Textbook outcome of laparoscopic hepatectomy in the context of precision surgery: A single center experience.

OBJECTIVE: Laparoscopic hepatectomy (LH) has become a common surgery for the treatment of liver tumor. To evaluate the surgical quality of laparoscopic hepatectomy under the context of precision surgery with Textbook outcome (TO), a comprehensive and holistic assessment approach. METHODS: A total of 1056 patients who underwent laparoscopic hepatectomy from May 2016 and December 2022 were enrolled in the study. All the patients were performed hepatectomy. The rate of TO and factors associated with achieving TO were examined. RESULTS: Among the 1056 patients, 75 % patients achieved TO. The main reason limited patients achieving textbook outcomes was prolonged length of hospital stay (LOS). The univariate analysis indicated that age>65, ASA classification ≥3, liver cirrhosis, tumor size > 3 cm, tumor number ≥2, type of primary cancer, and IWATE DSS were significantly associated with non-achievement of TO. The multivariate analysis indicated that the ASA classification ≥3 and advanced difficulty level in IWATE DSS independent factors associated with achieving TO. Reaching TO can significantly prolong the postoperative recurrence time and overall survival time of hepatocellular carcinoma patients. CONCLUSION: In the context of precision surgery, 75 % patients undergoing laparoscopic hepatectomy achieved a TO. Patients who achieved TO had significantly improved survival.

Pancreatobiliary reflux increases macrophage-secreted IL-8 and activates the PI3K/NFκB pathway to promote cholangiocarcinoma progression.

BACKGROUND: Persistent pancreaticobiliary reflux (PBR) is associated with a high risk of biliary malignancy. This study aimed to evaluate the proportion of PBR in biliary tract diseases and mechanisms by which PBR promoted cholangiocarcinoma progression. METHODS: Overall 227 consecutive patients with primary biliary tract disease participated in this study. The amylase levels in the collected bile were analyzed. The mechanisms underlying the effect of high-amylase bile on bile duct epithelial and cholangiocarcinoma cells progression were analyzed. The source of interleukin-8 (IL-8) and its effects on the biological functions of cholangiocarcinoma cells were investigated. RESULTS: The bile amylase levels in 148 of 227 patients were higher than the upper serum amylase limit of 135 IU/L. PBR was significantly correlated with sex, pyrexia, and serum gamma-glutamyl transferase (GGT) levels in the patient cohort. High-amylase bile-induced DNA damage and genetic differences in the transcript levels of the gallbladder mucosa and facilitated the proliferation and migration of bile duct cancer cells (HUCCT1 and QBC939 cells). The concentration of many cytokines increased in high-amylase bile. IL-8 is secreted primarily by macrophages via the mitogen-activated protein kinase pathway and partially by bile duct epithelial cells. IL-8 promotes the progression of HUCCT1 and QBC939 cells by regulating the expression of epithelial-mesenchymal transition-associated proteins and activating the phosphatidylinositol 3-kinase/nuclear factor kappa-B pathway. CONCLUSIONS: PBR is one of the primary causes of biliary disease. IL-8 secreted by macrophages or bile duct epithelial cells stimulated by high-amylase bile promotes cholangiocarcinoma progression.

Machine Learning Model Based on the Neutrophil-to-Eosinophil Ratio Predicts the Recurrence of Hepatocellular Carcinoma After Surgery.

Background: Circulating eosinophils are associated with tumor development. An eosinophil-related index, the neutrophil to eosinophil ratio (NER), can be used to predict the prognosis of patients with tumors. However, there is still a lack of efficient prognostic biomarkers for HCC. In this study, we aimed to investigate the predictive value of the NER and develop an optimal machine learning model for the recurrence of HCC patients. Patients and methods: A retrospective collection of 562 patients who underwent hepatectomy with a pathologic diagnosis of HCC was performed. The relationship between NER and progression-free survival (PFS) was investigated. We developed a new machine learning framework with 10 machine learning algorithms and their 101 combinations to select the best model for predicting recurrence after hepatectomy. The performance of the model was assessed by the area under the curve (AUC) of characteristics and calibration curves, and clinical utility was evaluated by decision curve analysis (DCA). Results: Kaplan‒Meier curves showed that the PFS in the low NER group was significantly better than that in the high NER group. Multivariate Cox regression analysis showed that NER was an independent risk factor for recurrence after surgery. The random survival forests (RSF) model was selected as the best model that had good predictive efficacy and outperformed the TNM, BCLC, and CNLC staging systems. Conclusion: The NER has good predictive value for postoperative recurrence in patients with hepatocellular carcinoma. Machine learning model based on NER can be used for accurate predictions.

