Integrating Single-Cell and Spatial Transcriptomics to Uncover and Elucidate GP73-Mediated Pro-Angiogenic Regulatory Networks in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) was characterized as being hypervascular. In the present study, we generated a single-cell spatial transcriptomic landscape of the vasculogenic etiology of HCC and illustrated overexpressed Golgi phosphoprotein 73 (GP73) HCC cells exerting cellular communication with vascular endothelial cells with high pro-angiogenesis potential via multiple receptor-ligand interactions in the process of tumor vascular development. Specifically, we uncovered an interactive GP73-mediated regulatory network coordinated with c-Myc, lactate, Janus kinase 2/signal transducer and activator of transcription 3 (JAK2/STAT3) pathway, and endoplasmic reticulum stress (ERS) signals in HCC cells and elucidated its pro-angiogenic roles in vitro and in vivo. Mechanistically, we found that GP73, the pivotal hub gene, was activated by histone lactylation and c-Myc, which stimulated the phosphorylation of downstream STAT3 by directly binding STAT3 and simultaneously enhancing glucose-regulated protein 78 (GRP78)-induced ERS. STAT3 potentiates GP73-mediated pro-angiogenic functions. Clinically, serum GP73 levels were positively correlated with HCC response to anti-angiogenic regimens and were essential for a prognostic nomogram showing good predictive performance for determining 6-month and 1-year survival in patients with HCC treated with anti-angiogenic therapy. Taken together, the aforementioned data characterized the pro-angiogenic roles and mechanisms of a GP73-mediated network and proved that GP73 is a crucial tumor angiogenesis niche gene with favorable anti-angiogenic potential in the treatment of HCC.

A novel surgical scheme for hepatectomy in hepatocellular carcinoma patients with clinically significant portal hypertension.

OBJECTIVE: Clinically significant portal hypertension (CSPH) seriously affects the feasibility and safety of surgical treatment for hepatocellular carcinoma (HCC) patients. The aim of this study was to establish a new surgical scheme defining risk classification of post-hepatectomy liver failure (PHLF) to facilitate the surgical decision-making and identify suitable candidates for individual hepatectomy among HCC patients with CSPH. BACKGROUNDS: Hepatectomy is the preferred treatment for HCC. Surgeons must maintain a balance between the expected oncological outcomes of HCC removal and short-term risks of severe PHLF and morbidity. CSPH aggravates liver decompensation and increases the risk of severe PHLF thus complicating hepatectomy for HCC. METHODS: Multivariate logistic regression and stochastic forest algorithm were performed, then the independent risk factors of severe PHLF were included in a nomogram to determine the risk of severe PHLF. Further, a conditional inference tree (CTREE) through recursive partitioning analysis validated supplement the misdiagnostic threshold of the nomogram. RESULTS: This study included 924 patients, of whom 137 patients (14.8%) suffered from mild-CSPH and 66 patients suffered from (7.1%) with severe-CSPH confirmed preoperatively. Our data showed that preoperative prolonged prothrombin time, total bilirubin, indocyanine green retention rate at 15 min, CSPH grade, and standard future liver remnant volume were independent predictors of severe PHLF. By incorporating these factors, the nomogram achieved good prediction performance in assessing severe PHLF risk, and its concordance statistic was 0.891, 0.850 and 0.872 in the training cohort, internal validation cohort and external validation cohort, respectively, and good calibration curves were obtained. Moreover, the calculations of total points of diagnostic errors with 95% CI were concentrated in 110.5 (range 76.9-178.5). It showed a low risk of severe PHLF (2.3%), indicating hepatectomy is feasible when the points fall below 76.9, while the risk of severe PHLF is extremely high (93.8%) and hepatectomy should be rigorously restricted at scores over 178.5. Patients with points within the misdiagnosis threshold were further examined using CTREE according to a hierarchic order of factors represented by the presence of CSPH grade, ICG-R15, and sFLR. CONCLUSION: This new surgical scheme established in our study is practical to stratify risk classification in assessing severe PHLF, thereby facilitating surgical decision-making and identifying suitable candidates for individual hepatectomy.

BTLA deficiency promotes HSC activation and protects against hepatic ischemia-reperfusion injury.

