Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms.

Background: Most patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients. Methods: Eligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (DL) algorithms. The model's performance was evaluated using C-index, receiver operating characteristic curve, Brier score and decision curve analysis, respectively, and the best model was interpreted using SHapley additive explanations (SHAP) interpretability technique. Results: A total of eight variables were included in the follow-up study, our analysis identified that the gradient boosted machine (GBM) model was the best prognostic model for advanced MHCC. In particular, the GBM model in the training cohort had a C-index of 0.73, a Brier score of 0.124, with area under the curve (AUC) values above 0.78 at the first, third, and fifth year. Importantly, the model also performed well in test cohort. The Kaplan-Meier (K-M) survival analysis demonstrated that the newly developed risk stratification system could well differentiate the prognosis of patients. Conclusion: Of the ML models, GBM model could predict the prognosis of advanced MHCC patients most accurately.

Knockdown of SLC16A3 decreases extracellular lactate concentration in hepatocellular carcinoma, alleviates hypoxia and induces ferroptosis.

SLC16A3/monocarboxylate transporter 4 (MCT4) regulates intracellular lactate transport and is highly expressed in many tumors, indicating poor prognosis. It may be related to inducing hypoxia, apoptosis and other mechanisms, but the study of MCT4 in HCC is far from complete. In this study, we first analyzed the expression of SLC16A3 in HCC tumor and non-tumor tissue samples based on TCGA data and immunohistochemistry. Subsequently, the effects of SLC16A3 expression on cell proliferation and invasion were analyzed using hepatocellular carcinoma (HCC) lines, and Western blot (WB) analysis was performed to explore the changes in pathway proteins and ferroptosis proteins. Finally, the drug sensitivity was tested by CCK8 kit. We found that SLC16A3 was significantly upregulated in tumor tissues, and was significantly correlated with TNM stage, histological grade, and macrovascular invasion. TCGA data and WB analysis showed that the high expression of SLC16A3 induced hypoxia, and knockdown could reverse hypoxia and inhibit ERK phosphorylation, thus limiting the malignant behavior of HCC cells. Moreover, knockdown of SLC16A3 significantly increased the level of lipid peroxidation and reactive oxygen species (ROS), while the expressions of GPX4, DHODH and SLC7A11 were inhibited. The expression of SLC16A3 affected the sensitivity of HCC cells to chemotherapy and targeted drugs, and RNA sequencing data suggested that the expression level influenced tumor microenvironment and response to immunotherapy. So, we draw a conclude that SLC16A3 is associated with poor prognosis of HCC. Inhibition of SLC16A3 expression is a potential therapeutic target for HCC.

Development of machine learning models for patients in the high intrahepatic cholangiocarcinoma incidence age group.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) has a poor prognosis and is understudied. Based on the clinical features of patients with ICC, we constructed machine learning models to understand their importance on survival and to accurately determine patient prognosis, aiming to develop reference values to guide physicians in developing more effective treatment plans. METHODS: This study used machine learning (ML) algorithms to build prediction models using ICC data on 1,751 patients from the SEER (Surveillance, Epidemiology, and End Results) database and 58 hospital cases. The models' performances were compared using receiver operating characteristic curve analysis, C-index, and Brier scores. RESULTS: A total of eight variables were used to construct the ML models. Our analysis identified the random survival forest model as the best for prognostic prediction. In the training cohort, its C-index, Brier score, and Area Under the Curve values were 0.76, 0.124, and 0.882, respectively, and it also performed well in the test cohort. Kaplan-Meier survival analysis revealed that the model could effectively determine patient prognosis. CONCLUSIONS: To our knowledge, this is the first study to develop ML prognostic models for ICC in the high-incidence age group. Of the ML models, the random survival forest model was best at prognosis prediction.

S100A16 is a potential target for reshaping the tumor microenvironment in the hypoxic context of liver cancer.

