New nomogram predicts the recurrence of hepatocellular carcinoma in patients with negative preoperative serum AFP subjected to curative resection

Prognostic performance of MRI LI-RADS version 2018 features and clinical-pathological factors in alpha-fetoprotein-negative hepatocellular carcinoma

PURPOSE

To evaluate the role of the magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS) version 2018 features and clinical-pathological factors for predicting the prognosis of alpha-fetoprotein (AFP)-negative (≤ 20 ng/ml) hepatocellular carcinoma (HCC) patients, and to compare with other traditional staging systems.

METHODS

We retrospectively enrolled 169 patients with AFP-negative HCC who received preoperative MRI and hepatectomy between January 2015 and August 2020 (derivation dataset:validation dataset = 118:51). A prognostic model was constructed using the risk factors identified via Cox regression analysis. Predictive performance and discrimination capability were evaluated and compared with those of two traditional staging systems.

RESULTS

Six risk factors, namely the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade, were associated with recurrence-free survival. The prognostic model constructed using these factors achieved C-index of 0.705 and 0.674 in the derivation and validation datasets, respectively. Furthermore, the model performed better in predicting patient prognosis than traditional staging systems. The model effectively stratified patients with AFP-negative HCC into high- and low-risk groups with significantly different outcomes (p < 0.05).

CONCLUSION

A prognostic model integrating the LI-RADS category, blood products in mass, microvascular invasion, tumor size, cirrhosis, and albumin-bilirubin grade may serve as a valuable tool for refining risk stratification in patients with AFP-negative HCC.

Connecting the Dots: The Cerebral Lymphatic System as a Bridge Between the Central Nervous System and Peripheral System in Health and Disease

As a recently discovered waste removal system in the brain, cerebral lymphatic system is thought to play an important role in regulating the homeostasis of the central nervous system. Currently, more and more attention is being focused on the cerebral lymphatic system. Further understanding of the structural and functional characteristics of cerebral lymphatic system is essential to better understand the pathogenesis of diseases and to explore therapeutic approaches. In this review, we summarize the structural components and functional characteristics of cerebral lymphatic system. More importantly, it is closely associated with peripheral system diseases in the gastrointestinal tract, liver, and kidney. However, there is still a gap in the study of the cerebral lymphatic system. However, we believe that it is a critical mediator of the interactions between the central nervous system and the peripheral system.

Development and validation of a nomogram to predict the recurrence of hepatocellular carcinoma patients with dynamic changes in AFP undergoing locoregional treatments

Background

Serum alpha-fetoprotein (AFP) is an important clinical indicator for screening, diagnosis, and prognosis of primary hepatocellular carcinoma (HCC). Our team's previous study showed that patients with negative AFP at baseline and positive AFP at relapse had a worse prognosis (N-P). Therefore, the aim of our study was to develop and validate a nomogram for this group of patients.

Methods

A total of 513 patients with HCC who received locoregional treatments at Beijing You'an Hospital, Capital Medical University, from January 2012 to December 2019 were prospectively enrolled. Patients admitted from 2012 to 2015 were assigned to the training cohort (n = 335), while 2016 to 2019 were in the validation cohort (n =183). The clinical and pathological features of patients were collected, and independent risk factors were identified using univariate and multivariate Cox regression analysis as a basis for developing a nomogram. The performance of the nomogram was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the training and validation cohorts.

Results

The content of the nomogram includes gender, tumor number, tumor size, lymphocyte, direct bilirubin (DBIL), gamma-glutamyl transferase (GGT), and prealbumin. The C-index (0.717 and 0.752) and 1-, 3-, and 5-year AUCs (0.721, 0.825, 0.845, and 0.740, 0.868, 0.837) of the training and validation cohorts proved the good predictive performance of the nomogram. Calibration curves and DCA curves suggested accuracy and net clinical benefit rates. The nomogram enabled to classify of patients with dynamic changes in AFP into three groups according to the risk of recurrence: low risk, intermediate risk, and high risk. There was a statistically significant difference in RFS between the three groups in the training and validation cohorts (P<0.001).

Conclusion

The nomogram developed and validated in this study had good predictive power for patients with dynamic changes in AFP.

Circulating biomarkers for diagnosis and management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver cancer, but the prognosis of HCC patients is poor due to the difficulty of early diagnosis and high recurrence rate. Therefore, it is particularly important to seek effective methods for early diagnosis and early recurrence monitoring after treatment. Circulating biomarkers play an important role in the diagnosis, progression monitoring, and prognosis evaluation of HCC. In recent years, with the discovery of a variety of new biomarkers, the development of biomarkers-related models, and the emergence of liquid biopsy technology, the diagnosis and treatment of HCC have been greatly improved. This article reviews the latest research advances of biomarkers in the diagnosis and treatment of HCC, aiming to provide new ideas for improving the prognosis of HCC patients.

