Radiomics model based on contrast-enhanced computed tomography to predict early recurrence in patients with hepatocellular carcinoma after radical resection.

BACKGROUND: Radical resection remains an effective strategy for patients with hepatocellular carcinoma (HCC). Unfortunately, the postoperative early recurrence (recurrence within 2 years) rate is still high. AIM: To develop a radiomics model based on preoperative contrast-enhanced computed tomography (CECT) to evaluate early recurrence in HCC patients with a single tumour. METHODS: We enrolled a total of 402 HCC patients from two centres who were diagnosed with a single tumour and underwent radical resection. First, the features from the portal venous and arterial phases of CECT were extracted based on the region of interest, and the early recurrence-related radiomics features were selected via the least absolute shrinkage and selection operator proportional hazards model (LASSO Cox) to determine radiomics scores for each patient. Then, the clinicopathologic data were combined to develop a model to predict early recurrence by Cox regression. Finally, we evaluated the prediction performance of this model by multiple methods. RESULTS: A total of 1915 radiomics features were extracted from CECT images, and 31 of them were used to determine the radiomics scores, which showed a significant difference between the early recurrence and nonearly recurrence groups. Univariate and multivariate Cox regression analyses showed that radiomics scores and serum alpha-fetoprotein were independent indicators, and they were used to develop a combined model to predict early recurrence. The area under the receiver operating characteristic curve values for the training and validation cohorts were 0.77 and 0.74, respectively, while the C-indices were 0.712 and 0.674, respectively. The calibration curves and decision curve analysis showed satisfactory accuracy and clinical utilities. Kaplan-Meier curves based on recurrence-free survival and overall survival showed significant differences. CONCLUSION: The preoperative radiomics model was shown to be effective for predicting early recurrence among HCC patients with a single tumour.

Dominant neoantigen verification in hepatocellular carcinoma by a single-plasmid system coexpressing patient HLA and antigen.

BACKGROUND: Previous studies confirmed that most neoantigens predicted by algorithms do not work in clinical practice, and experimental validations remain indispensable for confirming immunogenic neoantigens. In this study, we identified the potential neoantigens with tetramer staining, and established the Co-HA system, a single-plasmid system coexpressing patient human leukocyte antigen (HLA) and antigen, to detect the immunogenicity of neoantigens and verify new dominant hepatocellular carcinoma (HCC) neoantigens. METHODS: First, we enrolled 14 patients with HCC for next-generation sequencing for variation calling and predicting potential neoantigens. Then, the Co-HA system was established. To test the feasibility of the system, we constructed target cells coexpressing HLA-A*11:01 and the reported KRAS G12D neoantigen as well as specific T-cell receptor (TCR)-T cells. The specific cytotoxicity generated by this neoantigen was shown using the Co-HA system. Moreover, potential HCC-dominant neoantigens were screened out by tetramer staining and validated by the Co-HA system using methods including flow cytometry, enzyme-linked immunospot assay and ELISA. Finally, antitumor test in mouse mode and TCR sequencing were performed to further evaluate the dominant neoantigen. RESULTS: First, 2875 somatic mutations in 14 patients with HCC were identified. The main base substitutions were C>T/G>A transitions, and the main mutational signatures were 4, 1 and 16. The high-frequency mutated genes included HMCN1, TTN and TP53. Then, 541 potential neoantigens were predicted. Importantly, 19 of the 23 potential neoantigens in tumor tissues also existed in portal vein tumor thrombi. Moreover, 37 predicted neoantigens restricted by HLA-A*11:01, HLA-A*24:02 or HLA-A*02:01 were performed by tetramer staining to screen out potential HCC-dominant neoantigens. HLA-A*24:02-restricted epitope 5'-FYAFSCYYDL-3' and HLA-A*02:01-restricted epitope 5'-WVWCMSPTI-3' demonstrated strong immunogenicity in HCC, as verified by the Co-HA system. Finally, the antitumor efficacy of 5'-FYAFSCYYDL-3'-specific T cells was verified in the B-NDG-B2mtm1Fcrntm1(mB2m) mouse and their specific TCRs were successfully identified. CONCLUSION: We found the dominant neoantigens with high immunogenicity in HCC, which were verified with the Co-HA system.

Prediction of early treatment response to the combination therapy of TACE plus lenvatinib and anti-PD-1 antibody immunotherapy for unresectable hepatocellular carcinoma: Multicenter retrospective study.