BACKGROUND: The research on the S100 family has garnered significant attention; however, there remains a dearth of understanding regarding the precise role of S100A16 in the tumor microenvironment of liver cancer. METHOD: Comprehensive analysis was conducted on the expression of S100A16 in tumor tissues and its correlation with hypoxia genes. Furthermore, an investigation was carried out to examine the association between S100A16 and infiltration of immune cells in tumors as well as immunotherapy. Relevant findings were derived from the analysis of single cell sequencing data, focusing on the involvement of S100A16 in both cellular differentiation and intercellular communication. Finally, we validated the expression of S100A16 in liver cancer by Wuhan cohort and multiplexed immunofluorescence to investigate the correlation between S100A16 and hypoxia. RESULT: Tumor tissues displayed a notable increase in the expression of S100A16. A significant correlation was observed between S100A16 and genes associated with hypoxic genes. Examination of immune cell infiltration revealed an inverse association between T cell infiltration and the level of S100A16 expression. The high expression group of S100A16 exhibited a decrease in the expression of genes related to immune cell function. Single-cell sequencing data analysis revealed that non-immune cells predominantly expressed S100A16, and its expression levels increased along with the trajectory of cell differentiation. Additionally, there were significant variations observed in hypoxia genes as cells underwent differentiation. Cellular communication identified non-immune cells interacting with immune cells through multiple signaling pathways. The Wuhan cohort verified that S100A16 expression was increased in liver cancer. The expression of S100A16 and HIF was simultaneously elevated in endothelial cells. CONCLUSION: The strong association between S100A16 and immune cell infiltration is observed in the context of hypoxia, indicating its regulatory role in shaping the hypoxic tumor microenvironment in liver cancer.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq data reveals dynamic changes in tumor-associated neutrophils in the tumor microenvironment of hepatocellular carcinoma and leads to the establishment of a neutrophil-related prognostic model.

BACKGROUND: Analysis of hepatocellular carcinoma (HCC) single-cell sequencing data was conducted to explore the role of tumor-associated neutrophils in the tumor microenvironment. METHODS: Analysis of single-cell sequencing data from 12 HCC tumor cores and five HCC paracancerous tissues identified cellular subpopulations and cellular marker genes. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were used to establish and validate prognostic models. xCELL, TIMER, QUANTISEQ, CIBERSORT, and CIBERSORT-abs analyses were performed to explore immune cell infiltration. Finally, the pattern of tumor-associated neutrophil roles in tumor microenvironmental components was explored. RESULTS: A total of 271 marker genes for tumor-associated neutrophils were identified based on single-cell sequencing data. Prognostic models incorporating eight genes were established based on TCGA data. Immune cell infiltration differed between the high- and low-risk groups. The low-risk group benefited more from immunotherapy. Single-cell analysis indicated that tumor-associated neutrophils were able to influence macrophage, NK cell, and T-cell functions through the IL16, IFN-II, and SPP1 signaling pathways. CONCLUSION: Tumor-associated neutrophils regulate immune functions by influencing macrophages and NK cells. Models incorporating tumor-associated neutrophil-related genes can be used to predict patient prognosis and immunotherapy responses.

Tumor-associated endothelial cell prognostic risk model and tumor immune environment modulation in liver cancer based on single-cell and bulk RNA sequencing: Experimental verification.

BACKGROUND: To build a prognostic and immunotherapeutic response prediction model for liver cancer based on marker genes of tumor-associated endothelial cell (TEC). METHOD: Single cell sequencing data from Gene Expression Omnibus (GEO) liver cancer patients were utilized to identify TEC subpopulations. Models were built from transcriptomic and clinical data of TCGA liver cancer patients. The GSE76427 and ICGC databases were used as independent validation sets. Time-dependent receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to verify the ability of the model to predict survival. XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA were applied to evaluate tumor immune cell infiltration. The TIDE score was used to predict the effect of immunotherapy. Immune blockade checkpoint gene, tumor mutational load and GSVA enrichment analyses were further explored. The expression levels of candidate genes were measured and validated by real-time PCR between liver cancer tissues and adjacent nontumor liver tissues. RESULTS: Eighty-seven genes were identified as marker genes for TECs. IGFBP3, RHOC, S100A16, FSCN1, and CLEC3B were included in the constructed prognostic model. Time-dependent ROC curve values were higher than 0.700 in both the model and validation groups. The low risk group exhibited high immune cell infiltration and function than the higher risk group. The TIDE score indicated that the low-risk group benefited more from immunotherapy than the high-risk group. The risk score and multiple immune blockade checkpoint genes and immune-related pathways were strongly correlated. CONCLUSION: Novel signatures of TEC marker genes showed a powerful ability to predict prognosis and immunotherapy response in patients with liver cancer.

