Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Portal vein tumor thrombus(PVTT)is a common manifestation of advanced hepatocellular carcinoma(HCC),associated with poor prognosis and significant treatment challenges.Although various therapeutic options-including surgery,systemic therapies,and local treatments such as interventional procedures and radiotherapy-are available for HCC with PVTT,monotherapies often yield limited efficacy,highlighting the need for combined treatment strategies.With the advancement of radiotherapy technologies,stereotactic body radiation therapy(SBRT)has gained increasing recognition due to its high precision,ablative doses,and fewer treatment fractions.SBRT plays a crucial role in palliative care,conversion therapy,neoadjuvant,and adjuvant settings.Recent studies have demonstrated that SBRT,either alone or in combination with other modalities,significantly improves overall survival and local control rates in patients with HCC and PVTT.This review summarizes the current research progress of SBRT in the management of HCC with PVTT,emphasizing both monotherapy and combined approaches with surgery,interventional therapy,targeted agents,and immunotherapy,aiming to provide insights for clinical decision-making.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Portal vein tumor thrombus(PVTT)is a common manifestation of advanced hepatocellular carcinoma(HCC),associated with poor prognosis and significant treatment challenges.Although various therapeutic options-including surgery,systemic therapies,and local treatments such as interventional procedures and radiotherapy-are available for HCC with PVTT,monotherapies often yield limited efficacy,highlighting the need for combined treatment strategies.With the advancement of radiotherapy technologies,stereotactic body radiation therapy(SBRT)has gained increasing recognition due to its high precision,ablative doses,and fewer treatment fractions.SBRT plays a crucial role in palliative care,conversion therapy,neoadjuvant,and adjuvant settings.Recent studies have demonstrated that SBRT,either alone or in combination with other modalities,significantly improves overall survival and local control rates in patients with HCC and PVTT.This review summarizes the current research progress of SBRT in the management of HCC with PVTT,emphasizing both monotherapy and combined approaches with surgery,interventional therapy,targeted agents,and immunotherapy,aiming to provide insights for clinical decision-making.

Background and aims:Inflammatory factors play significant roles in the development and occurrence of hepatocellular carcinoma(HCC).However,the tumor-protective functions of growth differentiation factors(GDFs)in HCC are yet to be clarified.In this study,we aimed to evaluate the expression levels of 10 GDFs in tumor and paratumor tissues from patients with HCC and perform in vitro and in vivo ex-periments to elucidate the role of GDF7 in regulating the proliferation and metastasis of HCC.Methods:The gene expression of 10 GDFs was compared between HCC and paratumors using The Cancer Genome Atlas dataset and patient-derived tissues.A tumor microarray containing 108 HCC tissue samples was used to explore the prognostic value of GDF7 expression.Loss-of-function experiments were also performed in vitro and in vivo to investigate the role of GDF7 in HCC.Results:The mRNA and protein levels of GDF7 were significantly lower in HCC tumors than in para-tumors(P＜0.001).Kaplan-Meier analysis showed that decreased GDF7 expression in HCC was asso-ciated with worse overall survival(5-year rate:61.8％vs.27.5％,P＜0.001)and increased recurrence risk(P＜0.001).Multivariate Cox regression analysis demonstrated that low GDF7 expression,the presence of microvascular invasion,and elevated alpha-fetoprotein(AFP)levels were independent risk factors for tumor recurrence and poor survival.Downregulation of GDF7 also increased the tumor growth in HCC cells and in an HCC xenograft model.GDF7 knockdown promoted migration and invasion via epithelial-mesenchymal transition.Meanwhile,a negative correlation between JunB proto-oncogene(JUNB)and GDF7 was observed in HCC tissues.Modulating JUNB levels altered GDF7 protein expression.Conclusions:GDF7 is a potential biomarker for predicting superior outcomes in patients with HCC.GDF7 amplification is a potential therapeutic option for HCC.

Idiopathic pulmonary fibrosis(IPF)is a chronic progressive interstitial lung disease of unknown etiology,with a rapid disease course,poor prognosis,and the absence of effective therapeutic drugs.Mitochondrial dys-function is one of the crucial causes of inducing IPF.Silent information regulator 3(SIRT3)can restore mitochondrial ho-meostasis by inhibiting mitochondrial oxidative stress,repairing mitochondrial DNA damage,and ameliorating abnormal mitochondrial lipid metabolism.This paper summarizes the role and mechanism of SIRT3 in attenuating mitochondrial dys-function based on delineating the relationship between mitochondrial dysfunction and IPF,aiming to provide references for finding effective treatment methods for IPF.