Background and aim: The purpose of this study was to investigate and validate the efficacy of a nomogram model in predicting early objective response rate (ORR) in u-HCC patients receiving a combination of TACE, Lenvatinib, and anti-PD-1 antibody treatment after 3 months (triple therapy). Method: This study included 169 u-HCC cases from five different hospitals. As training cohorts (n = 102), cases from two major centers were used, and external validation cohorts (n = 67) were drawn from the other three centers. The clinical data and contrast-enhanced MRI characteristics of patients were included in this retrospective study. For evaluating MRI treatment responses, the modified revaluation criteria in solid tumors (mRECIST) were used. Univariate and multivariate logistic regression analyses were used to select relevant variables and develop a nomogram model. Our as-constructed nomogram was highly consistent and clinically useful, as confirmed by the calibration curve and decision curve analysis (DCA); an independent external cohort also calibrated the nomogram. Results: The ORR was 60.7% and the risk of early ORR was independently predicted by AFP, portal vein tumor thrombus (PVTT), tumor number, and size in both the training (C-index = 0.853) and test (C-index = 0.731) cohorts. The calibration curve revealed that the nomogram-predicted values were consistent with the actual response rates in both cohorts. Furthermore, DCA indicated that our developed nomogram performed well in clinical settings. Conclusion: The nomogram model accurately predicts early ORR achieved by triple therapy in u-HCC patients, which aids in individual decision-making and modifying additional therapies for u-HCC cases.

Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study.

OBJECTIVES: In this multicenter study, we sought to develop and validate a preoperative model for predicting early recurrence (ER) risk after curative resection of intrahepatic cholangiocarcinoma (ICC) through artificial intelligence (AI)-based CT radiomics approach. MATERIALS AND METHODS: A total of 311 patients (Derivation: 160; Internal and two external validations: 36, 74 and 61) from 8 medical centers who underwent curative resection were collected retrospectively. In derivation cohort, radiomics and clinical-radiomics models for ER prediction were constructed by LightGBM (a machine learning algorithm). A clinical model was also developed for comparison. Model performance was validated in internal and two external cohorts by ROC. In addition, we investigated the interpretability of the LightGBM model. RESULTS: The combined clinical-radiomics model that included 15 radiomic features and 3 clinical features (CA19-9 > 1000 U/ml, vascular invasion and tumor margin), resulting in the area under the curves (AUCs) of 0.974 (95% CI 0.946-1.000) in the derivation cohort, and 0.871-0.882 (95% CI 0.672-0.962) in the internal and external validation cohorts, respectively, which are higher than the AJCC 8th TNM staging system (AUCs: 0.686-0.717, p all < 0.05). Especially, the sensitivity of this machine learning model could reach 94.6% on average for all the cohorts. CONCLUSIONS: This AI-driven combined radiomics model may provide as a useful tool to preoperatively predict ER and improve therapeutic management of ICC patients.

A signature based on NKG2D ligands to predict the recurrence of hepatocellular carcinoma after radical resection.

INTRODUCTION: Due to the high recurrence, the HCC prognosis remains poor. Yet, the biomarkers for predicting the recurrence of high-risk patients are currently lacking. We aimed to develop a signature to predict the recurrence of HCC based on NKG2D ligands. METHODS: The multivariate Cox proportional hazards regression was used to select recurrence-related variables of NKG2D ligands in HCC patients from The Cancer Genome Atlas (TCGA). HCC patients from the OEP000321 dataset and Guilin cohort were used to validate the predictive signature. The mRNA expression of NKG2D ligands was measured by QRT-PCR. Immunohistochemistry analysis of HCC tissue microarray samples was used to identify the expression of NKG2D ligands. RESULTS: In this study, NKG2D ligands expression in the mRNA and protein level was both abnormally expressed in HCC and associated with recurrence-free survival (RFS). Then, the recurrence-related variables of NKG2D ligands in HCC were selected by the multivariate Cox proportional hazards regression. Among the eight NKG2D ligands, MICA (HR = 1.347; 95% CI = 1.012-1.793; p = 0.041), ULBP3 (HR = 0.453; 95% CI = 0.231-0.889; p = 0.021) and ULBP5 (HR = 3.617; 95% CI = 1.819-7.194; p < 0.001) were significantly correlated with RFS in the TCGA-LIHC cohort. Then, the signature was constructed by the three NKG2D ligands. The predictive effectiveness of this signature was also validated in the OEP000321 dataset and Guilin cohort. Further, HCC patients were classified into low-risk and high-risk subgroups by the predictive score. Compared with the low-risk group, the high-risk group had poor RFS in both training and validation cohorts. Importantly, compared with the low-risk patients with the G1-G2 stage, the levels of infiltrated NK-activated cells and NKG2D expression were both lower in the high-risk patients. CONCLUSIONS: The signature based on MICA, ULBP3, and ULBP5 could predict HCC recurrence.