Disulfidptosis-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in colon adenocarcinoma.

BACKGROUND: Disulfidptosis is independent of apoptosis, ferroptosis, and cuproptosis and is associated with cancer progression, treatment response, and prognosis. However, the predictive potential of disulfidptosis-associated lncRNAs in colon adenocarcinoma (COAD) and their features in the tumor immune microenvironment (TIME) require further elucidation. METHODS: RNA transcriptome, clinical information, and mutation data of COAD samples were obtained from the TCGA database. The risk model was first constructed by co-expression analysis of disulfidptosis genes and lncRNAs, and prognostic lncRNAs were screened using Cox regression, followed by least absolute shrinkage and selection operator analysis. Enrichment analyses were performed to explore the underlying biological functions and signaling of model-associated differentially expressed genes (MADEGs). Moreover, TIME of MADEGs was analyzed to assess the immunotherapy. Finally, the expression levels of the lncRNAs were verified by taking specimens of patients with COAD from the Affiliated Hospital of Qingdao University. RESULTS: We constructed a prognosis-related risk model based on four disulfidptosis-associated lncRNAs (ZEB1-AS1, SNHG16, SATB2-AS1, and ALMS1-IT1). By analyzing the survival of patients in the whole, training, and test groups, we found that patients with COAD in the low-risk group had better overall survival than those in the high-risk group. Validation of the model via Cox analysis and clinical indicators demonstrated that the model had a decent potential for predicting the prognosis of patients with COAD. Enrichment analyses revealed that the MADEGs were related to disulfidptosis-associated biological functions and cancer pathways. Furthermore, patients with COAD in the high-risk group had more positive responses to immune checkpoint inhibitors (ICIs) than those in the low-risk group, as confirmed by TIME analysis. ZEB1-AS1, SNHG16, and ALMS1-IT1 were expressed at higher levels in tumor samples than those in the corresponding paracancerous samples (p < 0.05), whereas SATB2-AS1 was upregulated in the paracancerous samples (p < 0.05). CONCLUSIONS: This signature may guide prognosis, molecular mechanisms, and treatment strategies, including ICIs and chemotherapy, in patients with COAD.

Machine learning-based radiomics analysis of preoperative functional liver reserve with MRI and CT image.

OBJECTIVE: The indocyanine green retention rate at 15 min (ICG-R15) is a useful tool to evaluate the functional liver reserve before hepatectomy for liver cancer. Taking ICG-R15 as criteria, we investigated the ability of a machine learning (ML)-based radiomics model produced by Gd-EOB-DTPA-enhanced hepatic magnetic resonance imaging (MRI) or contrast-enhanced computed tomography (CT) image in evaluating functional liver reserve of hepatocellular carcinoma (HCC) patients. METHODS: A total of 190 HCC patients with CT, among whom 112 also with MR, were retrospectively enrolled and randomly classified into a training dataset (CT: n = 133, MR: n = 78) and a test dataset (CT: n = 57, MR: n = 34). Then, radiomics features from Gd-EOB-DTPA MRI and CT images were extracted. The features associated with the ICG-R15 classification were selected. Five ML classifiers were used for the ML-model investigation. The accuracy (ACC) and the area under curve (AUC) of receiver operating characteristic (ROC) with 95% confidence intervals (CI) were utilized for ML-model performance evaluation. RESULTS: A total of 107 different radiomics features were extracted from MRI and CT, respectively. The features related to ICG-R15 which was classified into 10%, 20% and 30% were selected. In MRI groups, classifier XGBoost performed best with its AUC = 0.917 and ACC = 0.882 when the threshold was set as ICG-R15 = 10%. When ICG-R15 = 20%, classifier Random Forest performed best with AUC = 0.979 and ACC = 0.882. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.961 and ACC = 0.941. For CT groups, the classifier XGBoost performed best when ICG-R15 = 10% with AUC = 0.822 and ACC = 0.842. When ICG-R15 = 20%, classifier SVM performed best with AUC = 0.860 and ACC = 0.842. When ICG-R15 = 30%, classifier XGBoost performed best with AUC = 0.938 and ACC = 0.965. CONCLUSIONS: Both the MRI- and CT-based machine learning models are proved to be valuable noninvasive methods for functional liver reserve evaluation.