BACKGROUND AND AIMS: Hepatic ischemia-reperfusion injury (IRI) is unavoidable even despite the development of more effective surgical approaches. During hepatic IRI, activated HSC (aHSC) are involved in liver injury and recovery. APPROACH AND RESULT: A proportion of aHSC increased significantly both in the mouse liver tissues with IRI and in the primary mouse HSCs and LX-2 cells during hypoxia-reoxygenation. "Loss-of-function" experiments revealed that depleting aHSC with gliotoxin exacerbated liver damage in IRI mice. Subsequently, we found that the transcription of mRNA and the expression of B and T lymphocyte attenuator (BTLA) protein were lower in aHSC compared with quiescent HSCs. Interestingly, overexpression or knockdown of BTLA resulted in opposite changes in the activation of specific markers for HSCs such as collagen type I alpha 1, α-smooth muscle actin, and Vimentin. Moreover, the upregulation of these markers was also observed in the liver tissues of global BLTA-deficient (BTLA-/-) mice and was higher after hepatic IRI. Compared with wild-type mice, aHSC were higher, and liver injury was lower in BTLA-/- mice following IRI. However, the depletion of aHSC reversed these effects. In addition, the depletion of aHSC significantly exacerbated liver damage in BTLA-/- mice with hepatic IRI. Furthermore, the TGF-ß1 signaling pathway was identified as a potential mechanism for BTLA to negatively regulate the activation of HSCs in vivo and in vitro. CONCLUSIONS: These novel findings revealed a critical role of BTLA. Particularly, the receptor inhibits HSC-activated signaling in acute IRI, implying that it is a potential immunotherapeutic target for decreasing the IRI risk.

Research progress of the netrins and their receptors in cancer.

Netrins, a family of secreted and membrane-associated proteins, can regulate axonal guidance, morphogenesis, angiogenesis, cell migration, cell survival, and tumorigenesis. Four secreted netrins (netrin 1, 3, 4 and 5) and two glycosylphosphatidylinositols-anchored membrane proteins, netrin-G1 and G2, have been identified in mammals. Netrins and their receptors can serve as a biomarker and molecular therapeutic target for pathological differentiation, diagnosis and prognosis of malignant cancers. We review here the potential roles of the netrins family and their receptors in cancer.

C-Reactive Protein-Albumin Ratio (CAR): A More Promising Inflammation-Based Prognostic Marker for Patients Undergoing Curative Hepatectomy for Hepatocellular Carcinoma.

Background: Systemic inflammatory response is a hallmark of cancer and plays a significant role in the development and progression of various malignant tumors. This research aimed to estimate the prognostic function of the C-reactive protein-albumin ratio (CAR) in patients undergoing hepatectomy for hepatocellular carcinoma (HCC) and compare it with other inflammation-based prognostic scores, including the neutrophil-lymphocyte ratio, platelet-lymphocyte ratio, monocyte-lymphocyte ratio, systemic immune inflammation index, prognostic index, Glasgow prognostic score, and modified Glasgow prognostic score. Methods: Retrospective analysis was conducted on data from 1039 HCC cases who underwent curative liver resection. The prognostic performance of CAR was compared with other scores using the area under the time-dependent receiver operating characteristic (t-ROC) curve. Multivariable Cox regression analyses were performed to confirm independent predictors for disease-free survival (DFS) and overall survival (OS). Results: The area under the t-ROC curve for CAR in the evaluation of DFS and OS was significantly greater than that of other scores and alpha-fetoprotein (AFP). Patients were stratified based on the optimal cut-off value of CAR, and the data revealed that both DFS and OS were remarkably worse in the high-CAR set compared to the low-CAR set. Multivariable Cox analysis demonstrated that CAR was an independent prognostic parameters for assessing DFS and OS. Regardless of AFP levels, all patients were subsequently divided into significantly different subgroups of DFS and OS based on CAR risk stratification. Similar results were observed when applying CAR risk stratification to other scoring systems. CAR also showed good clinical applicability in patients with different clinical features. Conclusion: CAR is a more effective inflammation-based prognostic marker than other scores and AFP in predicting DFS as well as OS among patients with HCC after curative hepatectomy.