Novel index for the prediction of significant liver fibrosis and cirrhosis in chronic hepatitis B patients in China.

BACKGROUND: Noninvasive, practical, and convenient means of detection for the prediction of liver fibrosis and cirrhosis in China are greatly needed. AIM: To develop a precise noninvasive test to stage liver fibrosis and cirrhosis. METHODS: With liver biopsy as the gold standard, we established a new index, [alkaline phosphatase (U/L) + gamma-glutamyl transpeptidase (U/L)/platelet (109/L) (AGPR)], to predict liver fibrosis and cirrhosis. In addition, we compared the area under the receiver operating characteristic curve (AUROC) of AGPR, gamma-glutamyl transpeptidase to platelet ratio, aspartate transaminase to platelet ratio index, and FIB-4 and evaluated the accuracy of these routine laboratory indices in predicting liver fibrosis and cirrhosis. RESULTS: Correlation analysis revealed a significant positive correlation between AGPR and liver fibrosis stage (P < 0.001). In the training cohort, the AUROC of AGPR was 0.83 (95%CI: 0.78-0.87) for predicting fibrosis (≥ F2), 0.84 (95%CI: 0.79-0.88) for predicting extensive fibrosis (≥ F3), and 0.87 (95%CI: 0.83-0.91) for predicting cirrhosis (F4). In the validation cohort, the AUROCs of AGPR to predict ≥ F2, ≥ F3 and F4 were 0.83 (95%CI: 0.77-0.88), 0.83 (95%CI: 0.77-0.89), and 0.84 (95%CI: 0.78-0.89), respectively. CONCLUSION: The AGPR index should become a new, simple, accurate, and noninvasive marker to predict liver fibrosis and cirrhosis in chronic hepatitis B patients.

Preoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement. AIM: To develop and validate a contrast-enhanced computed tomography-based radiomics model for predicting the overall survival (OS) of HCC patients after radical hepatectomy. METHODS: A total of 150 HCC patients were randomly divided into a training cohort (n = 107) and a validation cohort (n = 43). Radiomics features were extracted from the entire tumour lesion. The least absolute shrinkage and selection operator algorithm was applied for the selection of radiomics features and the construction of the radiomics signature. Univariate and multivariate Cox regression analyses were used to identify the independent prognostic factors and develop the predictive nomogram, incorporating clinicopathological characteristics and the radiomics signature. The accuracy of the nomogram was assessed with the concordance index, receiver operating characteristic (ROC) curve and calibration curve. The clinical utility was evaluated by decision curve analysis (DCA). Kaplan-Meier methodology was used to compare the survival between the low- and high-risk subgroups. RESULTS: In total, seven radiomics features were selected to construct the radiomics signature. According to the results of univariate and multivariate Cox regression analyses, alpha-fetoprotein (AFP), neutrophil-to-lymphocyte ratio (NLR) and radiomics signature were included to build the nomogram. The C-indices of the nomogram in the training and validation cohorts were 0.736 and 0.774, respectively. ROC curve analysis for predicting 1-, 3-, and 5-year OS confirmed satisfactory accuracy [training cohort, area under the curve (AUC) = 0.850, 0.791 and 0.823, respectively; validation cohort, AUC = 0.905, 0.884 and 0.911, respectively]. The calibration curve analysis indicated a good agreement between the nomogram-prediction and actual survival. DCA curves suggested that the nomogram had more benefit than traditional staging system models. Kaplan-Meier survival analysis indicated that patients in the low-risk group had longer OS and disease-free survival (all P < 0.0001). CONCLUSION: The nomogram containing the radiomics signature, NLR and AFP is a reliable tool for predicting the OS of HCC patients.

Preoperative Radiomics Analysis of Contrast-Enhanced CT for Microvascular Invasion and Prognosis Stratification in Hepatocellular Carcinoma.