Analysis of M2 macrophage-associated risk score signature in pancreatic cancer TME landscape and immunotherapy.

Background: M2 macrophages perform an influential role in the progression of pancreatic cancer. This study is dedicated to explore the value of M2 macrophage-related genes in the treatment and prognosis of pancreatic cancer. Methods: RNA-Seq and clinical information were downloaded from TCGA, GEO and ICGC databases. The pancreatic cancer tumour microenvironment was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to detect M2 macrophage-associated gene modules. Univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression were applied to develop the prognostic model. The modelling and validation cohorts were divided into high-risk and low-risk groups according to the median risk score. The nomogram predicting survival was constructed based on risk scores. Correlations between risk scores and tumour mutational load, clinical variables, immune checkpoint blockade, and immune cells were further explored. Finally, potential associations between different risk models and chemotherapeutic agent efficacy were predicted. Results: The intersection of the WGCNA results from the TCGA and GEO data screened for 317 M2 macrophage-associated genes. Nine genes were identified by multivariate COX regression analysis and applied to the construction of risk models. The results of GSEA analysis revealed that most of these genes were related to signaling, cytokine receptor interaction and immunodeficiency pathways. The high and low risk groups were closely associated with tumour mutational burden, immune checkpoint blockade related genes, and immune cells. The maximum inhibitory concentrations of metformin, paclitaxel, and rufatinib lapatinib were significantly differences on the two risk groups. Conclusion: WGCNA-based analysis of M2 macrophage-associated genes can help predict the prognosis of pancreatic cancer patients and may provide new options for immunotherapy of pancreatic cancer.

Construction and validation of a nomogram for patients with multiple hepatocellular carcinoma: A SEER-based study.

BACKGROUND: American Joint Committee on Cancer (AJCC)-TNM system doesn't accurately predict prognosis. Our study was designed to identify prognostic factors in patients with multiple hepatocellular carcinoma (MHCC), establish and validate a nomogram model to predict the risk and overall survival (OS) of MHCC patients. METHODS: We selected eligible HCC patients from the Surveillance, Epidemiology, and End Results (SEER) database, used univariate and multivariate COX regression to determine prognostic factors in MHCC patients, and used these factors to build a nomogram. The accuracy of the prediction was evaluated using the C-index, receiver operating characteristic (ROC) and calibration curve. Decision curve analysis (DCA), net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to compare the nomogram with AJCC-TNM staging system. Finally, the prognosis of different risks was analyzed using Kaplan-Meier (K-M) method. RESULTS: 4950 eligible patients with MHCC were enrolled in our study and randomly assigned to the training cohort and test cohort in a 7:3 ratio. After COX regression analysis, age, sex, histological grade, AJCC-TNM stage, tumor size, alpha-fetoprotein (AFP), surgery, radiotherapy and chemotherapy in total 9 factors could be used to independently determine OS of patients. the above factors were used to construct a nomogram, and the consistency C-index was 0.775. C-index, DCA, NRI and IDI showed that our nomogram was superior to the AJCC-TNM staging system. K-M plots for OS were performed using the log-rank test, the P-value of which was <0.001. CONCLUSIONS: The practical nomogram can provide more accurate prognostic prediction for multiple hepatocellular carcinoma patients.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on NK cell marker genes to predict prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Prognostic modeling of NK cell marker genes in patients with hepatocellular carcinoma based on single cell sequencing and transcriptome data analysis. METHODS: Marker genes of NK cells were analyzed according to single cell sequencing data of hepatocellular carcinoma. Univariate Cox regression, lasso regression analysis, and multivariate Cox regression were performed to estimate the prognostic value of NK cell marker genes. TCGA, GEO and ICGC transcriptomic data were applied to build and validate the model. Patients were divided into high and low risk groups based on the median risk score. XCELL, timer, quantitative sequences, MCP counter, EPIC, CIBERSORT and CIBERSORT-abs were performed to explore the relationship between risk score and tumor microenvironment in hepatocellular carcinoma. Finally the sensitivity of the model to chemotherapeutic agents was predicted. RESULTS: Single-cell sequencing identified 207 marker genes for NK cells in hepatocellular carcinoma. Enrichment analysis suggested that NK cell marker genes were mainly involved in cellular immune function. Eight genes were selected for prognostic modeling after multifactorial COX regression analysis. The model was validated in GEO and ICGC data. Immune cell infiltration and function were higher in the low-risk group than in the high-risk group. The low-risk group was more suitable for ICI and PD-1 therapy. Half-maximal inhibitory concentrations of Sorafenib, Lapatinib, Dabrafenib, and Axitinib were significantly different on the two risk groups. CONCLUSION: A new signature of hepatocyte NK cell marker genes possesses a powerful ability to predict prognosis and immunotherapeutic response in patients with hepatocellular carcinoma.