FKBP11 upregulation promotes proliferation and migration in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer related deaths world over. Early diagnosis and effective treatment monitoring significantly improves patients' outcomes. FKBP11 gene is highly expressed in HCC and could play a role in its development, early diagnosis and treatment.

OBJECTIVE

This study aimed to evaluate the expression of FKBP11 in HCC, its correlation with patients' clinical characteristics and potential role in HCC development.

METHODS

Expression was determined by bioinformatics analysis, quantitative real-time PCR, western blot, and immunohistochemistry. CCK-8, Transwell and wound healing assays were used to investigate involvement in HCC development.

RESULTS

FKBP11 was significantly upregulated in HCC cells, tissues and blood (all p< 0.001). Its receiver operator characteristic (ROC) curve had an AUC of 0.864 (95% CI: 0.823-0.904), at a sensitivity of 0.86 and specificity of 0.78 indicating a good diagnostic potential in HCC. Its expression was markedly reduced after surgery (p< 0.0001), indicating a potential application in HCC treatment follow-up. Knockdown of FKBP11 in HCC cells attenuated proliferation and migration, suggesting a possible role in HCC pathogenesis.

CONCLUSION

This study thus found that FKBP11 is upregulated in HCC, and the upregulation promotes HCC development. FKBP11 levels are significantly reduced post-surgery and could be a potential diagnostic and prognostic marker for HCC.

Integrated Bioinformatics and Experimental Analysis Identified TRIM28 a Potential Prognostic Biomarker and Correlated with Immune Infiltrates in Liver Hepatocellular Carcinoma

Background

Since the 1970s, liver hepatocellular carcinoma (LIHC) has experienced a constant rise in incidence and mortality rates, making the identification of LIHC biomarkers very important. Tripartite Motif-Containing 28 (TRIM28) is a protein-coding gene which encodes the tripartite motif-containing proteins (TRIMs) family and is associated with specific chromatin regions. TRIM28 expression and its prognostic value and impact on the immune system in LIHC patients are being investigated for the first time.

Methods

The TRIM28 expression data from TCGA database was used to analyze TRIM28 expression, clinicopathological information, gene enrichment, and immune infiltration and conduct additional bioinformatics analysis. R language was used for statistical analysis. TIMER, CIBERSORT, and ssGSEA were used to assess immune responses of TRIM28 in LIHC. Next, the results were validated using GEPIA, ROC analysis, and immunohistochemical staining pictures from the THPA. GSE14520, GSE63898, and GSE87630 datasets were analyzed using ROC analysis to further evaluate TRIM28's diagnostic value. To ultimately determine TRIM28 expression, we performed qRT-PCR (quantitative real-time polymerase chain reaction).

Results

High TRIM28 expression level was associated with T classification, pathologic stage, histologic grade, and serum AFP levels. In patients with LIHC, TRIM28 was an independent risk factor for a poor prognosis. The pathways ligand-receptor interaction, which is critical in LIHC patients, were closely associated with TRIM28 expression, and the function of DC could be suppressed by overexpression of TRIM28. As a final step, our results were validated by GEO data and qRT-PCR.

Conclusions

TRIM28 will shed new light on LIHC mechanisms. As an effective diagnostic and intervention tool, this gene will be able to diagnose and treat LIHC at an early stage.

Modified preoperative score to predict disease-free survival for hepatocellular carcinoma patients with surgical resections

BACKGROUND

No prognostic models specific to hepatocellular carcinoma patients receiving surgical resection have been considered strong and convincing enough for survival prediction thus far, and there are no models including only preoperative predictors. We derived a nomogram to predict disease-free survival in a previous study.

AIM

To simplify our score and compare research outcomes among other scoring systems.

METHODS

We retrospectively reviewed data from 1106 patients with hepatocellular carcinoma who underwent liver resection at the Linkou Chang Gung Memorial Hospital between April 2003 and December 2012. Multivariate analyses were conducted to identify the significant survival predictors. Homogeneity, Harrell’s C-index, and Akaike information criterion were compared between our score, AJCC 8^th edition, Tokyo score, and Taipei Integrated Scoring System (TTV-CTP-AFP model).

RESULTS

Among the 1106 patients, 731 (66.1%) had tumor recurrence at a median follow-up of 83.9 mo. Five risk factors were identified: platelet count, albumin level, indocyanine green retention rate, multiplicity, and radiologic total tumor volume. Patients were divided into three risk groups, and the 5-year survival rates were 61.7%, 39%, and 25.7%, respectively. The C-index was 0.617, which was higher than the Tokyo score (0.613) and the Taipei Integrated Scoring System (0.562) and equal to the value of the AJCC 8^th edition (0.617).

CONCLUSION

The modified score provides an easier method to predict survival. Appropriate treatment can be planned preoperatively by dividing patients into risk groups.