Purpose: Microvascular invasion (MVI) impairs long-term prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel nomogram to predict MVI and patients' prognosis based on radiomic features of contrast-enhanced CT (CECT). Patients and Methods: HCC patients who underwent curative resection were enrolled. The radiomic features were extracted from the region of tumor, and the optimal MVI-related radiomic features were selected and applied to construct radiomic signature (Rad-score). The prediction models were created according to the logistic regression and evaluated. Biomarkers were analyzed via q-PCR from randomly selected HCC patients. Correlations between biomarkers and radiomic signature were analyzed. Results: A total of 421 HCC patients were enrolled. A total of 1962 radiomic features were extracted from the region of tumor, and the 11 optimal MVI-related radiomic features showed a favor predictive ability with area under the curves (AUCs) of 0.796 and 0.810 in training and validation cohorts, respectively. Aspartate aminotransferase (AST), tumor number, alpha-fetoprotein (AFP) level, and radiomics signature were independent risk factors of MVI. The four factors were integrated into the novel nomogram, named as CRM, with AUCs of 0.767 in training cohort and 0.793 in validation cohort for predicting MVI, best among radiomics signature alone and clinical model. The nomogram was well-calibrated with favorable clinical value demonstrated by decision curve analysis and can divide patients into high- or low-risk subgroups of recurrence and mortality. In addition, gene BCAT1, DTGCU2, DOCK3 were analyzed via q-PCR and serum AFP were identified as having significant association with radiomics signature. Conclusion: The novel nomogram demonstrated good performance in preoperatively predicting the probability of MVI, which might guide clinical decision.

Predictive potential of Nomogram based on GMWG for patients with hepatocellular carcinoma after radical resection.

BACKGROUND: Since it's a challenging task to precisely predict the prognosis of patients with hepatocellular carcinoma (HCC). We developed a nomogram based on a novel indicator GMWG [(Geometric Mean of gamma-glutamyltranspeptidase (GGT) and white blood cell (WBC)] and explored its potential in the prognosis for HCC patients. METHODS: The patients enrolled in this study were randomly assigned to training and validation cohorts. And we performed the Least Absolute Shrinkage and Selection Operator proportional hazards model (LASSO Cox) model with clinical characteristics, serum indexes, and novel GMWG. Multivariate analysis was performed to build a nomogram. The performance of the nomogram was evaluated by C-index, the area under the receiver operating characteristic curve (AUC), and the calibration curve. Kaplan-Meier curves showed discrimination of the nomogram. Clinical utility was assessed by decision curve analysis (DCA). The discrimination ability of the nomogram was determined by the net reclassification index (NRI). RESULTS: The geometric mean of GGT and white WBC count (GMWG), neutrophil to lymphocyte ratio (NLR), and tumor size were significantly associated with the overall survival (OS). The variables above were used to develop the nomogram. The indexes of nomogram were 0.70 and 071 in the training or validation cohort, respectively. AUC of 1-, 3- and 5-year OS showed satisfactory accuracy as well. The calibration curve showed agreement between the ideal and predicted values. Kaplan-Meier curves based on the overall survival (OS) and disease-free survival (DFS) showed significant differences between nomogram predictive low and high groups. DCA showed clinical utilities while NRI showed discrimination ability in both training or validation cohort. CONCLUSIONS: GMWG might be a potential prognostic indicator for patients with HCC. The nomogram containing GMWG also showed satisfaction prediction capacity.

A Novel Post-Operative ALRI Model Accurately Predicts Clinical Outcomes of Resected Hepatocellular Carcinoma Patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading malignant tumors worldwide. Prognosis and long-term survival of HCC remain unsatisfactory, even after radical resection, and many non-invasive predictors have been explored for post-operative patients. Most prognostic prediction models were based on preoperative clinical characteristics and pathological findings. This study aimed to investigate the prognostic value of a newly constructed nomogram, which incorporated post-operative aspartate aminotransferase to lymphocyte ratio index (ALRI). METHODS: A total of 771 HCC patients underwent radical resection from three medical centers were enrolled and grouped into the training cohort (n = 416) and validation cohort (n = 355). Prognostic prediction potential of ALRI was assessed by receiver operating curve (ROC) analysis. The Cox regression model was used to identify independent prognostic factors. Nomograms for overall survival (OS) and disease-free survival (DFS) were constructed and further validated externally. RESULTS: The ROC analysis ranked ALRI as the most effective prediction marker for resected HCC patients, with the cut-off value determined at 22.6. Higher ALRI level positively correlated with larger tumor size, higher tumor node metastasis (TNM) stage, and inversely with lower albumin level and shorter OS and DFS. Nomograms for OS and DFS were capable of discriminating HCC patients into different risk-groups. CONCLUSIONS: Post-operative ALRI was of prediction value for HCC prognosis. This novel nomogram may categorize HCC patients into different risk groups, and offer individualized surveillance reference for post-operative patients.