Clinical Features and Prognostic Models in Patients with Intrahepatic Cholangiocarcinoma: a Population-Based Analysis.

BACKGROUND: This study aims to construct a risk classification system and a nomogram in intrahepatic cholangiocarcinomafor patients (ICC). METHODS: Three thousand seven hundred thirty-seven patients diagnosed with ICC between 2010 and 2015 were selected from the Surveillance, Epidemiology and End Results. The consistency index, time-dependent receiver operating characteristic curve, and the calibration plots were adopted to evaluate the effective performance of nomogram. Decision curve analysis (DCA), net reclassification index (NRI), and comprehensive discrimination improvement (IDI) were used to compare the advantages and disadvantages of two models. Kaplan-Meier curve showed the difference in prognosis among different groups. RESULTS: Ten variables were selected to establish the nomogram for ICCA. The C-index (training cohort: 0.765, P < 0.05; validation cohort: 0.776, P < 0.05) and the time-dependent AUCs (the training cohort: the values of 1, 3, 5 years were 0.836, 0.873, and 0.888; the validation cohort: the values of 1, 3, 5 years were 0.833, 0.838, and 0.881) showed satisfactory discrimination. The calibration curves also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 3-, 5-year CSS: 0.879, 0.94, 0.771; validation cohort: 1-, 3-, 5-year CSS: 0.905, 0.945, 0.717) and IDI (training cohort: 1-, 3-, 5-year CSS: 0.24, 0.23, 0.22; validation cohort: 1-, 3-, 5-year CSS: 0.24, 0.46, 0.27) (P < 0.05) (compared with AJCC staging). DCA showed that the new model was more practical and had better recognition than AJCC staging. CONCLUSIONS: A new risk stratification system for ICC patients has been developed, which can be a practical tool for patient management.

Establishment and validation of a prognostic nomogram for extrahepatic cholangiocarcinoma.

Simple summary: Accurately estimate the prognosis of patients with ECCA is important. However, the TNM system has some limitations, such as low accuracy, exclusion of other factors (e.g., age and sex), and poor performance in predicting individual survival risk. In contrast, a nomogram-based clinical model related to a comprehensive analysis of all risk factors is intuitive and straightforward, facilitating the probabilistic analysis of tumor-related risk factors. Simultaneously, a nomogram can also effectively drive personalized medicine and facilitate clinicians for prognosis prediction. Therefore, we construct a novel practical nomogram and risk stratification system to predict CSS in patients with ECCA. Background: Accurately estimate the prognosis of patients with extrahepatic cholangiocarcinoma (ECCA) was important, but the existing staging system has limitations. The present study aimed to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in ECCA patients. Methods: 3415 patients diagnosed with ECCA between 2010 and 2015 were selected from the SEER database and randomized into a training cohort and a validation cohort at 7:3. The nomogram was identified and calibrated using the C-index, receiver operating characteristic curve (ROC), and calibration plots. Decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination improvement (IDI) and the risk stratification were used to compare the nomogram with the AJCC staging system. Results: Nine variables were selected to establish the nomogram. The C-index (training cohort:0.785; validation cohort:0.776) and time-dependent AUC (>0.7) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. The NRI (training cohort: 1-, 2-, and 3-year CSS:0.27, 0.27,0.52; validation cohort:1-,2-,3-year CSS:0.48,0.13,0.34), IDI (training cohort: 1-, 2-, 3-year CSS:0.22,0.18,0.16; validation cohort: 1-,2-,3-year CSS:0.18,0.16,0.17), and DCA indicated that the established nomogram significantly outperformed the AJCC staging system (P<0.05) and had better recognition compared to the AJCC staging system. Conclusions: We developed a practical prognostic nomogram to help clinicians assess the prognosis of patients with ECCA.