Prognostic analysis of hepatocellular carcinoma with macrovascular invasion after liver resection and a successful case of conversion therapy.

Objective: Macrovascular invasion (MVI) is an important factor leading to poor prognosis in hepatocellular carcinoma (HCC). Liver resection may offer favorable prognosis for selected patients with HCC. This study aimed to analyze the prognostic factors of HCC with MVI after liver resection as well as demonstrate a case of conversion therapy in an HCC patient with portal vein tumor thrombus (PVTT). Methods: A total of 168 HCC patients with MVI who underwent primary liver resection at the Affiliated Hospital of Qingdao University between January 2013 and October 2021 were enrolled in the study. Clinicopathological data were collected retrospectively. Univariate and multivariate regression analyses were used to investigate the risk factors influencing recurrence and overall survival. Additionally, conversion therapy with drug-eluting bead transarterial chemoembolization (D-TACE), and sorafenib plus sintilimab treatment was performed in an HCC patient with PVTT. Results: Among the 168 patients with HCC, 11 were diagnosed with hepatic vein tumor thrombosis, and the rest were diagnosed with PVTT. The 1-year disease-free survival rate was 37.5%, and the 3-year overall survival rate was 52.7%. Univariate and multivariate regression analyses revealed that HBsAg positivity, alpha-fetoprotein (AFP) level ≥400 ng/ml, liver capsule invasion, and tumor number ≥2 were independent prognostic factors for tumor recurrence, whereas HBsAg positivity was an independent risk factor for overall survival. Postoperative prophylactic medication did not significantly prolong the recurrence time. The median survival time (MST) after tumor recurrence was 13.4 months. In the patient treated with conversion therapy, the tumor gradually shrank and was eventually surgically resected. Conclusions: This study identified the independent prognostic and risk factors associated with recurrence and overall survival in HCC patients with MVI. Additionally, we successfully performed conversion therapy in an HCC patient with PVTT. The findings would help identify patients at high risk of recurrence and indicate that combined therapy may prolong the survival of HCC patients with PVTT.

Ensemble learning based on efficient features combination can predict the outcome of recurrence-free survival in patients with hepatocellular carcinoma within three years after surgery.

Preoperative prediction of recurrence outcome in hepatocellular carcinoma (HCC) facilitates physicians' clinical decision-making. Preoperative imaging and related clinical baseline data of patients are valuable for evaluating prognosis. With the widespread application of machine learning techniques, the present study proposed the ensemble learning method based on efficient feature representations to predict recurrence outcomes within three years after surgery. Radiomics features during arterial phase (AP) and clinical data were selected for training the ensemble models. In order to improve the efficiency of the process, the lesion area was automatically segmented by 3D U-Net. It was found that the mIoU of the segmentation model was 0.8874, and the Light Gradient Boosting Machine (LightGBM) was the most superior, with an average accuracy of 0.7600, a recall of 0.7673, a F1 score of 0.7553, and an AUC of 0.8338 when inputting radiomics features during AP and clinical baseline indicators. Studies have shown that the proposed strategy can relatively accurately predict the recurrence outcome within three years, which is helpful for physicians to evaluate individual patients before surgery.

The TTYH3/MK5 Positive Feedback Loop regulates Tumor Progression via GSK3-ß/ß-catenin signaling in HCC.

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and identification of novel targets is necessary for its diagnosis and treatment. This study aimed to investigate the biological function and clinical significance of tweety homolog 3 (TTYH3) in HCC. TTYH3 overexpression promoted cell proliferation, migration, and invasion and inhibited HCCM3 and Hep3B cell apoptosis. TTYH3 promoted tumor formation and metastasis in vivo. TTYH3 upregulated calcium influx and intracellular chloride concentration, thereby promoting cellular migration and regulating epithelial-mesenchymal transition-related protein expression. The interaction between TTYH3 and MK5 was identified through co-immunoprecipitation assays and protein docking. TTYH3 promoted the expression of MK5, which then activated the GSK3ß/ß-catenin signaling pathway. MK5 knockdown attenuated the activation of GSK3ß/ß-catenin signaling by TTYH3. TTYH3 expression was regulated in a positive feedback manner. In clinical HCC samples, TTYH3 was upregulated in the HCC tissues compared to nontumor tissues. Furthermore, high TTYH3 expression was significantly correlated with poor patient survival. The CpG islands were hypomethylated in the promoter region of TTYH3 in HCC tissues. In conclusion, we identified TTYH3 regulates tumor development and progression via MK5/GSK3-ß/ß-catenin signaling in HCC and promotes itself expression in a positive feedback loop.