Preoperative Gadoxetic Acid-Enhanced MRI Based Nomogram Improves Prediction of Early HCC Recurrence After Ablation Therapy.

PURPOSE: This study aimed to identify preoperative gadoxetic acid-enhanced MRI features and establish a nomogram for predicting early recurrence (≤ 2 years) of hepatocellular carcinoma (HCC) after ablation therapy. METHODS: A total of 160 patients who underwent gadoxetic acid-enhanced MRI and ablation HCC therapy from January 2015 to June 2018, were included retrospectively and divided into a training cohort (n = 112) and a validation cohort (n = 48). Independent clinical risk factors and gadoxetic acid-enhanced MRI features associated with early recurrence were identified by univariate and multivariate logistic regression analysis and used for construction of a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility. RESULTS: Alpha-fetoprotein (AFP) level, tumor number, arterial peritumoral enhancement, satellite nodule and peritumoral hypointensity at hepatobiliary phases in the training cohort were identified as independent risk factors for early recurrence after ablation. A new nomogram that was constructed with these five features showed an area under the curve (AUC) of 0.843 (95%CI 0.771-0.916) and 0.835 (95%CI 0.713-0.956) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had good consistency and clinical utility. CONCLUSIONS: A new nomogram that was constructed using four preoperative gadoxetic acid-enhanced MRI features and serum AFP level can predict the risk of early HCC recurrence after ablation therapy with AUC up to 0.843. The strong performance of this nomogram may help hepatologists to categorize patients' recurrent risk to guide selecting treatment options and improve postoperative management.

A Novel Nomogram to Predict Prolonged Survival After Hepatectomy in Repeat Recurrent Hepatocellular Carcinoma.

Background: Repeat hepatectomy is an important treatment for patients with repeat recurrent hepatocellular carcinoma (HCC). Methods: This study was a multicenter retrospective analysis of 1,135 patients who underwent primary curative liver resection for HCC. One hundred recurrent patients with second hepatectomy were included to develop a nomogram to predict the risk of post-recurrence survival (PRS). Thirty-eight patients in another institution were used to externally validate the nomogram. Univariate and multivariate Cox regression analyses were used to identify independent risk factors of PRS. Discrimination, calibration, and the Kaplan-Meier curves were used to evaluate the model performance. Results: The nomogram was based on variables associated with PRS after HCC recurrence, including the tumor, node, and metastasis (TNM) stage; albumin and aspartate aminotransferase levels at recurrence; tumor size, site, differentiation of recurrences; and time to recurrence (TTR). The discriminative ability of the nomogram, as indicated by the C statistics (0.758 and 0.811 for training cohort and external validation cohorts, respectively), was shown, which was better than that of the TNM staging system (0.609 and 0.609, respectively). The calibration curves showed ideal agreement between the prediction and the real observations. The area under the curves (AUCs) of the training cohort and external validation cohorts were 0.843 and 0.890, respectively. The Kaplan-Meier curve of the established nomogram also performed better than those of both the TNM and the BCLC staging systems. Conclusions: We constructed a nomogram to predict PRS in patients with repeat hepatectomy (RH) after repeat recurrence of HCC.

A new model based on gamma-glutamyl transpeptidase to platelet ratio (GPR) predicts prognostic outcome after curative resection of solitary hepatocellular carcinoma.