A Case of Curative Treatment with Apatinib and Camrelizumab Following Liver Resection for Advanced Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC), a highly malignant digestive system tumor, poses substantial challenges due to its intricate underlying causes and pronounced post-surgery recurrence. Consequently, the prognosis for HCC remains notably unfavorable. The endorsement of sorafenib and PD-L1 inhibitors for HCC signifies the onset of a new era embracing immunotherapy and targeted treatment approaches for this condition. Hence, comprehending the mechanisms underpinning targeted immune combination therapy has become exceedingly vital for the prospective management of HCC patients. This article initially presents a triumphant instance of curative treatment involving the combination of TKI and PD-1 inhibitor subsequent to liver resection, targeting an advanced stage HCC as classified by the BCLC staging system. The case patient carries a decade-long history of hepatitis B, having undergone a regimen of 20 courses of treatments involving apatinib and camrelizumab. Throughout the treatment period, no occurrences of grade 3 or 4 adverse events (AE) were noted. Subsequently, the patient underwent a left hepatectomy. Following the hepatectomy, their serum AFP levels have consistently remained within normal limits, and CT imaging has indicated the absence of tumor recurrence over a span of 36 months. The patient had been reviewed on time for two years after the operation. The last time a CT was performed for this patient in our hospital was 7 May 2021, and no new tumors were found. Follow-up is still ongoing. When applying combined targeted immune transformation therapy using TKI and ICI for a patient with BCLC advanced stage HCC, apatinib treatment serves a dual purpose. It inhibits the survival and angiogenesis of tumor cells, while also enhancing the efficacy of camrelizumab in obstructing the interaction between PD-1 and PD-L1. This restoration of T cell cytotoxicity subsequently facilitates the elimination of tumor cells, leading to an enhanced anticancer effect.

Indocyanine Green Retention Test as a Predictor of Postoperative Complications in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma.

Background: Accurate preoperative estimation of liver function reserve is the key to the safety of hepatectomy. Recently, indocyanine green retention test at 15 minutes (ICG-R15) has been widely used to estimate hepatic function reserve in different liver diseases. The purpose of this research was to investigate the clinical value of ICG-R15 in predicting postoperative major complications and severe posthepatectomy liver failure (PHLF) in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) subjected to hepatectomy. Methods: A total of 354 HBV-associated HCC patients who underwent hepatectomy were enrolled. The Child-Pugh, model for end-stage liver disease (MELD), albumin-bilirubin (ALBI) and ICG-R15 for assessing postoperative complications risk were compared using receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Results: Postoperative major complications developed in 32 patients (9.1%) and severe PHLF developed in 57 (16.1%) patients. Multivariate analyses revealed that ICG-R15 were independent factors for predicting postoperative major complications and severe PHLF. ROC curve analyses and DCA plots showed that the predictive abilities of ICG-R15 for postoperative major complications and severe PHLF risk was significantly greater than Child-Pugh, MELD, and ALBI scores. Similar results were obtained by stratifying different background subgroups. Then, patients were divided into three different risk cohorts, emphasizing the significantly discrepancy between the incidence of postoperative major complications and severe PHLF. Conclusion: Compared with Child-Pugh, MELD and ALBI scores, ICG-R15 revealed significantly advantages in predicting postoperative major complications and severe PHLF in HBV-related HCC patients subjected to liver resection.

Developmental artificial neural network model to evaluate the preoperative safe limit of future liver remnant volume for HCC combined with clinically significant portal hypertension.

Background & aims: Finding a way to comprehensively integrate the presence and grade of clinically significant portal hypertension, amount of preserved liver function and extent of hepatectomy into the guidelines for choosing appropriate candidates to hepatectomy remained challenging. This study sheds light on these issues to facilitate precise surgical decisions for clinicians. Methods: Independent risk factors associated with grade B/C post-hepatectomy liver failure were identified by stochastic forest algorithm and logistic regression in hepatitis B virus-related hepatocellular carcinoma patients. Results: The artificial neural network model was generated by integrating preoperative pre-ALB, prothrombin time, total bilirubin, AST, indocyanine green retention rate at 15 min, standard future liver remnant volume and clinically significant portal hypertension grade. In addition, stratification of patients into three risk groups emphasized significant distinctions in the risk of grade B/C post-hepatectomy liver failure. Conclusion: The authors' artificial neural network model could provide a reasonable therapeutic option for clinicians to select optimal candidates with clinically significant portal hypertension for hepatectomy and supplement the hepatocellular carcinoma surgical treatment algorithm.