Low-grade hepatocellular carcinoma characteristics, a practical nomogram and risk stratification system: a SEER population-based study.

BACKGROUND: The purpose of this study is to establish a nomogram and risk stratification system to predict OS in patients with low-grade HCC. RESEARCH DESIGN AND METHODS: Data were extracted from the SEER database. C-index, time-dependent AUCs, and calibration plots were used to evaluate the effective performance of the nomogram. NRI, IDI, and DCA curves were adopted to compare the clinical utility of nomogram with AJCC. RESULTS: 3415 patients with low-grade HCC were available. The C-indices for the training and validation cohorts were 0.773 and 0.772. The time-dependent AUCs in the training cohort were 0.821, 0.817, and 0.846 at 1, 3 and 5 years. Calibration plots for 1-, 3- and 5-year OS showed good consistency between actual observations and that predicted by the nomogram. The values of NRI at 1, 3, and 5 years were 0.37, 0.66, and 0.64. The IDI values at 1, 3, and 5 years were 0.11, 0.16, and 0.23 (P< 0.001). DCA curves demonstrated that the nomogram showed better ability of predicting 1-, 3-, and 5-year OS probabilities than AJCC. CONCLUSIONS: A nomogram and risk stratification system for predicting OS in patients with low-grade HCC were established and validated.

Advances in Promoting the Efficacy of Chimeric Antigen Receptor T Cells in the Treatment of Hepatocellular Carcinoma.

HCC, one of the most common and deadly cancers worldwide, develops from hepatocytes and accounts for more than 90% of primary liver cancers. The current widely used treatment modalities are far from meeting the needs of liver cancer patients. CAR-T cell therapy, which has recently emerged, has shown promising efficacy in lymphoma and hematologic cancers, but there are still many challenges to overcome in its application to the clinical treatment of HCC, including osmotic barriers, the inhibition of hepatocellular carcinoma microenvironment activity, the limited survival and killing ability of CAR-T cells, and inevitable side effects, among others. As a result, a number of studies have begun to address the suboptimal efficacy of CAR-T cells in HCC, and many of these schemes hold good promise. This review focuses on advances in the past five years aimed at promoting the efficacy of CAR-T cell therapy for treatment of HCC.

Clinical features and prognostic factors in patients with microvascular infiltration of hepatocellular carcinoma: Development and validation of a nomogram and risk stratification based on the SEER database.

Background: The goal is to establish and validate an innovative prognostic risk stratification and nomogram in patients of hepatocellular carcinoma (HCC) with microvascular invasion (MVI) for predicting the cancer-specific survival (CSS). Methods: 1487 qualified patients were selected from the Surveillance, Epidemiology and End Results (SEER) database and randomly assigned to the training cohort and validation cohort in a ratio of 7:3. Concordance index (C-index), area under curve (AUC) and calibration plots were adopted to evaluate the discrimination and calibration of the nomogram. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities and compare it to the American Joint Committee on Cancer (AJCC) tumor staging system. C-index, net reclassification index (NRI) and integrated discrimination improvement (IDI) were applied to evaluate the improvement of the new model over the AJCC tumor staging system. The new risk stratifications based on the nomogram and the AJCC tumor staging system were compared. Results: Eight prognostic factors were used to construct the nomogram for HCC patients with MVI. The C-index for the training and validation cohorts was 0.785 and 0.776 respectively. The AUC values were higher than 0.7 both in the training cohort and validation cohort. The calibration plots showed good consistency between the actual observation and the nomogram prediction. The IDI values of 1-, 3-, 5-year CSS in the training cohort were 0.17, 0.16, 0.15, and in the validation cohort were 0.17, 0.17, 0.17 (P<0.05). The NRI values of the training cohort were 0.75 at 1-year, 0.68 at 3-year and 0.67 at 5-year. The DCA curves indicated that the new model more accurately predicted 1-year, 3-year, and 5-year CSS in both training and validation cohort, because it added more net benefit than the AJCC staging system. Furthermore, the risk stratification system showed the CSS in different groups had a good regional division. Conclusions: A comprehensive risk stratification system and nomogram were established to forecast CSS for patients of HCC with MVI.