BACKGROUND: This study intends to explore the potential clinical value of gamma-glutamyl transpeptidase to platelet ratio (GPR) and the new multi-factor scoring model for recurrence and prognosis prediction in solitary HCC patients who received radical resection. METHODS: This study retrospectively analyzed 295 HCC patients after curative resection. According to the Receiver Operating Characteristic (ROC) curve, the optimal cut-off value of GPR for predicting prognosis of HCC after resection was determined. The Kaplan Meier method and Cox regression analysis were performed to assess the important potential factors in the prognosis of HCC and determine the independent risk factors. Assign a value to each independent risk factor and establish a new scoring model. Then, using GPR and the new scoring model to evaluate overall survival (OS) and postoperative recurrence probability. RESULTS: When GPR's cut-off value was selected as 0.30, its predictive efficiency for postoperative prognosis was more favorable than those of other cut-off values (0.76, 0.84 and 0.94). GPR, tumor size, microvascular invasion and neutrophil to lymphocyte ratio (NLR) were identified as independent prognostic predictors. Using these variables, a novel prognostic scoring model was devised and established to identify different levels of risk: high, intermediate and low risk groups. We found that patients with high GPR level and of high risk group would have a poorer OS and a higher recurrence rate after radical resection. CONCLUSIONS: GPR may serve as a promising predictor for postoperative prognosis and recurrence probability of HCC, and the new prognostic scoring model may be available for postoperative management among HCC patients.

AGLR is a novel index for the prognosis of hepatocellular carcinoma patients: a retrospective study.

BACKGROUND: Most hepatocellular carcinoma (HCC) patients' liver function indexes are abnormal. We aimed to investigate the relationship between (alkaline phosphatase + gamma-glutamyl transpeptidase)/lymphocyte ratio (AGLR) and the progression as well as the prognosis of HCC. METHODS: A total of 495 HCC patients undergoing radical hepatectomy were retrospectively analyzed. We randomly divided these patients into the training cohort (n = 248) and the validation cohort (n = 247). In the training cohort, receiver operating characteristic (ROC) curve was used to determine the optimal cut-off value of AGLR for predicting postoperative survival of HCC patients, and the predictive value of AGLR was evaluated by concordance index (C-index). Further analysis of clinical and biochemical data of patients and the correlation analysis between AGLR and other clinicopathological factors were finished. Univariate and multivariate analyses were performed to identify prognostic factors for HCC patients. Survival curves were analyzed using the Kaplan-Meier method. RESULTS: According to the ROC curve analysis, the optimal predictive cut-off value of AGLR was 90. The C-index of AGLR was 0.637 in the training cohort and 0.654 in the validation cohort, respectively. Based on this value, the HCC patients were divided into the low-AGLR group (AGLR ≤ 90) and the high-AGLR group (AGLR > 90). Preoperative AGLR level was positively correlated with alpha-fetoprotein (AFP), tumor size, tumor-node-metastasis (TNM) stage, and microvascular invasion (MVI) (all p < 0.05). In the training and validation cohorts, patients with AGLR > 90 had significantly shorter OS than patients with AGLR ≤ 90 (p < 0.001). Univariate and multivariate analyses of the training cohort (HR, 1.79; 95% CI 1.21-2.69; p < 0.001) and validation cohort (HR, 1.82; 95% CI 1.35-2.57; p < 0.001) had identified AGLR as an independent prognostic factor. A new prognostic scoring model was established based on the independent predictors determined in multivariate analysis. CONCLUSIONS: The elevated preoperative AGLR level indicated poor prognosis for patients with HCC; the novel prognostic scoring model had favorable predictive capability for postoperative prognosis of HCC patients, which may bring convenience for clinical management.

The Significance of Gamma-Glutamyl Transpeptidase to Lymphocyte Count Ratio in the Early Postoperative Recurrence Monitoring and Prognosis Prediction of AFP-Negative Hepatocellular Carcinoma.

BACKGROUND: Currently, there is still a lack of effective biomarkers for the recurrence monitoring and survival prognosis assessment of hepatocellular carcinoma (HCC) patients with alpha-fetoprotein (AFP)-negative (≤20 ng/mL) after radical resection. METHODS: The clinicopathological data of 606 patients (303 in the AFP-negative group and 303 in the AFP-positive group) who underwent radical resection of HCC were analyzed retrospectively. RESULTS: The gamma-glutamyl transpeptidase to lymphocyte count ratio (GLR) of patients in the AFP-negative group was lower than that in the AFP-positive group (p <0.001). The GLR level of the early-recurrence group was higher than that of the non-early-recurrence group (p =0.003). GLR had fair accuracy in predicting the early-recurrence of HCC patients [c-index=0.654 (95% CI=0.606-0.702); AUC=0.681 (95% CI=0.625-0.733)]. Univariate analysis showed that patients with tumor size <5 cm, no microvascular invasion, single tumor, no metastasis, BCLC stage 0-A, no recurrence, and GLR ≤45.0 had longer disease-free survival (DFS) and overall survival (OS) among AFP-negative HCC patients. In addition, multivariate Cox proportional hazards regression analysis showed that tumor size <5 cm (p =0.003), no recurrence (p <0.001), and GLR <45.0 (p <0.001) were independent predictors of longer OS. CONCLUSION: GLR may be a potential indicator for early recurrence monitoring and prognosis evaluation in HCC patients with AFP-negative after radical resection.