Hepatectomy involves removing the tumor from the liver and is considered the most effective treatment for hepatocellular carcinoma (HCC). Clinically significant portal hypertension is characterized by the presence of gastric and/or esophageal varices and a platelet count <100 × 10⁹/l with the presence of splenomegaly, which would aggravate the risk of post-hepatectomy liver failure, and is therefore regarded as a contraindication to hepatectomy. Over the past few decades, with improvement in surgical techniques and perioperative care, the morbidity of postoperative complications and mortality have decreased greatly. Current HCC guidelines recommend the expansion of hepatectomy to HCC patients with clinically significant portal hypertension. However, determining how to select optimal candidates for hepatectomy remains challenging. The authors' artificial neural network is a mathematical tool developed by simulating the properties of neurons with large-scale information distribution and parallel structure. Here the authors retrospectively enrolled 871 hepatitis B virus-related HCC patients and developed an artificial neural network model to predict the risk of post-hepatectomy liver failure, which could provide a reasonable therapeutic option and facilitate precise surgical decisions for clinicians.

Preoperative fibrinogen-to-albumin ratio predicts the prognosis of patients with hepatocellular carcinoma subjected to hepatectomy.

BACKGROUND: Systemic inflammatory response (SIR) plays a crucial role in every step of tumorigenesis and development. More recently, the fibrinogen-to-albumin ratio (FAR), an inflammation-based model, was suggested as a prognostic maker for various cancer patients. This research aimed to estimate the prognostic abilities of FAR, neutrophil-lymphocyte ratio (NLR), monocyte-lymphocyte ratio (MLR), platelet- lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) in patients with hepatocellular carcinoma (HCC) subjected to curative hepatectomy. METHODS: A total of 1,502 cases who underwent hepatectomy for HCC were included. The predictive performances of FAR, NLR, MLR, PLR and SII were assessed with regards to overall survival (OS) and disease-free survival (DFS). The area under the time-dependent receiver operating characteristic curve was used to compare prognostic performances. RESULTS: Data revealed that FAR had higher predictive accuracy than other inflammation-based models and alpha-fetoprotein (AFP) in assessing OS and DFS. Indeed, the OS and DFS of patients with high FAR (> 8.9), differentiated by the optimal cut-off value of FAR, were remarkably reduced (p < 0.05 for OS and DFS). Multivariate Cox regression analyses identified that AFP, FAR, clinically significant portal hypertension, tumor size, Barcelona Clinical Liver Cancer staging system, major resection and blood loss were independent indicators for predicting OS and DFS. Furthermore, these patients could be classified according to their FAR into significantly different subgroups, regardless of AFP levels (p < 0.05 for DFS and OS). Similar results were obtained in other inflammation-based prognostic models. CONCLUSIONS: Compared with NLR, MLR, PLR, SII and AFP, FAR showed significant advantages in predicting survival of HCC patients subjected to liver resection.

Aggravation of hepatic ischemia­reperfusion injury with increased inflammatory cell infiltration is associated with the TGF­ß/Smad3 signaling pathway.

Ischemia­reperfusion (IR) injury is a major challenge influencing the outcomes of hepatic transplantation. Transforming growth factor­ß (TGF­ß) and its downstream gene, SMAD family member 3 (Smad3), have been implicated in the pathogenesis of chronic hepatic injuries, such as hepatic fibrosis. Thus, the present study aimed to investigate the role of the TGF­ß/Smad3 signaling pathway on hepatic injury induced by IR in vivo. In total, 20 129S2/SvPasCrl wild­type (WT) mice were randomized into two groups; 10 mice underwent IR injury surgery and 10 mice were sham­operated. Histopathological changes in liver tissues and serum levels of alanine aminotransferase (ALT) were examined to confirm hepatic injury caused by IR surgery. The expression levels of TGF­ß1, Smad3 and phosphorylated­Smad3 (p­Smad3) were detected via western blotting. Furthermore, a total of five Smad3­/­ 129S2/SvPasCrl mice (Smad3­/­ mice) and 10 Smad3+/+ littermates received IR surgery, while another five Smad3­/­ mice and 10 Smad3+/+ littermates received the sham operation. Histopathological changes in liver tissues and serum levels of ALT were then compared between the groups. Furthermore, hepatic apoptosis and inflammatory cell infiltration after IR were evaluated in the liver tissues of Smad3­/­ mice and Smad3+/+ mice. The results demonstrated that the expression levels of TGF­ß1, Smad3 and p­Smad3 were elevated in hepatic tissue from WT mice after IR injury. Aggravated hepatic injury, increased apoptosis and enhanced inflammatory cell infiltration induced by hepatic IR injury were observed in the Smad3­/­ mice compared with in Smad3+/+ mice. Collectively, the current findings suggested that activation of the TGF­ß/Smad3 signaling pathway was present alongside the hepatic injury induced by IR. However, the TGF­ß/Smad3 signaling pathway may have an effect on protecting against liver tissue damage caused by IR injury in vivo.

Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion.

BACKGROUND: The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperative adjuvant treatment and monitoring. This study aimed to develop and validate an artificial neural network (ANN) model to predict PHER in HCC patients without macroscopic vascular invasion. METHODS: Nine hundred and three patients who underwent curative liver resection for HCC participated in this study. They were randomly divided into derivation (n = 679) and validation (n = 224) cohorts. The ANN model was developed in the derivation cohort and subsequently verified in the validation cohort. RESULTS: PHER morbidity in the derivation and validation cohorts was 34.8 and 39.2%, respectively. A multivariable analysis revealed that hepatitis B virus deoxyribonucleic acid load, γ-glutamyl transpeptidase level, α-fetoprotein level, tumor size, tumor differentiation, microvascular invasion, satellite nodules, and blood loss were significantly associated with PHER. These factors were incorporated into an ANN model, which displayed greater discriminatory abilities than a Cox's proportional hazards model, preexisting recurrence models, and commonly used staging systems for predicting PHER. The recurrence-free survival curves were significantly different between patients that had been stratified into two risk groups. CONCLUSION: When compared to other models and staging systems, the ANN model has a significant advantage in predicting PHER for HCC patients without macroscopic vascular invasion.

Combining Aspartate Aminotransferase-to-Platelet Ratio Index with Future Liver Remnant to Assess Preoperative Hepatic Functional Reserve in Patients with Hepatocellular Carcinoma.

BACKGROUND: Accurate preoperative assessment of hepatic functional reserve is essential for conducting a safe hepatectomy. In recent years, aspartate aminotransferase-to-platelet ratio index (APRI) has been used as a noninvasive model for assessing fibrosis stage, hepatic functional reserve, and prognosis after hepatectomy with a high level of accuracy. The purpose of this research was to evaluate the clinical value of combining APRI with standardized future liver remnant (sFLR) for predicting severe post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC). METHODS: Six hundred thirty-seven HCC patients who had undergone hepatectomy were enrolled in this study. The performance of the Child-Pugh (CP) grade, model for end-stage liver disease (MELD), APRI, sFLR, and APRI-sFLR in predicting severe PHLF was assessed using the area under the ROC curve (AUC). RESULTS: Severe PHLF was found to have developed in 101 (15.9%) patients. Multivariate logistic analyses identified that prealbumin, cirrhosis, APRI score, sFLR, and major resection were significantly associated with severe PHLF. The AUC values of the CP, MELD, APRI, and sFLR were 0.626, 0.604, 0.725, and 0.787, respectively, indicating that the APRI and sFLR showed significantly greater discriminatory abilities than CP and MELD (P < 0.05 for all). After APRI was combined with sFLR, the AUC value of APRI-sFLR for severe PHLF was 0.816, which greatly improved the prediction accuracy, compared with APRI or sFLR alone (P < 0.05 for all). Stratified analysis using the status of cirrhosis and extent of resection yielded similar results. Moreover, the incidence and grade of PHLF were significantly different among the three risk groups. CONCLUSION: The combination of APRI and sFLR can be considered to be a predictive factor with increased accuracy for severe PHLF in HCC patients, compared with CP grade, MELD, APRI, or sFLR alone.

Nomogram for prediction of the international study Group of Liver Surgery (ISGLS) grade B/C Posthepatectomy liver failure in HBV-related hepatocellular carcinoma patients: an external validation and prospective application study.