Involvement of adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1 in diallyl trisulfide-induced cytotoxicity in hepatocellular carcinoma cells.

It has been demonstrated that APPL1 (adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1) is involved in the regulation of several growth-related signaling pathways and thus closely associated with the development and progression of some cancers. Diallyl trisulfide (DAT), a garlic-derived bioactive compound, exerts selective cytotoxicity to various human cancer cells through interfering with pro-survival signaling pathways. However, whether and how DAT affects survival of human hepatocellular carcinoma (HCC) cells remain unclear. Herein, we tested the hypothesis of the involvement of APPL1 in DAT-induced cytotoxicity in HCC HepG2 cells. We found that Lys 63 (K63)-linked polyubiquitination of APPL1 was significantly decreased whereas phosphorylation of APPL1 at serine residues remained unchanged in DAT-treated HepG2 cells. Compared with wild-type APPL1, overexpression of APPL1 K63R mutant dramatically increased cell apoptosis and mitigated cell survival, along with a reduction of phosphorylation of STAT3, Akt, and Erk1/2. In addition, DAT administration markedly reduced protein levels of intracellular TNF receptor-associated factor 6 (TRAF6). Genetic inhibition of TRAF6 decreased K63-linked polyubiquitination of APPL1. Moreover, the cytotoxicity impacts of DAT on HepG2 cells were greatly attenuated by overexpression of wild-type APPL1. Taken together, these results suggest that APPL1 polyubiquitination probably mediates the inhibitory effects of DAT on survival of HepG2 cells by modulating STAT3, Akt, and Erk1/2 pathways.

A Practical Nomogram and Risk Stratification System Predicting the Cancer-Specific Survival for Patients With Advanced Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) has the highest cancer-related mortality rate. This study aims to create a nomogram to predict the cancer-specific survival (CSS) in patients with advanced hepatocellular carcinoma. Methods: Patients diagnosed with advanced HCC (AJCC stage III and IV) during 1975 to 2018 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Qualified patents were randomized into training cohort and validation cohort at a ratio of 7:3. The results of univariate and multivariate Cox regression analyses were used to construct the nomogram. Consistency index (C-index), area under the time-dependent receiver operating characteristic (ROC) curve [time-dependent area under the curve (AUC)], and calibration plots were used to identify and calibrate the nomogram. The net reclassification index (NRI), integrated discrimination improvement (IDI), and C-index, and decision curve analysis DCA were adopted to compare the nomogram's clinical utility with the AJCC criteria. Results: The 3,103 patients with advanced hepatocellular carcinoma were selected (the training cohort: 2,175 patients and the validation cohort: 928 patients). The C-index in both training cohort and validation cohort were greater than 0.7. The AUC for ROC in the training cohort was 0.781, 0.771, and 0.791 at 1, 2, and 3 years CSS, respectively. Calibration plots showed good consistency between actual observations and the 1-, 2-, and 3-year CSS predicted by the nomogram. The 1-, 2-, and 3-year NRI were 0.77, 0.46, and 0.48, respectively. The 1-, 2-, and 3-year IDI values were 0.16, 0.15, and 0.12 (P < 0.001), respectively. DCA curves in both the training and validation cohorts demonstrated that the nomogram showed better predicted 1-, 2-, and 3-year CSS probabilities than AJCC criteria. Conclusions: This study established a practical nomogram for predicting CSS in patients with advanced HCC and a risk stratification system that provided an applicable tool for clinical management.