RFX5 promotes the progression of hepatocellular carcinoma through transcriptional activation of KDM4A.

Regulatory factor X-5 (RFX5) represents a key transcription regulator of MHCII gene expression in the immune system. This study aims to explore the molecular mechanisms and biological significance of RFX5. Firstly, by analyzing ENCODE chromatin immunoprecipitation (ChIP)-seq in HepG2 and TCGA RNA-seq data, we discovered lysine-specific demethylase 4A (KDM4A), also named JMJD2A, to be a major downstream target gene of RFX5. Moreover, RFX5 was verified to bind directly to the KDM4A's promoter region and sequentially promoted its transcription determined by the ChIP-PCR assay and luciferase assay. In addition, RFX5-dependent regulation of KDM4A was demonstrated in HCC. Compared with adjacent non-tumor tissues, the expression levels of KDM4A were significantly raised in HCC tumor tissues. Notably, elevated levels of KDM4A were strongly correlated with HCC patient prognosis. Functionally, KDM4A overexpression largely rescued the growth inhibitory effects of RFX5 deletion, highlighting KDM4A as a downstream effector of RFX5. Mechanistically, the RFX5-KDM4A pathway promoted the progression of the cell cycle from G0/G1 to S phase and was protective against cell apoptosis through regulation of p53 and its downstream genes in HCC. In conclusion, RFX5 could promote HCC progression via transcriptionally activating KDM4A expression.

Impact of liver fibrosis score on prognosis after common therapies for intrahepatic cholangiocarcinoma: a propensity score matching analysis.

BACKGROUND: Liver fibrosis or cirrhosis is associated with the dismal prognosis of hepatocellular carcinoma (HCC), and it might also be involved in intrahepatic cholangiocarcinoma (ICC). The effect of hepatic fibrosis on the survival of ICC patients is still unclear. This study aims to explore whether liver fibrosis impacts the overall survival (OS) and disease-specific survival (DSS) of ICC patients. METHODS: Data of 729 eligible ICC patients receiving different therapies from the Surveillance, Epidemiology, and End Results database (2004-2015) were analyzed. Unmatched, propensity score-matched, and propensity score-weighted cohorts were used to investigate the relationships of different fibrosis scores (low fibrosis score vs. high fibrosis score) and survival. A Cox regression and Kaplan-Meier curves were used to explore the influence of fibrosis score on patients' survival. Stratified analyses based on treatment modality were conducted to compare the survival difference in ICC patients with different fibrosis scores. RESULTS: Before matching, the one-, three-, and five-year OS were 50.9, 28.0, and 16.1% in the low fibrosis score group (n = 465) and 39.3, 20.1, and 8.0% in the high fibrosis score group (n = 264) (P < 0.001), respectively. After propensity score matching, the one-, three-, and five-year OS were 45.0, 26.0, and 10.2% in the low fibrosis score group and 36.0, 8.1, and 2.3% in the high fibrosis score group (P = 0.008), respectively. The multivariate Cox regression results showed that a high fibrosis score was an independent risk factor of OS. Additionally, patients with high fibrosis scores achieved low DSS after matching (P = 0.032). The survival benefits of the low fibrosis score group were consistent across treatment cohorts. CONCLUSIONS: High fibrosis scores were associated with poor clinical outcomes of ICC patients receiving different common therapies.

Correlation Analysis Between Preoperative Serum Iron Level and Prognosis as Well as Recurrence of HCC After Radical Resection.