MVI-Mind: A Novel Deep-Learning Strategy Using Computed Tomography (CT)-Based Radiomics for End-to-End High Efficiency Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) directly affects a patient's prognosis. The development of preoperative noninvasive diagnostic methods is significant for guiding optimal treatment plans. In this study, we investigated 138 patients with HCC and presented a novel end-to-end deep learning strategy based on computed tomography (CT) radiomics (MVI-Mind), which integrates data preprocessing, automatic segmentation of lesions and other regions, automatic feature extraction, and MVI prediction. A lightweight transformer and a convolutional neural network (CNN) were proposed for the segmentation and prediction modules, respectively. To demonstrate the superiority of MVI-Mind, we compared the framework's performance with that of current, mainstream segmentation, and classification models. The test results showed that MVI-Mind returned the best performance in both segmentation and prediction. The mean intersection over union (mIoU) of the segmentation module was 0.9006, and the area under the receiver operating characteristic curve (AUC) of the prediction module reached 0.9223. Additionally, it only took approximately 1 min to output a prediction for each patient, end-to-end using our computing device, which indicated that MVI-Mind could noninvasively, efficiently, and accurately predict the presence of MVI in HCC patients before surgery. This result will be helpful for doctors to make rational clinical decisions.

A Novel Multimodal Radiomics Model for Predicting Prognosis of Resected Hepatocellular Carcinoma.

Objective: To explore a new model to predict the prognosis of liver cancer based on MRI and CT imaging data. Methods: A retrospective study of 103 patients with histologically proven hepatocellular carcinoma (HCC) was conducted. Patients were randomly divided into training (n = 73) and validation (n = 30) groups. A total of 1,217 radiomics features were extracted from regions of interest on CT and MR images of each patient. Univariate Cox regression, Spearman's correlation analysis, Pearson's correlation analysis, and least absolute shrinkage and selection operator Cox analysis were used for feature selection in the training set, multivariate Cox proportional risk models were established to predict disease-free survival (DFS) and overall survival (OS), and the models were validated using validation cohort data. Multimodal radiomics scores, integrating CT and MRI data, were applied, together with clinical risk factors, to construct nomograms for individualized survival assessment, and calibration curves were used to evaluate model consistency. Harrell's concordance index (C-index) values were calculated to evaluate the prediction performance of the models. Results: The radiomics score established using CT and MR data was an independent predictor of prognosis (DFS and OS) in patients with HCC (p < 0.05). Prediction models illustrated by nomograms for predicting prognosis in liver cancer were established. Integrated CT and MRI and clinical multimodal data had the best predictive performance in the training and validation cohorts for both DFS [(C-index (95% CI): 0.858 (0.811-0.905) and 0.704 (0.563-0.845), respectively)] and OS [C-index (95% CI): 0.893 (0.846-0.940) and 0.738 (0.575-0.901), respectively]. The calibration curve showed that the multimodal radiomics model provides greater clinical benefits. Conclusion: Multimodal (MRI/CT) radiomics models can serve as effective visual tools for predicting prognosis in patients with liver cancer. This approach has great potential to improve treatment decisions when applied for preoperative prediction in patients with HCC.

New insights into a microvascular invasion prediction model in hepatocellular carcinoma: A retrospective study from the SEER database and China.