A Prognosis Marker Dynein Cytoplasmic 1 Heavy Chain 1 Correlates with EMT and Immune Signature in Liver Hepatocellular Carcinoma by Bioinformatics and Experimental Analysis

Background

Liver hepatocellular carcinoma (LIHC) has had a continuous increase in incidence and mortality rates over the last 40 years. Dynein Cytoplasmic 1 Heavy Chain 1 (DYNC1H1) is a protein coding gene which encodes the cytoplasmic dynein heavy chain family. This is the first investigation into the expression of DYNC1H1 and its mechanisms of action in LIHC patients.

Methods

Based on the DYNC1H1 expression data from the TCGA database, we performed the DYNC1H1 expression, clinicopathological data, gene enrichment, and immune infiltration analysis. TIMER and CIBERSORT were used to assess immune responses of DYNC1H1 in LIHC. GEPIA, K-M survival analysis, and immunohistochemical staining pictures from the THPA were used to validate the results. In order to evaluate the diagnostic value of DYNC1H1, GEO datasets were analyzed by using ROC analysis. And quantitative real-time polymerase chain reaction was also carried out to evaluate the expression of DYNC1H1.

Results

DYNC1H1 expression levels were associated with T classification, pathologic stage, histologic grade, and serum AFP levels. DYNC1H1 is an independent factor for a poor prognosis in patients with LIHC. Further study showed that high expression of DYNC1H1 was enriched in epithelial–mesenchymal transition (EMT) and the TGF β signaling pathway by GSEA analysis enrichment, indicating that DYNC1H1 might play a key role in the progression of CRC through EMT and immune response, which also had been validated by the experimental assays.

Conclusions

DYNC1H1 will provide a novel and important perspective for the mechanisms of LIHC by regulating EMT. This gene will be able to act as an efficacious tool for the early diagnosis and effective intervention of LIHC.

Prognostic role of multiparameter MRI and radiomics in progression of advanced unresectable hepatocellular carcinoma following combined transcatheter arterial chemoembolization and lenvatinib therapy

BACKGROUND

Current study aims to determine the prognostic value of Multiparameter MRI after combined Lenvatinib and TACE therapy in patients with advanced unresectable hepatocellular carcinoma (HCC).

METHODS

A total of 61 HCC patients with pre-treatment Multiparameter MRI in Sun Yat-sen University Cancer Center from January 2019 to March 2021 were recruited in the current study. All patients received combined Lenvatinib and TACE treatment. Potential clinical and imaging risk factors for disease progression were analyzed using Cox regression model. Each patient extracts signs from the following 7 sequences: T1WI, T1WI arterial phase, T1WI portal phase, T1WI delay phase, T2WI, DWI (b = 800), ADC.1782 quantitative 3D radiomic features were extracted for each sequence, A random forest algorithm is used to select the first 20 features by feature importance. 7 logit regression-based prediction model was built for seven sequences based on the selected features and fivefold cross validation was used to evaluate the performance of each model.

RESULTS

CR, PR, SD were reported in 14 (23.0%), 35 (57.4%) and 7 (11.5%) patients, respectively. In multivariate analysis, tumor number (hazard ratio, HR = 4.64, 95% CI 1.03-20.88), and arterial phase intensity enhancement (HR = 0.24, 95% CI 0.09-0.64; P = 0.004) emerged as independent risk factors for disease progression. In addition to clinical factors, the radiomics signature enhanced the accuracy of the clinical model in predicting disease progression, with an AUC of 0.71, a sensitivity of 0.99%, and a specificity of 0.95.

CONCLUSION

Radiomic signatures derived from pretreatment MRIs could predict response to combined Lenvatinib and TACE therapy. Furthermore, it can increase the accuracy of a combined model for predicting disease progression. In order to improve clinical outcomes, clinicians may use this to select an optimal treatment strategy and develop a personalized monitoring protocol.

Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data

Aim

To predict survival time of Korean hepatocellular carcinoma (HCC) patients using multi-center data as a foundation for the development of a predictive artificial intelligence model according to treatment methods based on machine learning.

Methods

Data of patients who underwent treatment for HCC from 2008 to 2015 was provided by Korean Liver Cancer Study Group and Korea Central Cancer Registry. A total of 10,742 patients with HCC were divided into two groups, with Group I (2920 patients) confirmed on biopsy and Group II (5562 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. The data were modeled according to features of patient clinical characteristics. Features effective in predicting survival rate were analyzed retrospectively. Various machine learning methods were used.

Results

Target was overall survival time, which divided into approximately 60 months (= /< 60 m, > 60 m). Target distribution in Group I (total 514 samples) was 28.8%: (148 samples) less than 60 months, 71.2% (366 samples) greater than 60 months, and in Group II (total 757 samples) was 66.6% (504 samples) less than 60 months, 33.4% (253 samples) greater than 60 months. Using NG Boost method, its accuracy was 83%, precision 84%, sensitivity 95%, and F1 score 89% for more than 60 months survival time in Group I with surgical resection. Moreover, its accuracy was 79%, precision 82%, sensitivity 87%, and F1 score 84% for less than 60 months survival time in Group II with TACE. The feature importance with gain criterion indicated that pathology, portal vein invasion, surgery, metastasis, and needle biopsy features could be explained as important factors for prediction in case of biopsy (Group I).