BACKGROUND: The purpose of this retrospective study was to investigate the relationship between serum iron levels and the prognosis and risk of recurrence in patients with hepatocellular carcinoma (HCC). METHODS: A total of 253 HCC patients who underwent radical resection were involved in this study. RESULTS: According to the receiver operating characteristic (ROC) curve, the optimal cut-off value for preoperative serum iron in the assessment of HCC postoperative prognosis was 94 ug/dL. The overall survival (OS) of patients in the high iron group was significantly better than that in the low iron group (p < 0.001). The recurrence rate of patients in the low iron group was higher than that in the high iron group (p = 0.011). Correlation analysis showed that preoperative serum iron level was correlated with tumor size >5 cm (χ 2 = 11.590, p < 0.001), recurrence (χ 2 = 5.714, p = 0.017) and microvascular invasion (χ 2 = 5.087, p = 0.024). In addition, univariate analysis showed that OS and disease-free survival (DFS) of HCC patients with high iron level were better than those with low iron level. Furthermore, multivariate COX proportional hazards regression analysis showed that serum iron ≤94 µg/dL, tumor size >5 cm, and microvascular invasion were independent predictors for shorter OS and DFS in HCC patients after operation, while recurrence was for shorter OS. CONCLUSION: Patients with low preoperative serum iron level had worse postoperative survival and higher recurrence rate in HCC. Preoperative serum iron is an independent predictor of HCC patients. For HCC patients with low iron levels, prognosis of patients may be improved if appropriate iron is supplemented.

Predictive value of gamma-glutamyl transpeptidase to lymphocyte count ratio in hepatocellular carcinoma patients with microvascular invasion.

BACKGROUND: Microvascular invasion (MVI) is an independent risk factor for poor prognosis in hepatocellular carcinoma (HCC). However, there is still a lack of preoperative markers to predict MVI in HCC. This study intends to explore the potential application value of the gamma-glutamyl transpeptidase (GGT) to lymphocyte count ratio (GLR) in predicting MVI in HCC and provide guidance for clinical diagnosis and treatment. METHODS: From March 2010 to December 2015, 230 HCC patients who underwent surgical treatment in the Affiliated Hospital of Guilin Medical University were selected. Clinicopathological parameters between the MVI group (n = 115) and the non-MVI group (n = 115) were comparatively analyzed. The GLR was used as the potential risk factor for HCC with MVI, and its optimal cut-off value was estimated by using the receiver operating characteristic (ROC) curve. The Kaplan-Meier method was used to analyze the survival of HCC patients, and univariate and multivariate Cox regression analyses were used to establish independent predictors affecting postoperative HCC patients. RESULTS: The GLR levels in the MVI group and non-MVI group were 84.83 ± 61.84 and 38.42 ± 33.52 (p <  0.001), respectively. According to ROC curve analysis, the optimal cut-off value of GLR was 56.0, and the area under the ROC curve (AUC) was 0.781 (95% CI, 0.719-0.833) for the risk prediction of MVI in HCC patients. Multivariate analysis showed that tumor size > 5 cm, HCC combined with MVI and GLR >  56.0 were independent risk factors for poor prognosis in HCC patients. In addition, compared with the non-MVI group, patients in the MVI group had shorter progression-free survival (PFS) and overall survival (OS). CONCLUSION: GLR could be a predictive biomarker of HCC after operation and a potential predictor of HCC combined with MVI.

Long Intergenic Nonprotein Coding RNA 0152 Promotes Hepatocellular Carcinoma Progression by Regulating Phosphatidylinositol 3-Kinase/Akt/Mammalian Target of Rapamycin Signaling Pathway through miR-139/PIK3CA.

Hepatocellular carcinoma (HCC) ranks as the fifth most common cancer worldwide, and it is the primary histologic subtype of liver cancer, with high incidence and poor prognosis. Recently, numerous long noncoding RNAs have been reported to be associated with the tumorigenesis of HCC; however, the underlying mechanisms of long intergenic nonprotein coding RNA 0152 (LINC00152) action in HCC are poorly understood. Herein, we identified a significant up-regulation of LINC00152 in both HCC tissues and cell lines. Functional studies showed that knockdown of LINC00152 inhibited cell proliferation, migration, and invasion, but promoted cell apoptosis, indicating its oncogenic functions in HCC tumorigenesis. Mechanistically, LINC00152 functioned as an efficient miR-139 sponge, thereby releasing the suppression of PIK3CA (a target gene of miR-139). Anti-miR-139 rescued the inhibition of cell proliferation, migration, and invasion induced by LINC00152 knockdown. Similarly, PIK3CA-overexpressing plasmid also reversed miR-139-mediated biological functions in HCC cells. Taken together, our study revealed a crucial regulatory network of LINC00152/miR-139/PIK3CA axis in the tumorigenesis of HCC, implying that LINC00152 may be a biomarker and novel therapeutic target for further clinical therapy of HCC.