Background and Aims: The prognosis of liver cancer is strongly influenced by microvascular infiltration (MVI). Accurate preoperative MVI prediction can aid clinicians in the selection of suitable treatment options. In this study, we constructed a novel, reliable, and adaptable nomogram for predicting MVI. Methods: Using the Surveillance, Epidemiology, and End Results (SEER) database, we extracted the clinical data of 1,063 patients diagnosed with hepatocellular carcinoma (HCC) and divided it into either a training (n = 739) or an internal validation cohort (n = 326). Based on multivariate analysis, the training cohort data were analyzed and a nomogram was generated for MVI prediction. This was further verified using an internal validation cohort and an external validation cohort involving 293 Chinese patients. Furthermore, to evaluate the efficacy, accuracy, and clinical use of the nomogram, we used concordance index (C-index), calibration curve, and decision curve analysis (DCA) techniques. Results: In accordance with the multivariate analysis, tumor size, tumor number, alpha-fetoprotein (AFP), and histological grade were independently associated with MVI. The established model exhibited satisfactory performance in predicting MVI. The C-indices were 0.719, 0.704, and 0.718 in the training, internal validation, and external validation cohorts, respectively. The calibration curves showed an excellent consistency between the predictions and actual observations. Finally, DCA demonstrated that the newly developed nomogram had favorable clinical utility. Conclusions: We established and verified a novel preoperative MVI prediction model in HCC patients. This model can be a beneficial tool for clinicians in selecting an optimal treatment plan for HCC patients.

Knockdown of long non-coding MIR210HG inhibits cell proliferation, migration, and invasion in hepatoblastoma via the microRNA-608-FOXO6 axis.

OBJECTIVE: Hepatoblastoma is the most common liver tumor. Recent research has found that long non-coding (lnc)RNAs are involved in multiple types of cancers, but the potential mechanism of lncRNA MIR210HG in hepatoblastoma remains unknown. The present study explored the molecular mechanism of MIR210HG in hepatoblastoma progression. METHODS: The cell counting kit-8 was used to detect cell viability, and Transwell assays assessed cell migration and invasion. Luciferase reporter assays showed the relationship between MIR210HG and microRNA (miR)-608 and between miR-608 and forkhead box O6 (FOXO6). Functional tests were verified in vivo by a tumor xenograft model. The expression of MIR210HG, miR-608, FOXO6, E-cadherin, N-cadherin, and vimentin was determined by quantitative reverse transcription polymerase chain reaction and western blotting. RESULTS: MIR210HG was shown to be highly expressed in hepatoblastoma tissues and cell lines. Knockdown of MIR210HG reduced proliferation, migration, and invasion in liver cancer cells, and suppressed tumor growth in vivo. MIR210HG competitively combined with miR-608, and miR-608 decreased FOXO6 expression. CONCLUSION: Our study demonstrated that knockdown of MIR210HG inhibits hepatoblastoma development through binding to miR-608 and downregulating FOXO6. Our results provide novel insights for hepatoblastoma treatment involving the MIR210HG-miR608-FOXO6 axis.

A Novel Four-Gene Signature as a Potential Prognostic Biomarker for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with high incidence and mortality rates. However, a reliable prognostic signature has not yet been confirmed. Thus, the purpose of the present study was to develop a biomarker with high specificity and sensitivity for the diagnosis and prognosis of patients with HCC. The mRNA expression profiles of HCC were obtained from the GSE19665, GSE41804, and TCGA databases. Subsequently, 193 differentially expressed genes (DEGs) were identified from the intersection of the data from the three datasets. Bioinformatics analysis showed that the identified DEGs are related to the cell cycle, oocyte meiosis, and p53 signaling pathway, among other factors, in cancers. A protein-protein interaction (PPI) and a functional analysis were performed to investigate the biological function of the DEGs and obtain the candidate genes using the MCODE of Cytoscape. The candidate genes were introduced into the TCGA database for survival analysis, and the four candidate genes that were hub genes and meaningful for survival were retained for further verification. We validated the gene and protein expression and determined the prognosis of our patient cohort. In addition, we evaluated the biological functions regulating tumor cell proliferation and metastasis in vitro. According to the ROC curve analysis of gene expression in clinical samples, it was found that the four genes can be used to predict the diagnosis. A survival analysis based on data from the TCGA database and clinical samples showed that the four genes may be used as biomarkers for providing prognoses for patients. The cell functional experiments revealed that these four genes were related to tumor proliferation, migration, and invasion. In conclusion, the genes identified in the present study could be used as markers to diagnose and predict the prognosis of patients with HCC and guide targeted therapy.

Upregulation of TTYH3 promotes epithelial-to-mesenchymal transition through Wnt/ß-catenin signaling and inhibits apoptosis in cholangiocarcinoma.