BACKGROUND: To develop a nomogram for predicting the International Study Group of Liver Surgery (ISGLS) grade B/C posthepatectomy liver failure (PHLF) in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) patients. METHODS: Patients initially treated with hepatectomy were included. Univariate regression analysis and stochastic forest algorithm were applied to extract the core indicators and reduce redundancy bias. The nomogram was then constructed by using multivariate logistic regression, and validated in internal and external cohorts, and a prospective clinical application. RESULTS: There were 900, 300 and 387 participants in training, internal and external validation cohorts, with the morbidity of grade B/C PHLF were 13.5, 11.0 and 20.2%, respectively. The nomogram was generated by integrating preoperative total bilirubin, platelet count, prealbumin, aspartate aminotransferase, prothrombin time and standard future liver remnant volume, then achieved good prediction performance in training (AUC = 0.868, 95%CI = 0.836-0.900), internal validation (AUC = 0.868, 95%CI = 0.811-0.926) and external validation cohorts (AUC = 0.820, 95%CI = 0.756-0.861), with well-fitted calibration curves. Negative predictive values were significantly higher than positive predictive values in training cohort (97.6% vs. 33.0%), internal validation cohort (97.4% vs. 25.9%) and external validation cohort (94.3% vs. 41.1%), respectively. Patients who had a nomogram score < 169 or ≧169 were considered to have low or high risk of grade B/C PHLF. Prospective application of the nomogram accurately predicted grade B/C PHLF in clinical practise. CONCLUSIONS: The nomogram has a good performance in predicting ISGLS grade B/C PHLF in HBV-related HCC patients and determining appropriate candidates for hepatectomy.

Identification and validation of a ten-gene set variation score as a diagnostic and prognostic stratification tool in hepatocellular carcinoma.

We aimed to identify a hepatocellular carcinoma (HCC)-specific gene set during progression. Using the HCC data set from The Cancer Genome Atlas, we found that 10 genes were gradually upregulated with the progression of HCC and associated with survival, classified as HCC-unfavorable genes; 29 genes were gradually downregulated and associated with survival, classified as HCC-favorable genes. Gene set variation analysis (GSVA) was used to score individual samples against the two gene sets. Receiver operating characteristic (ROC) curve analysis showed that both the HCC-unfavorable GSVA score and HCC-favorable GSVA score were reliable biomarkers for diagnosing HCC. Moreover, tROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated that the HCC-unfavorable GSVA score was an independent prognostic biomarker. The results were validated in an external independent data set. Our results support a ten-gene set variation score as a diagnostic and predictive strategy tool in HCC.

Artificial Neural Network Model for Liver Cirrhosis Diagnosis in Patients with Hepatitis B Virus-Related Hepatocellular Carcinoma.

BACKGROUND: Testing for the presence of liver cirrhosis (LC) is one of the most critical diagnostic and prognostic assessments for patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). More non-invasive tools are needed to diagnose LC but the predictive abilities of current models are still inconclusive. This study aimed to develop and validate a novel and non-invasive artificial neural network (ANN) model for diagnosing LC in patients with HBV-related HCC using routine laboratory serological indicators. METHODS: A total of 1152 HBV-related HCC patients who underwent hepatectomy were included and randomly divided into the training set (n = 864, 75%) and validation set (n = 288, 25%). The ANN model was constructed from the training set using multivariate Logistic regression analysis and then verified in the validation set. RESULTS: The morbidity of LC in the training and validation sets was 41.2% and 46.8%, respectively. Multivariate analysis showed that age, platelet count, prothrombin time and total bilirubin were independent risk factors for LC (P < 0.05). The area under the ROC curve (AUC) analyses revealed that the ANN model had higher predictive accuracy than the Logistic model (ANN: 0.757 vs Logistic: 0.721; P < 0.001), and other scoring systems (ANN: 0.757 vs CP: 0.532, MELD: 0.594, ALBI: 0.575, APRI: 0.621, FIB-4: 0.644, AAR: 0.491, and GPR: 0.604; P < 0.05 for all) in diagnosing LC. Similar results were obtained in the validation set. CONCLUSION: The ANN model has better diagnostic capabilities than other commonly used models and scoring systems in assessing LC risk in patients with HBV-related HCC.

Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma.