Kupffer cells(KC),an important subset of immune cells in the liver,are essential for maintaining tissue homeostasis and responding quickly to liver damage.The complement receptor of the immuno-globulin superfamily(CRIg)is a receptor protein on the KC membrane.CRIg can not only capture pathogens in the blood flowing through the liver by complement binding but also mediate immune responses by regulating im-mune cells in the liver.Recent studies have confirmed the role of CRIg in regulating liver immunity.This article reviews the main modes of action of CRIg and the research progress of CRIg in regulating liver immunity.

Purpose: This study aims to evaluate the efficacy and safety of a new combination treatment of vinorelbine and pyrotinib in human epidermal growth factor receptor 2 (HER2)–positive metastatic breast cancer (MBC) and provide higher level evidence for clinical practice. Materials and Methods: This was a prospective, single-arm, phase 2 trial conducted at three institutions in China. Patients with HER2-positive MBC, who had previously been treated with trastuzumab plus a taxane or trastuzumab plus pertuzumab combined with a chemotherapeutic agent, were enrolled between March 2020 and December 2021. All patients received pyrotinib 400 mg orally once daily plus vinorelbine 25 mg/m2 intravenously or 60-80 mg/m2 orally on day 1 and day 8 of 21-day cycle. The primary endpoint was progression-free survival (PFS), and the secondary endpoints included the objective response rate (ORR), disease control rate (DCR), overall survival, and safety. Results: A total of 39 patients were enrolled. All patients had been pretreated with trastuzumab and 23.1% (n=9) of them had accepted trastuzumab plus pertuzumab. The median follow-up time was 16.3 months (95% confidence interval [CI], 5.3 to 27.2), and the median PFS was 6.4 months (95% CI, 4.0 to 8.8). The ORR was 43.6% (95% CI, 27.8% to 60.4%) and the DCR was 84.6% (95% CI, 69.5% to 94.1%). The median PFS of patients with versus without prior pertuzumab treatment was 4.6 and 8.3 months (p=0.017). The most common grade 3/4 adverse events were diarrhea (28.2%), neutrophil count decreased (15.4%), white blood cell count decreased (7.7%), vomiting (5.1%), and anemia (2.6%). Conclusion Pyrotinib plus vinorelbine showed promising efficacy and tolerable toxicity as second-line treatment in patients with HER2-positive MBC.

Objective:To explore the early and medium-long term outcomes of steatosis donor liver transplantation(LT)for an optimal clinical application.Methods:From January 2015 to December 2020, this retrospective cohort study was conducted jointly at Shulan (Hangzhou) Hospital, First Affiliated Hospital of Zhejiang University and First Hospital of Jilin University. The relevant clinicopathological and follow-up data were collected from 1535 LT recipients. For comparison, propensity score was utilized for case-control matching of steatosis and non-steatosis donor livers. According to presence or absence of liver steatosis, the recipients were divided into two groups of steatosis donor liver (n=243) and non-steatosis donor liver (n=1292). And 1∶1 propensity score matching was made for two groups. Then early and medium-long term outcomes of two groups were examined. Counts were described as absolute numbers. Kaplan-Meier method was employed for calculating survival time and plotting survival curve and Log-rank test for survival analysis. COX regression model was utilized for univariate and multivariate analyses. Based on basic metabolic disease pre-LT, steatosis donor liver recipients were divided into three subgroups: BMI ≥25 kg/m 2 with hypertension or diabetes (n=21), BMI<25 kg/m 2 and no hypertension or diabetes (n=130) and other recipients (n=92). A comparative study was performed for determining the prognosis of subgroups according to the different characteristics of recipient and donor liver. Results:No significant inter-group difference existed in 2-year survival post-LT ( P=0.174). However, significant inter-group difference in survival existed after 2 years post-LT ( P=0.004). And 3/5-year survival rate of steatosis donor liver was 66.4% and 44.2% respectively. Both were significantly lower than those of non-steatosis donor liver. Multivariate Cox regression analysis indicated that steatosis donor liver and male recipients were independent risk factors for prognosis >2 years survival post-LT( P=0.008, P=0.004). Subgroup analysis of steatosis liver donors showed that the prognosis of patients with BMI ≥25 kg/m 2 with hypertension or diabetes was significantly worse than other subgroups (BMI <25 kg/m 2 with no hypertension or diabetes and other recipients) <2 years survival post-LT ( P=0.029, P=0.043). Conclusions:Steatosis donor liver does not affect early survival of recipients, yet reduces medium-long term survival rate of recipients notably. In steatosis donor liver recipients, early survival rate declines markedly in recipients with preoperative BMI ≥25 kg/m 2 with hypertension or diabetes as compared with BMI <25 kg/m 2 with no hypertension or diabetes group.