PURPOSE: Cholangiocarcinoma (CCA) is a highly invasive malignant tumor originating from the bile duct epithelium. Tweety homolog 3 (TTYH3) is a member of the family of calcium-activated chloride channels, which have several biological functions. Here, we aimed to investigate the expression and biological function of TTYH3 in CCA. METHODS: The mRNA and protein expression levels of TTYH3 were investigated in primary human CCA tissues and normal tissues. The DNA methylation levels of three CpG sites in the TTYH3 promoter region were evaluated using pyrosequencing. The effect of TTYH3 expression on proliferation, apoptosis, migration and invasion were assessed in HUCCT1 and QBC939 cells. Xenograft models were developed to substantiate its role in the development of CCA. Western blot analysis was used to investigate the mechanistic role of TTYH3 in regulating CCA progression. RESULTS: We found that TTYH3 was highly expressed both at the mRNA and protein levels in CCA (p = 0.0001) and that the expression levels were significantly related to a poor overall survival of the patients (p = 0.0019). The DNA methylation levels of three CpG sites in the TTYH3 promoter region were significantly lower in CCA tissues compared to normal tissues (p < 0.05). In vitro studies indicated that TTYH3 can promote the proliferation, migration and invasion of the CCA cells. TTYH3 overexpression significantly promoted tumor progression and cellular proliferation in vivo as indicated by Ki-67 expression. In addition, we found that exogenous TTYH3 overexpression induced epithelial-mesenchymal transition (EMT) in CCA as indicated by expression changes in E-cadherin, N-cadherin and vimentin. The EMT process was found to occur through the Wnt/ß-catenin signaling pathway, with simultaneous changes in P-GSK3ß and ß-catenin levels. CONCLUSIONS: Our data indicate that DNA hypomethylation-induced overexpression of TTYH3 regulates CCA development and metastasis through the Wnt/ß-catenin pathway.

miRNA-Based Signature Associated With Tumor Mutational Burden in Colon Adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most common malignant tumors. Tumor mutation burden (TMB) has become an independent biomarker for predicting the response to immune checkpoint inhibitors (ICIs). miRNAs play an important role in cancer-related immune regulation. However, the relationship between miRNA expression and TMB in COAD remains unclear. Therefore, the transcriptome profiling data, clinical data, mutation annotation data, and miRNA expression profiles for cases of COAD were downloaded from the TCGA database. Subsequently, 323 COAD cases were randomly divided into training and test sets. The differential expression of miRNAs in the high and low TMB groups in the training set was obtained as a signature using the least absolute shrinkage and selection operator (LASSO) logistic regression and verified in the test set. Based on the LASSO method, principal component analysis (PCA), and ROC, we found that the signature was credible because it can discriminate between high and low TMB levels. In addition, the correlation between the 18-miRNA-based signature and immune checkpoints was performed, followed by qRT-PCR, to measure the relative expression of 18 miRNAs in COAD patients. The miRNA-based model had a strong positive correlation with TMB and a weak positive correlation with CTLA4 and CD274 (PD-L1). However, no correlation was observed between the model and SNCA (PD-1). Finally, enrichment analysis of the 18 miRNAs was performed to explore their biological functions. The results demonstrated that 18 miRNAs were involved in the process of immunity and cancer pathways. In conclusion, the 18-miRNA-based signature can effectively predict and discriminate between the different TMB levels of COAD and provide a guide for its treatment with ICIs.

Trans-Ancestry Mutation Landscape of Hepatoblastoma Genomes in Children.

Hepatoblastoma (HB) is the most common malignant tumor in the liver of infants and young children. The incidence rate varies among different populations. However, genetic differences in HB patients with different epidemiological and ancestral backgrounds have not been found. In this study, we aim to analyze data from 16 patients treated at our center and collected published data from whole-exome sequencing studies on HB, and to explore the genetic differences between races. Data from a total of 75 HB patients of three races (24 Asian, 37 Caucasian and 14 Hispanic) were analyzed. We identified 16 genes with recurrent somatic mutations and 7 core pathway modules. Among them, the Wnt/ß-catenin pathway had the highest mutation rate, and the mutation frequency in Caucasians and Hispanics was approximately twice as high as that in Asians. In addition, this study compared the characteristics of gene mutations between patients who underwent preoperative chemotherapy and those who did not and found that there was no significant difference in gene mutations between the two groups. We also preliminarily verified the function of cancer-associated candidate genes (CTNNB1 and KMT2D). In conclusion, we found ethnic differences in HB biology at the genomic level, which expands our understanding of the genetics of HB in children.