Conclusion

By developing a predictive model using machine learning algorithms to predict prognosis of HCC patients, it is possible to project optimized treatment by case according to liver function and tumor status.

A nomogram for predicting the risk of postoperative recurrence of hepatitis B virus-related hepatocellular carcinoma in patients with high preoperative serum glutamyl transpeptidase

BACKGROUND

Recurrence is a major risk factor affecting the postoperative survival of patients with hepatocellular carcinoma (HCC), especially those with high preoperative serum γ-glutamyl transpeptidase (GGT) levels. This study had the aim of developing a personalized predictive tool to accurately determine the risk of postoperative recurrence of hepatitis B-virus (HBV)-related HCC in patients with high preoperative serum GGT levels.

METHODS

Patients who underwent curative liver resection of HBV-related HCC and had high preoperative GGT levels were consecutively enrolled between 2008 and 2011. Prognostic indicators for recurrence were determined using Cox regression analysis. A nomogram was then developed and assessed by integrating the independent risk factors into the model.

RESULTS

A total of 603 eligible patients were included. The final nomogram for predicting HCC recurrence in patients with high preoperative GGT levels consisted of five independent prognostic factors: α-fetoprotein (AFP), HBV-DNA, satellite nodules, microvascular invasion, and tumor grade. The C-index of the nomogram for predicting recurrence was 0.759, and validation showed high accuracy and discriminatory.

CONCLUSIONS

The predictive nomogram developed and validated in this study performs well in predicting postoperative recurrence of HBV-related HCC in patients with high preoperative GGT levels. It can provide personalized assessments to inform the development of surveillance strategies and allows patients with a high risk of recurrence to be selected for further adjuvant treatment.

A New Prognostic Algorithm Predicting HCC Recurrence in Patients With Barcelona Clinic Liver Cancer Stage B Who Received PA-TACE

Background

Postoperative adjuvant transcatheter arterial chemoembolization (PA-TACE) is effective in preventing the recurrence of hepatocellular carcinoma (HCC) in patients treated with surgery. However, there is a lack of reports studying the risk factors associated with recurrence in HCC patients who received PA-TACE. In this study, we identified the independent risk factors for recurrence of HCC patients who received PA-TACE. We also developed a novel, effective, and valid nomogram to predict the individual probability of recurrence, 1, 3, and 5 years after PA-TACE.

Methods

A retrospective study was performed to identify the independent risk factors for recurrence of HCC in a group of 502 patients diagnosed in stage B based on the Barcelona Clinic Liver Cancer (BCLC) evaluation system for HCC that underwent curative resections. Then, subgroup analysis was performed for 184 patients who received PA-TACE, who were included in the training cohort. The other 147 HCC patients were included in a validation cohort. A recurrence-free survival (RFS)-predicting nomogram was constructed, and results were assessed using calibration and decision curves and a time-dependent AUC diagram.

Results

PA-TACE was shown to be a significant independent prognostic value for patients with BCLC stage B [p < 0.001, hazard ratio (HR) = 0.508, 95% CI = 0.375–0.689 for OS, p = 0.002; HR = 0.670, 95%CI = 0.517–0.868 for RFS]. Alpha fetoprotein (AFP), tumor number, tumor size, microvascular invasion (MVI), and differentiation were considered as independent risk factors for RFS in the training cohort, and these were further confirmed in the validation cohort. Next, a nomogram was constructed to predict RFS. The C-index for RFS in the nomogram was 0.721 (95% CI = 0.718–0.724), which was higher than SNACOR, HAP, and CHIP scores (0.587, 0.573, and 0.607, respectively). Calibration and decision curve analyses and a time-dependent AUC diagram were used. Our nomogram showed stronger performance than these other nomograms in both the training and validation cohorts.

Conclusions

HCC patients diagnosed as stage B according to BCLC may benefit from PA-TACE after surgery. The RFS nomogram presented here provides an accurate and reliable prognostic model to monitor recurrence. Patients with a high recurrence score based on the nomogram should receive additional high-end imaging exams and shorter timeframes in between follow-up visits.

Combination of serum paraoxonase/arylesterase 1 and antithrombin-III is a promising non-invasion biomarker for discrimination of AFP-negative HCC versus liver cirrhosis patients

OBJECTIVE

α-fetoprotein is often used in the diagnosis of hepatocellular carcinoma (HCC). However, there are currently less efficient and highly specific biomarkers to distinguish AFP-negative HCC from liver cirrhosis (LC) patients.