A Practical Nomogram and Risk Stratification System Predicting Cancer-Specific Survival for Hepatocellular Carcinoma Patients With Severe Liver Fibrosis.

Objective: Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide. This study aims to construct a novel practical nomogram and risk stratification system to predict cancer-specific survival (CSS) in HCC patients with severe liver fibrosis. Methods: Data on 1,878 HCC patients with severe liver fibrosis in the period 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results database (SEER). Patients were block-randomized (1,316 training cohort, 562 validation cohort) by setting random seed. Univariate and multivariate COX regression analyses were employed to select variables for the nomogram. The consistency index (C-index), the area under time-dependent receiver operating characteristic curve (time-dependent AUC), and calibration curves were used to evaluate the performance of the nomogram. Decision curve analysis (DCA), the C-index, the net reclassification index (NRI), and integrated discrimination improvement (IDI) were used to compare the nomogram with the AJCC tumor staging system. We also compared the risk stratification of the nomogram with the American Joint Committee on Cancer (AJCC) staging system. Results: Seven variables were selected to establish the nomogram. The C-index (training cohort: 0.781, 95%CI: 0.767-0.793; validation cohort: 0.793, 95%CI = 95%CI: 0.779-0.798) and the time-dependent AUCs (the training cohort: the values of 1-, 3-, and 5 years were 0.845, 0.835, and 0.842, respectively; the validation cohort: the values of 1-, 3-, and 5 years were 0.861, 0.870, and 0.876, respectively) showed satisfactory discrimination. The calibration plots also revealed that the nomogram was consistent with the actual observations. NRI (training cohort: 1-, 2-, and 3-year CSS: 0.42, 0.61, and 0.67; validation cohort: 1-, 2-, and 3-year CSS: 0.26, 0.52, and 0.72) and IDI (training cohort: 1-, 3-, and 5-year CSS:0.16, 0.20, and 0.22; validation cohort: 1-, 3-, and 5-year CSS: 0.17, 0.26, and 0.30) indicated that the established nomogram significantly outperformed the AJCC staging system (P < 0.001). Moreover, DCA also showed that the nomogram was more practical and had better recognition. Conclusion: A nomogram for predicting CSS for HCC patients with severe liver fibrosis was established and validated, which provided a new system of risk stratification as a practical tool for individualized treatment and management.

Protective Mechanism of MIF Inhibitor ISO-1 on Intrahepatic Bile Duct Cells in Rats with Severe Acute Pancreatitis.

AIMS: This study aimed to explore the protection mechanism of ISO-1 on severe acute pancreatitis-associated intrahepatic bile duct (IBD) injury in rats. METHODS: Forty-eight specific-pathogen-free male Wistar rats were randomly divided into four groups (N = 12): a sham operation group (SO group), a severe acute pancreatitis model group (SAP group), a ISO-1 treatment group (ISO-1 + SAP group), and a ISO-1 control group (ISO-1 + SO group). All rats were killed after 12 h of being made models. Immunohistochemistry was used to detect the expression of MIF and P38 in IBD cells. MIF mRNA expression in IBD cells was observed using real-time fluorescent quantitative polymerase chain reaction (real-time PCR). In addition, Western blotting was performed to detect the protein expression of P38, phosphorylated P38 (P-P38), nuclear factor-κB (NF-κB p65), and tumor necrosis factor alpha (TNF-α). Enzyme-linked immunosorbent assays were used to analyze the levels of TNF-α, IL-1ß, and IL-6 in the IBD of rats. RESULTS: Compared with SAP, after treatment with ISO-1, the pathological injuries of pancreas, liver, and IBD cells in ISO-1 treatment group remarkably relieved. The expression of MIF in the IBD cells was significantly downregulated both at mRNA and at protein levels in ISO-1 treatment group. Besides, the protein expression levels of P38, P-P38, NF-κBp65, TNF-α, IL-1ß, and IL-6 in the IBD in rats were also significantly decreased in ISO-1 treatment group (all P < 0.05). CONCLUSION: ISO-1 may protect the IBD cells, reduce pathological injuries, and reduce the inflammatory response in SAP rats. Its mechanisms may be via inhibiting the expression of MIF and then blocking the activation of p38-MAPK and NF-κB signaling pathway.

