression in HCC and to assist in clinical decision-making. METHODS This study included 164 pathologically confirmed HCC patients. Radiomic features were extracted from the computed tomography images reconstructed by superresolution of the intratumoral and peritumoral regions. Features were selected via the intraclass correlation coefficient, t tests, Pearson correlation coefficients and least absolute shrinkage and selection operator regression methods, and models were constructed via various machine learning methods. The best model was selected, and the radiomics score (Radscore) was calculated. A nomogram incorporating the Radscore and clinical risk factors was constructed. The predictive performance of each model was evaluated via receiver operating characteristic (ROC) curves and calibration curves, and decision curve analysis was used to assess the clinical benefits. RESULTS In total, 164 HCC patients, namely, 104 patients with high Ki-67 expression and 60 with low Ki-67 expression, were included. Compared with the models in which only intratumoral or peritumoral features were used, the fusion model in which intratumoral and peritumoral features were combined demonstrated stronger predictive ability. Moreover, the clinical-radiomics model including the Radscore and clinical features had higher predictive performance than did the fusion model (area under the ROC curve = 0.848 vs 0.780 in the training group, area under the ROC curve = 0.830 vs 0.760 in the validation group). The calibration curve showed good consistency between the predicted probability and the actual probability, and the decision curve further confirmed its clinical benefit. CONCLUSION A machine learning model based on the radiomic features of the intratumoral and peritumoral regions on superresolution computed tomography in conjunction with clinical factors can accurately evaluate Ki-67 expression. The model provides valuable assistance in selecting treatment strategies for HCC patients and contributes to research on neoadjuvant therapy for liver cancer.

MMP14 is a diagnostic gene of intrahepatic cholangiocarcinoma associated with immune cell infiltration

Background: With the advancement of hepatocellular carcinoma (HCC) treatment technology, the treatment options for HCC patients have increased. However, due to high heterogeneity, among other reasons, the five-year survival rate of patients is still very low. Currently, gene expression prognostic models can suggest more appropriate strategies for the treatment of HCC. This study investigates the role of FAT10 in hepatocarcinogenesis and its underlying mechanism. Methods: The expression of FAT10 was detected by immunohistochemical method using tissue arrays containing 4 specimens of patients with digestive cancer. The expression of FAT10 was determined by a tissue microarray which included 286 pairs of HCC samples and corresponding normal mucosae and was further confirmed by real-time polymerase chain reaction (PCR) and western blot. The Kaplan-Meier survival curve was used to determine the correlation of FAT10 expression with patients’ recurrence and overall survival (OS) rate. In vivo, liver fibrosis, cirrhosis, and HCC models were established to assess the FAT10 expression. Moreover, FAT10 over-expressing cell lines were used to determine the molecular mechanism underlying the FAT10-induced cell proliferation and hepatocarcinogenesis by reporter gene measure, real-time PCR, and western blot. Based on TCGA database, signal pathways associated with FAT10 and HCC invasion and metastasis were analyzed by KEGG enrichment analyze. Results: Overexpression of FAT10 in HCC was observed in this study compared with its expression in other digestive tumors. Clinicopathological analysis revealed that FAT10 expression levels were closely associated with tumor diameters and poor prognosis of HCC. This study also confirmed through in vivo experiments that the expression of FAT10 in liver fibrosis, cirrhosis, and HCC gradually increases. Further study revealed that forced FAT10 expression enhanced the growth ability of HCC cells and mediated the degradation of the critical anti-cancer protein p53, which led to carcinogenesis. Finally, 9 signal pathways related to HCC metastasis were obtained through bioinformatics analysis. Conclusions: FAT10 may act as a proto-oncogene that facilitates HCC carcinogenesis by mediating p53 degradation, and the expression of FAT10 is negatively correlated with the prognosis of HCC patients. FAT10 is expected to become a potential combined target and prognostic warning marker for HCC treatment.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. During the recent years, external-beam radiation therapy (EBRT) has been safely and effectively employed for the management of HCC. We overviewed the current evidence regarding the efficacy and safety of EBRT for HCC according to the different target population. PubMed database was searched for identifying English-language full-text articles regarding EBRT for the treatment of HCC. Search items were "hepatocellular carcinoma AND radiation therapy". Until now, preliminary evidence has suggested the following role of EBRT for HCC. 1) EBRT, especially stereotactic body radiation therapy, is an emerging choice of therapy for small HCC. 2) EBRT combined with non-surgical treatment can achieve an excellent intrahepatic tumor control and a potential survival benefit for huge HCC. 3)Adjunctive EBRT may improve the efficacy of transarterial chemoembolization for HCC with portal vein tumor thrombosis. 4) EBRT can relieve the pain and improve the quality of life for patients with extrahepatic metastases. 5) EBRT may be a bridge to liver transplantation by minimizing the tumor progression. 6) Adjunctive EBRT may reduce the tumor recurrence and improve the survival after resection. In summary, EBRT is a promising choice of treatment of HCC. However, more high-quality evidence is needed to further establish the status of EBRT for the management of HCC.

14-3-3β has been demonstrated to possess the oncogenic potential, and its increased expression has been detected in multiple types of carcinomas. However, majority of previous studies focused on the role of 14-3-3β in tumor cell proliferation and apoptosis, leaving much to be elucidated about its function in tumor cell invasion and metastasis. Hence, the present study aimed to investigate the role of 14-3-3β in the invasion of hepatocellular carcinoma (HCC) cells and the implications in the prognosis of HCC patients. We first examined the expression of 14-3-3β in the primary tumors of HCC patients with or without portal vein tumor thrombus (PVTT), and found that 14-3-3β expression was higher in the primary tumors with PVTT, and the level was even higher in the PVTTs. Kaplan-Meier curves and multivariate analysis revealed that high expression of 14-3-3β was associated with overall survival (OS) and time to recurrence (TTR) of HCC patients. In addition, ectopic expression of 14-3-3β in HCC cell lines led to enhanced migration ability and invasiveness, as well as up-regulation of matrix metalloproteinase 2 and 9, which could be suppressed by inhibiting the activation of Akt and nuclear factor-κB (NF-κB) signaling. Furthermore, we identified a correlated elevation of 14-3-3β and p-Akt in the primary tumors of HCC patients, and showed that a combinatory detection of 14-3-3β and p-Akt could better predict post-surgical outcome of HCC patients.

目的: 研究Genistein对人胰腺癌细胞系Panc-1诱导细胞上皮-间质样转化的作用, 以探讨Genistein抑制胰腺癌侵袭作用的机制. 方法: 用TGF-β1和不同浓度Genistein(0、1、25、50 μmol/L)处理Panc-1细胞, 对照组加入等量PSB. 采用Transwell小室法检测Genistein作用于TGF-β1诱导Panc-1细胞后侵袭力的变化; RT-PCR技术检测波形蛋白(Vimentin)、E-钙依赖黏附素(E-Cadherin)的mRNA表达; Western blot蛋白印迹法检测E-Cadherin蛋白的表达; 应用显微镜观察分析细胞结构的改变. 结果: TGF-β1对Panc-1细胞有显著诱导上皮-间质转化的作用, 并增加细胞侵袭力, Genistein不但对诱导后Panc-1细胞侵袭力有抑制, 对细胞上皮-间质样转化也有抑制, 且这种作用呈剂量依赖性. 0 μmol/L Genistein组细胞计数明显比对照组高(99.16±11.30 vs 65.46±8.99, P结论: Genistein具有明显抑制TGF-β1诱导的人胰腺癌细胞Panc-1侵袭力的作用, 这可能是其抗侵袭作用的机制之一.