PATIENTS AND METHODS

We retrospectively analyzed the data of patients who were treated in our hospitals. iTRAQ coupled with mass spectrometry was used to identify candidate serum proteins in a discovery set (n = 36) including AFP-negative HCC and LC patients. After Western blot detection, potential serum biomarkers were confirmed using ELISA in a validation set (n = 90). The diagnostic performance of the selected proteins was assessed using receiver operating characteristic (ROC).

RESULTS

PON1 and ATIII were selected as target proteins and were significantly higher in LC than those in AFP-negative HCC patients as validated by Western blot and ELISA, which was consistent with the result of iTRAQ. The AUC was 0.848 as PON1 and ATIII were combined (sensitivity: 80.0%; specificity: 73.3%), and performed much better than that of a single biomarker.

CONCLUSION

These findings suggest that PON1 and ATIII have the potential to serve as effective biomarkers for distinguishing AFP-negative HCC from cirrhosis.

Predicting survival time of Korean hepatocellular carcinoma patients using the Cox proportional hazards model: a retrospective study based on big data analysis

AIM

To predict survival time of Korean hepatocellular carcinoma (HCC) patients by analyzing big data using Cox proportional hazards model.

METHODS

Big data of the patients who underwent treatment for HCC from 2008 to 2015, provided by Korea Central Cancer Registry, National Cancer Center, and Ministry of Health and Welfare, were analyzed. A total of 10 742 patients with HCC were divided into two groups, with Group I (3021 patients) confirmed on biopsy and Group II (5563 patients) diagnosed as HCC according to HCC diagnostic criteria as outlined in Korean Liver Cancer Association guidelines. Univariate and multivariate Cox regression analyses were performed to identify independent risk factors of recurrence after treatment and survival status.

RESULTS

A total of 3021 patients in Group I and 5563 patients in Group II were included in the study and the difference in survival time between the two groups was statistically significant (P < 0.05). Recurrence was only included in intrahepatic cases, and the rates were 21.2 and 19.8% while the periods from the first treatment to recurrence were 15.57 and 14.19 months, respectively. Age, diabetes, BMI, platelet, alpha-fetoprotein, histologic tumor maximum size, imaging T stage, presence of recurrence, and duration of recurrence were included in multivariate analysis.

CONCLUSION

By using nationwide, multicenter big data, it is possible to predict recurrence rate and survival time which can provide the basis for treatment response to develop a predictive program.

Evaluation of serum AFP and DCP levels in the diagnosis of early-stage HBV-related HCC under different backgrounds

OBJECTIVE

This study compared the diagnostic performance of alpha-fetoprotein (AFP) and des-gamma-carboxyprothrombin (DCP) in early-stage hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) under different backgrounds.

METHODS

Patients were enrolled and divided in four groups: chronic HBV infection (CHB), liver cirrhosis (LC), early-stage CHB-HCC, and early-stage LC-HCC. Serum AFP and DCP levels were measured. Receiver-operating characteristic (ROC) curve and area under the curve (AUC) analyses were applied to compare the diagnostic performance of DCP and AFP for HCC.

RESULTS

In total, 200 patients were enrolled, including 48, 64, 33, and 55 patients with CHB, LC, CHB-HCC, and LC-HCC, respectively. ROC curve analysis revealed that the AUCs of AFP, DCP, and their combination in differentiating early-stage LC-HCC from LC were 0.776, 0.758, and 0.786, respectively. The values of these markers in discriminating early-stage CHB-HCC from CHB were 0.828, 0.731, and 0.862, respectively.

CONCLUSIONS

DCP was inferior to AFP in differentiating early-stage CHB-HCC from CHB. However, AFP and DCP displayed similar performance in distinguishing early-stage LC-HCC and LC.

Homer2 and Homer3 Act as Novel Biomarkers in Diagnosis of hepatitis B virus-induced Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common causes of cancer‐related mortality worldwide. Early detection of HCC can significantly improve patients' outcomes. An increasing number of studies have validated that Homer is dysregulated in cancers and may serve as diagnostic markers. In the present study, we investigated the expression profile and diagnostic significance of Homer2 and Homer3 in hepatitis B virus-induced HCC (HBV-HCC).

Methods: Quantitative real-time PCR (QRT-PCR), western blot analysis and immunohistochemistry analysis.

Results: Homer2 and Homer3 were downregulated in HCC. The expression of Homer2 was associated with tumor differentiation grade (P= 0.012) and total protein (TP) level (P= 0.032). Homer3 was related to tumor size (P= 0.010), tumor nodes (P= 0.026) and γ-glutamyl transferase (GGT) level (P= 0.001). The receiver operating characteristic curve analyses indicated that the combination of Homer2, Homer3 and AFP possessed a high accuracy (AUC=0.900) to diagnose HCC cases from healthy controls.

Conclusion: Our data indicated that Homer2 and Homer3 were downregulated in HCC and might be potential diagnostic marker for HCC.