Upregulation of ubiquitin-conjugating enzyme E2Z is associated with human hepatocellular carcinoma.

UBE2Z, a member of ubiquitin-conjugating enzymes, has been reported to participate in multiple biological processes. However, its roles in hepatocellular carcinoma (HCC) remain undiscovered. This study aimed at investigating the functions of UBE2Z in HCC. Firstly, we evaluated UBE2Z expression in HCC and identified associations among UBE2Z expression, clinicopathological features, copy number alterations, DNA methylation, and survival of patients using data from the Cancer Genome Atlas (TCGA). As a result, UBE2Z was remarkably overexpressed in HCC tissues relative to normal liver tissues (P < 0.05). High UBE2Z expression was significantly correlated with age, advanced TNM stage, histological grade, vascular invasion, elevated serum alpha-fetoprotein expression (AFP), worse overall survival (OS) and disease-free survival (DFS) of HCC patients (all P < 0.05). Besides, data mining in UCSC Xena Browser showed that UBE2Z DNA amplification which was significantly associated with its expression was common (108 out of 364) in HCC, and that the level of UBE2Z DNA methylation was negatively associated with its expression (Pearson's correlation = -0.4, P < 0.0001). After analyzing the datasets from TCGA, we further confirmed the up-regulation of UBE2Z in 60 HCC tissues and several HCC cell lines. Finally, functional assays were performed and showed that knockdown UBE2Z using small interfering RNA (siRNA) could significantly restrain tumor cell proliferation and suppress cell migration and cell invasion through repressing the expression of MMP2 and MMP9. Meanwhile, UBE2Z knockdown could effectively reduce the expression of p-ERK, p-p38, p-JNK, p-Stat3 and p-JAK2, suggesting that UBE2Z might promote HCC progression by targeting ERK and stat3 signaling pathway. These findings implied that UBE2Z might be considered as a prognostic biomarker in HCC and provided a potential therapeutic tumor-associated antigen for HCC.

Overexpression of PARPBP Correlates with Tumor Progression and Poor Prognosis in Hepatocellular Carcinoma.

BACKGROUND: PARP1-binding protein (PARPBP/PARI/C12orf48), a negative regulator of homologous recombination (HR), has been suggested to function as an oncogene in cervical, lung, and pancreatic cancer. OBJECTIVE: To investigate the expression profile of PARPBP and its role in hepatocellular carcinoma (HCC). METHODS: Using data from the Cancer Genome Atlas and Human Protein Atlas databases, PARPBP expression level and its clinical implication in HCC were identified by t test and Chi-square test. The prognostic value of PARPBP in HCC was evaluated by Kaplan-Meier method, Cox regression analysis, and nomogram. Gene set enrichment analysis (GSEA) was used to screen biological pathways correlated with PARPBP expression in HCC. RESULTS: PARPBP was significantly upregulated in HCC tissues compared with normal liver tissues (P < 0.05). High PARPBP expression was significantly associated with elevated serum AFP level, vascular invasion, poor tumor differentiation, and advanced TNM stage (all P < 0.05). Kaplan-Meier analyses suggested that upregulation of PARPBP was correlated with worse overall survival (OS) and recurrence-free survival (RFS) in HCC. Multivariate analyses further confirmed that PARPBP upregulation was an independent indicator of poor OS and RFS (all P < 0.05). The prognostic nomograms based on PARPBP mRNA expression and TNM stage were superior to those based on the TNM staging system alone (all P < 0.05). Besides, PARPBP DNA copy gain and miR-139-5p downregulation were associated with PARPBP upregulation in HCC. GSEA revealed that "cell cycle," "HR," "DNA replication," and "p53 signaling" pathways were enriched in high PARPBP expression group. CONCLUSION: PARPBP may be a promising prognostic biomarker and candidate therapeutic target in HCC.