BACKGROUND: Posthepatectomy liver failure is a worrisome complication after major hepatectomy for hepatocellular carcinoma and is the leading cause of postoperative mortality. Recommendations for hepatectomy for hepatocellular carcinoma are based on the risk of severe posthepatectomy liver failure, and accurately predicting posthepatectomy liver failure risk before undertaking major hepatectomy is of great significance. Thus, herein, we aimed to establish and validate an artificial neural network model to predict severe posthepatectomy liver failure in patients with hepatocellular carcinoma who underwent hemihepatectomy. METHODS: Three hundred and fifty-three patients who underwent hemihepatectomy for hepatocellular carcinoma were included. We randomly divided the patients into a development set (n = 265, 75%) and a validation set (n = 88, 25%). Multivariate logistic analysis facilitated identification of independent variables that we incorporated into the artificial neural network model to predict severe posthepatectomy liver failure in the development set and then verified in the validation set. RESULTS: The morbidity of patients with severe posthepatectomy liver failure in the development and validation sets was 24.9% and 23.9%, respectively. Multivariate analysis revealed that platelet count, prothrombin time, total bilirubin, aspartate aminotransferase, and standardized future liver remnant were all significant predictors of severe posthepatectomy liver failure. Incorporating these factors, the artificial neural network model showed satisfactory area under the receiver operating characteristic curve for the development set of 0.880 (95% confidence interval, 0.836-0.925) and for the validation set of 0.876 (95% confidence interval, 0.801-0.950) in predicting severe posthepatectomy liver failure and achieved well-fitted calibration ability. The predictive performance of the artificial neural network model for severe posthepatectomy liver failure outperformed the traditional logistic regression model and commonly used scoring systems. Moreover, stratification into 3 risk groups highlighted significant differences between the incidences and grades of posthepatectomy liver failure. CONCLUSION: The artificial neural network model accurately predicted the risk of severe posthepatectomy liver failure in patients with hepatocellular carcinoma who underwent hemihepatectomy. Our artificial neural network model might help surgeons identify intermediate and high-risk patients to facilitate earlier interventions.

Construction of a Comprehensive Multiomics Map of Hepatocellular Carcinoma and Screening of Possible Driver Genes.

Objectives: The occurrence of hepatocellular carcinoma (HCC) is a complex process involving genetic mutations, epigenetic variation, and abnormal gene expression. However, a comprehensive multiomics investigation of HCC is lacking, and the available multiomics evidence has not led to improvements in clinical practice. Therefore, we explored the molecular mechanism underlying the development of HCC through an integrative analysis of multiomics data obtained at multiple levels to provide innovative perspectives and a new theoretical basis for the early diagnosis, personalized treatment and medical guidance of HCC. Methods: In this study, we collected whole-exome sequencing data, RNA (mRNA and miRNA) sequencing data, DNA methylation array data, and single nucleotide polymorphism (SNP) array data from The Cancer Genome Atlas (TCGA). We analyzed the copy number variation (CNV) in HCC using GISTIC2. MutSigCV was applied to identify significantly mutated genes (SMGs). Functional enrichment analyses were performed using the clusterProfiler package in R software. The prognostic values of discrete variables were estimated using Kaplan-Meier survival curves. Results: By analyzing the HCC data in TCGA, we constructed a comprehensive multiomics map of HCC. Through copy number analysis, we identified significant amplification at 29 loci and significant deletions at 33 loci. A total of 13 significant mutant genes were identified. In addition, we also identified three HCC-related mutant signatures, and among these, signature 22 was closely related to exposure to aristolochic acids. Subsequently, we analyzed the methylation level of HCC samples and identified 51 epigenetically silenced genes that were significantly associated with methylation. The differential expression analysis identified differentially expressed mRNAs and miRNAs in HCC samples. Based on the above-described results, we identified a total of 93 possible HCC driver genes, which are driven by mutations, methylation, and CNVs and have prognostic value. Conclusion: Our study reveals variations in different dimensions of HCC. We performed an integrative analysis of genomic signatures, single nucleotide variants (SNVs), CNVs, methylation, and gene expression in HCC. Based on the results, we identified HCC possible driver genes that might facilitate prognostic prediction and support decision making with regard to the choice of therapy.

Comprehensive analysis of biological networks and the eukaryotic initiation factor 4A-3 gene as pivotal in hepatocellular carcinoma.