Analysis of Serum Paraoxonase 1 Using Mass Spectrometry and Lectin Immunoassay in Patients With Alpha-Fetoprotein Negative Hepatocellular Carcinoma

The diagnosis of AFP (alpha-fetoprotein)-negative HCC (hepatocellular carcinoma) mostly relies on imaging and pathological examinations, and it lacks valuable and practical markers. Protein N-glycosylation is a crucial post-translation modifying process related to many biological functions in an organism. Alteration of N-glycosylation correlates with inflammatory diseases and infectious diseases including hepatocellular carcinoma. Here, serum N-linked intact glycopeptides with molecular weight (MW) of 40–55 kDa were analyzed in a discovery set (n = 40) including AFP-negative HCC and liver cirrhosis (LC) patients using label-free quantification methodology. Quantitative lens culinaris agglutin (LCA) ELISA was further used to confirm the difference of glycosylation on serum PON1 in liver diseases (n = 56). Then, the alteration of site-specific intact N-glycopeptides of PON1 was comprehensively assessed by using Immunoprecipitation (IP) and mass spectrometry based ¹⁶O/¹⁸O C-terminal labeling quantification method to distinguish AFP-negative HCC from LC patients in a validation set (n = 64). Totally 195 glycopeptides were identified using a dedicated search engine pGlyco. Among them, glycopeptides from APOH, HPT/HPTR, and PON1 were significantly changed in AFP-negative HCC as compared to LC. In addition, the reactivity of PON1 with LCA in HCC patients with negative AFP was significantly elevated than that in cirrhosis patients. The two glycopeptides HAN²⁵³WTLTPLK (H5N4S2) and (H5N4S1) corresponding to PON1 were significantly increased in AFP-negative HCC patients, as compared with LC patients. Variations in PON1 glycosylation may be associated with AFP-negative HCC and might be helpful to serve as potential glycomic-based biomarkers to distinguish AFP-negative HCC from cirrhosis.

N-glycan fingerprint predicts alpha-fetoprotein negative hepatocellular carcinoma: A large-scale multicenter study

Alpha-fetoprotein (AFP)-negative hepatocellular carcinoma (ANHCC) patients account for more than 30% of the whole entity of HCC patients and are easily misdiagnosed. This three-phase study was designed to find and validate new ANHCC N-glycan markers which identified from The Cancer Genome Atlas (TCGA) database and noninvasive detection. Differentially expressed genes (DEGs) of N-glycan biosynthesis and degradation related genes were screened from TCGA database. Serum N-glycan structure abundances were analyzed using N-glycan fingerprint (NGFP) technology. Totally 1340 participants including ANHCC, chronic liver diseases and healthy controls were enrolled after propensity score matching (PSM). The Lasso algorithm was used to select the most significant N-glycan structures abundances. Three machine learning models [random forest (RF), support vector machine (SVM) and logistic regression (LR)] were used to construct the diagnostic algorithms. All 13N-glycan structure abundances analyzed by NGFP demonstrated significant and was enrolled by Lasso. Among the three machine learning models, LR algorithm demonstrated the best diagnostic performance for identifying ANHCC in training cohort (LR: AUC: 0.842, 95%CI: 0.784-0.899; RF: AUC: 0.825, 95%CI: 0.766-0.885; SVM: AUC: 0.610, 95%CI: 0.527-0.684). This LR algorithm achieved a high diagnostic performance again in the independent validation (AUC: 0.860, 95%CI: 0.824-0.897). Furthermore, the LR algorithm could stratify ANHCC into two distinct subgroups with high or low risks of overall survival and recurrence in follow-up validation. In conclusion, the biomarker panel consisting of 13N-glycan structures abundances using the best-performing algorithm (LR) was defined and indicative as an effective tool for HCC prediction and prognosis estimate in AFP negative subjects.

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.

A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

Background

Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC.

Methods

A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort.

Results

The C-index of nomogram1was 0.708 (95%CI: 0.673–0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606–0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690–0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691–0.813; AUC: 0.784, 95%CI: 0.709–0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively.

Conclusions

Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.

Clinical Significance of Alpha-Fetoprotein in Alpha-Fetoprotein Negative Hepatocellular Carcinoma Underwent Curative Resection

BACKGROUND

The clinical value of alpha-fetoprotein (AFP) in patients with AFP-negative (< 20 ng/ml) hepatocellular carcinoma (HCC) who underwent curative resection remained controversial.

AIMS

To investigate clinical relevance and prognostic effect of preoperative serum AFP level in this subgroup.

METHODS

A total of 1879 patients with AFP-negative HCC who underwent curative resection were included in the study. Overall survival (OS) and disease-free survival (DFS) rate were displayed by Kaplan-Meier method and compared by log-rank test. Multivariate cox proportional hazard regression analysis was used to identify the independent prognostic factors. The prognostic predictive performance was analyzed by time-dependent areas under receiver operating characteristic curve (AUC).