Adipose-derived mesenchymal stem cells induced PAX8 promotes ovarian cancer cell growth by stabilizing TAZ protein.

Our previous studies have shown that the Adipose-derived mesenchymal stem cells (ADSCs) can regulate metastasis and development of ovarian cancer. However, its specific mechanism has yet to be fully revealed. In this study, an RNA-seq approach was adopted to compare the differences in mRNA levels in ovarian cancer cells being given or not given ADSCs. The mRNA level of paired box 8 (PAX8) changed significantly and was confirmed as an important factor in tumour-inducing effect of ADSCs. In comparison with the ovarian cancer cells cultured in the common growth medium, those cultured in the medium supplemented with ADSCs showed a significant increase of the PAX8 level. Moreover, the cancer cell growth could be restricted, even in the ADSC-treated group (P < .05), by inhibiting PAX8. In addition, an overexpression of PAX8 could elevate the proliferation of ovarian cancer cells. Moreover, Co-IP assays in ovarian cancer cells revealed that an interaction existed between endogenous PAX8 and TAZ. And the PAX8 levels regulated the degradation of TAZ. The bioluminescence images captured in vivo manifested that the proliferation and the PAX8 expression level in ovarian cancers increased in the ADMSC-treated group, and the effect of ADSCs in promoting tumours was weakened through inhibiting PAX8. Our findings indicate that the PAX8 expression increment could contribute a role in promoting the ADSC-induced ovarian cancer cell proliferation through TAZ stability regulation.

Analysis of survival rates and prognosis of hepatoblastoma in children: a retrospective study from a single center in China.

Hepatoblastoma (HB) is the most common liver cancer in children, this study aims at analyzing the prognostic factors affecting the survival rates and summarizing the treatment experience. In this study, we reviewed patients with primary HB under the age of 14 years who underwent complete tumor resection from June 1997 to March 2019. The data of 72 patients were collected. Survival analysis was performed by Kaplan-Meier, multivariate Cox proportional hazards regression and linear mixed model for repeated measures (LMMRM). The 5-year and the 10-year event-free survival (EFS) of all patients were 78.2% and 73%, respectively. Both the 5-year and 10-year overall survival (OS) were 85.7%. Kaplan-Meier survival analysis showed that patients with tumor capsule infiltration (TCI) and patients with surgical margin less than 1 cm may also have a good prognosis. The Cox proportional hazards regression model analysis results were similar to the Kaplan-Meier analysis results. LMMRM analysis showed that there were significant differences in platelet, alpha-fetoprotein, C-reactive protein and hemoglobin values after surgery in the metastasis group (P < 0.05). This study suggests that patients with TCI or narrow surgical margin (<1 cm) may also have a good prognosis, and the risk stratification of HB can be used as the latest grading standard to evaluate the prognosis of patients.

Standard Liver Volume-Predicting Formulae Derived From Normal Liver Volume in Children Under 18 Years of Age.

Background: Standard liver volume (SLV) is important in risk assessment for major hepatectomy. We aimed to investigate the growth patterns of normal liver volume with age and body weight (BW) and summarize formulae for calculating SLV in children. Methods: Overall, 792 Chinese children (<18 years of age) with normal liver were enrolled. Liver volumes were measured using computed tomography. Correlations between liver volume and BW, body height (BH), and body surface area (BSA) were analyzed. New SLV formulae were selected from different regression models; they were assessed by multicentral validations and were compared. Results: The growth patterns of liver volume with age (1 day-18 years) and BW (2-78 kg) were summarized. The volume grows from a median of 139 ml (111.5-153.6 in newborn) to 1180.5 ml (1043-1303.1 at 16-18 years). Liver volume was significantly correlated with BW (r = 0.95, P < 0.001), BH (r = 0.92, P < 0.001), and BSA (r = 0.96, P < 0.001). The effect of sex on liver volume increases with BW, and BW of 20 kg was identified as the optimal cutoff value. The recommended SLV formulae were BW≤20 kg: SLV = 707.12 × BSA 1.09; BW>20 kg, males: SLV = 691.90 × BSA 1.06; females: SLV = 663.19 × BSA 1.04. Conclusions: We summarized the growth patterns of liver volume and provided formulae predicting SLV in Chinese children, which is useful in assessing the safety of major hepatectomies.