Eukaryotic initiation factor 4A-3 (EIF4A3) is a core component of the exon junction complex (EJC). Abnormalities in EIF4A3 are associated with carcinogenesis. The present study aimed to determine the biological role of EIF4A3 in hepatocellular carcinoma (HCC). Our study is based on the analysis of HCC sequencing data from public databases. We first used the Gene Expression Profiling Interactive Analysis tool and ONCOMINE to analyze the EIF4A3 expression, and the results were validated in human clinical tissues by a quantitative real-time polymerase chain reaction, Western blot, and immunohistochemical. Then, we used cBioPortal to identify EIF4A3 alterations and function networks. Finally, we created a network of genes that were positively correlated with EIF4A3 using LinkedOmics, and analyzed this network using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. For the genes identified, we also analyzed the relevant kinase and transcription factor target networks as well as the protein-protein interaction networks. Our results show that EIF4A3 was overexpressed in HCC tissues in comparison with normal tissues, and high EIF4A3 expression was significantly associated with poor prognosis. Analysis of the functional networks of genes that were co-occurring with EIF4A3 amplification revealed connections with several chemokine signaling pathways. Furthermore, genes that positively correlated with EIF4A3 were mainly related to cell cycle and spliceosome pathways, several cell cycle regulatory kinases, and tumor-associated transcription factors. Finally, crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) data showed that EIF4A3 protein binds to multiple exon regions of the cell cycle regulatory genes cyclin-dependent kinases 1 and 2 and transcription factor E2F1. Our study unveils potential biological networks in HCC and the pivotal role of EIF4A3 as a bridging protein, highlighting the need for an in-depth study of EIF4A3 in carcinogenesis.

Circulating tumor cells and CXCR4 in the prognosis of hepatocellular carcinoma.

BACKGROUND: This study was to determine circulating tumor cells (CTCs) and the expression of CXC chemokine receptor type 4 (CXCR4) in primary hepatocellular carcinoma (HCC) and the relationships with prognosis. METHODS: We used an advanced CanPatrolTM CTC-enrichment technique to collect CTCs for isolation and characterization from blood samples. The RNA in situ hybridization (RNA-ISH) method, which is based on branched DNA (bDNA) signal amplification technology, was used to determine the expression of CXCR4 according to epithelial-mesenchymal transition (EMT) markers in 99 patients with primary liver cancer in blood samples pre-operatively. The relationship between the EMT markers and HCC was determined. RESULTS: The positive rates of CTCs and CXCR4 were 89.9% and 58.8%, respectively. CTCs were positively correlated with the Barcelona clinic liver cancer (BCLC) staging, tumor diameter and number, envelope, microsatellite damage, portal vein thrombosis, alpha-fetoprotein (AFP), and hepatitis B DNA, and negatively correlated with Edmondson grade. There were significant differences in the expression of CXCR4 between interstitial CTCs and mixed CTCs. A total of 99 patients underwent CTCs testing prior to surgery. The tumor-free survival time of HCC patients with interstitial CTCs <1 (13.3 months) was significantly longer than patients with interstitial CTCs ≥1 (5.0 months) pre-operatively. CONCLUSIONS: CTC-positivity was shown to be associated with HCC and can be used as an independent prognostic factor for HCC. High CXCR4 protein expression was more common in mixed CTCs.

Establishment of a prognostic model for predicting short-term disease-free survival in cases of hepatitis B-related hepatocellular carcinoma with the TP53 249Ser mutation in southern China.

BACKGROUND: Hepatitis B virus (HBV) infection and dietary aflatoxin exposure are two major and synergistic carcinogenic factors of hepatocellular carcinoma (HCC) in southern China. Mutation of the TP53 gene at codon 249 (TP53 249Ser) is recognized as a fingerprint of aflatoxin B1 (AFB1) exposure. METHODS: A total of 485 HCC patients positive for serum hepatitis B surface antigen were enrolled. The clinicopathological information and survival time were collected. TP53 249Ser mutations in HCC were detected by Sanger DNA sequencing after PCR amplification. Immunohistochemical staining was used to evaluate TP53 expression. Propensity score matching (PSM) and Cox proportional hazards regression (CPHR) were conducted to identify independent risk factors for prognosis that were incorporated into the nomogram. Univariate logistic regression analysis was used to compare differences in clinical factors between the TP53 249Ser mutation group and the non-mutation group. A Kaplan-Meier plot, univariate and multivariate Cox proportional hazards models were used to assess the association between clinicopathological characteristics and survival outcomes. RESULTS: After PSM, a total of 322 cases were included in the analysis of clinical prognosis. Results of CPHR showed that the mutation group had a relatively higher risk of tumor recurrence within 2 years after undergoing hepatectomy (P=0.039, HR =1.47, 95% CI: 1.02-2.18). The prognostic model performed better in terms of 2-year DFS prediction than BCLC stage. Patients who had a nomogram score of more than 160 were considered to have a higher risk of recurrence within 2 years. CONCLUSIONS: Our study found that the TP53 249Ser mutation may be a high risk factor of HBV-related HCC recurrence in the short term. And we initially established a nomogram scoring system for predicting 2-year recurrence in HBV-related HCC patients in southern China.