RESULTS

Even in AFP-negative HCC, patients with high preoperative serum AFP level tended to have multiple tumor (P < 0.001), poorer differentiation of tumor cell (P < 0.001), presence of satellite nodules (P < 0.001), and MVI (P = 0.002). Kaplan-Meier analysis showed the adverse impact of AFP level on prognosis, especially for DFS. Multivariate analysis identified AFP as the independent unfavorable factor for OS and DFS (P < 0.001 for both). Time-dependent AUC analysis showed that the combination with AFP could improve the prognostic predictive performance of 8th AJCC and BCLC staging system.

CONCLUSIONS

AFP was still the surrogate of aggressive behavior of HCC and independent prognostic factor for patients with AFP-negative HCC underwent curative resection. Even combining with such a low level of AFP could significantly improve the predictive performance of conventional staging system.

miR-23b-3p Plays an Oncogenic Role in Hepatocellular Carcinoma

BACKGROUND

Reports show miR-23b to be a cancer-related biomarker in various cancer types. Interestingly, it has a dual role of oncogenic and tumor-suppressive functions, depending on the cancer type. This study focused on the unknown association of miR-23b-3p with hepatocellular carcinoma (HCC).

METHODS

Expression of miR-23b-3p was measured in nine HCC cell lines and 125 resected human HCC samples by TaqMan microRNA assays. To detect its downstream target, miR-23b-3p mimic and inhibitor constructs were transfected and analyzed.

RESULTS

HepG2, a high miR-23b-3p-expressing cell line, was transfected with a miR-23b-3p inhibitor construct, whereas SK-Hep1, a low miR-23b-3p-expressing cell line, was transfected with a mimic construct. Proliferation of HCC cells was activated by miR-23b-3p overexpression and diminished by its knockdown. Then, 125 clinical HCC samples were examined to measure miR-23b-3p expression. Tumor expression of miR-23b-3p was upregulated in 48 cases (38%) and downregulated in 77 cases (62%). The upregulated cases were correlated with elderly patients (P = 0.015). These patients also showed significantly poor overall survival [hazard ratio (HR), 3.10; 95% conflidence interval (CI), 1.57-6.29; P = 0.001] in a multivariate analysis. Furthermore, mitochondrial metabolism-related genes (MICU3 and AUH) were detected as specific binding targets.

CONCLUSION

The study showed that miR-23b-3p functions as an oncogenic microRNA in HCC cell lines. Its overexpression in resected HCC tissues was a significant prognostic factor of overall survival. Both MICU3 and AUH may be candidate gene targets of miR-23b-3p.

Single Large Nodule (>5 cm) Prognosis in Hepatocellular Carcinoma: Kinship with Barcelona Clinic Liver Cancer (BCLC) Stage A or B?

Background

The aim of the present study was to evaluate the prognosis among patients with a single large hepatocellular carcinoma (HCC) >5 cm compared with other patients in Barcelona Clinic Liver Cancer (BCLC) stage A or stage B.

Material/Methods

Data on patients with BCLC stage A/B HCC were collected between 2008 and 2012. BCLC stage A was subclassified as A1 (single tumor, 2–5 cm, or 2–3 nodules ≤3 cm), or A2 (single tumor >5 cm). Overall survival (OS) was evaluated and compared.

Results

Among 1005 patients with HCC, 455 were stage A1, 188 were stage A2, and 362 were stage B. The OS of stage A2 patients was significantly worse than that of stage A1 patients (median survival, 30.6 vs. 43.2 months, p<0.001), and was similar to that of stage B patients (median survival, 30.6 vs. 33.5 months, p=0.519). After surgical resection, OS was statistically distinct between stage A1+A2 and B (median survival, 51.2 vs. 36.0 months, p=0.001), and between stage A1 and A2+B (median survival, 54.4 vs. 36.8 months, p<0.001). In contrast, when treated by transarterial chemoembolization, there was no difference in OS between patients with stage A1+A2 HCC and patients with stage B HCC (median survival, 32.4 vs. 31.3 months, p=0.310), whereas patients with stage A1 HCC showed a significantly more favorable OS than those with stage A2+B HCC (median survival, 39.6 vs. 31.8 months, p=0.023). On multivariable analysis, the groupings that showed significantly different associations with OS were BCLC stage A2+B vs. A1 (hazard ratio 1.6, p<0.001) rather than stage B vs. A1+A2.

Conclusions

Patients with solitary HCC >5 cm had a comparable survival with BCLC stage B. HCC >5 cm should therefore be classified as an intermediate stage.

Bioinformatic Analysis to Identify a Multi-mRNA Signature for the Prediction of Metastasis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) with metastasis indicates worse prognosis for patients. However, the current methods are insufficient to accurately predict HCC metastasis at early stage. Based on the expression profiles of three Gene Expression Omnibus datasets, the differentially expressed genes associated with HCC metastasis were screened by online analytical tool GEO2R and weighted gene co-expression network analysis. Second, a risk score model including 27-mRNA was established by univariate Cox regression analyses, time-dependent ROC curves and least absolute shrinkage and selection operator Cox regression analysis. Then, we validated the model in cohort The Cancer Genome Atlas-liver hepatocellular carcinoma and analyzed the functions and key signaling pathways of the genes associated with the risk score model. According to the risk score model, patients were divided into two subgroups (high risk and low risk groups). The metastasis rate between two subgroups was significantly different in training cohort (p < 0.0001, hazard ratio [HR]: 10.3, confidence interval [95% CI]: 6.827-15.55) and external validation cohort (p = 0.0008, HR: 1.768, 95% CI: 1.267-2.467). Multivariable analysis showed that the risk score model was superior to and independent of other clinical factors (such as tumor stage, tumor size, and other parameters) in predicting early HCC metastasis. Moreover, the risk score model could predict the overall survival of patients with HCC. Finally, most of 27-mRNA were enriched in exosome and membrane bounded organelle, and these were involved in transportation and metabolic biological process. Protein-protein interaction network analysis showed most of these genes might be key genes affecting the progression of HCC. In addition, 3 genes of 27-mRNA were also differentially expressed in peripheral blood mononuclear cell. In conclusion, by using two combined methods and a broader of HCC datasets, our study provided reliable and superior predictive model for HCC metastases, which will facilitate individual medical management for these high metastatic risk HCC patients.

Models to predict disease-free survival for hepatocellular carcinoma patients with surgical resections

BACKGROUND AND OBJECTIVES

Precise prognostic prediction for an individual hepatocellular carcinoma (HCC) patient before and after liver resection is important. We aimed to establish simple prognostic models to predict disease-free survival (DFS) for these patients.

METHODS

Six hundred and ninety-eight HCC patients with liver resections were reviewed. Preoperative (model 1) and postoperative (model 2) nomogram-based scoring systems were constructed by multivariate analyses, and DFS was estimated.

RESULTS

Among 698 patients, 490 (70.2%) patients had tumor recurrence at a median follow-up of 84.4 months. Risk factors of tumor recurrence in model 1 included viral hepatitis, platelet count, albumin, indocyanine green retention rate, multiplicity of tumor, and radiologic total tumor volume (TTV). Prognostic variables identified in model 2 were viral hepatitis, platelet count, multiplicity of tumor, cirrhosis, microvascular invasion, and pathologic TTV. By nomogram in model 1, the patients were classified into three groups with 5-year DFS of 61.0%, 35.7%, and 21.1%, respectively (P < .0001). In model 2, the patients were divided into five groups with 5-year DFS of 58.0%, 43.7%, 24.0%, 15.4%, and 0.0%, respectively (P < .0001).

CONCLUSION

Based on nomogram models, DFS for the patients who had liver resection for HCC can be predicted before liver resection and re-assessed after liver resection.

Prognostic Nomogram for Hepatitis B Virus-related Hepatocellular Carcinoma With Adjuvant Transarterial Chemoembolization After Radical Resection

OBJECTIVES

The objective of this study was to establish a reliable and effective nomogram for predicting prognosis of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) with postoperative adjuvant transarterial chemoembolization (TACE).

PATIENTS AND METHODS

A derivation cohort of 370 HCC patients treated with postoperative TACE in the Eastern Hepatobiliary Surgery Hospital from January 2009 to December 2012 were retrospectively analyzed. Univariate and multivariate analysis were performed by Cox regression and independent prognostic factors for overall survival were determined to construct the nomogram. Concordance index (C-index), calibration curve and decision curve analysis were performed to evaluate the capability of the nomogram and the established nomogram was compared with TNM stage and Barcelona Clinic Liver Cancer (BCLC) stage to identify the superior model. The results were validated in a validation cohort of 123 HCC patients in the same center.

RESULTS

Multivariate analysis indicated that γ-glutamyl transferase, α-fetoprotein, tumor number, tumor size, satellite lesions, microvascular invasion, and HBV-DNA were independent prognostic factors for overall survival in the derivation cohort, and all these factors were selected into the nomogram. The C-index was 0.755 for survival prediction of the nomogram, which was significantly higher than the TNM stage (0.636, P<0.001) and BCLC stage (0.594, P<0.001). A fair uniformity and a superior net benefit with wide range threshold probabilities were showed in the calibration curves and decision curve analysis. In the validation cohort, the C-index of the nomogram (0.785) also had a higher predictive accuracy than TNM stage (0.744, P=0.019) and BCLC stage (0.616, P<0.001).

CONCLUSIONS

The nomogram with accurate and reasonable performance was proposed for predicting survival of HBV-related HCC with postoperative adjuvant TACE.

Novel Prognostic Nomograms for Predicting Early and Late Recurrence of Hepatocellular Carcinoma After Curative Hepatectomy

Aim

Hepatectomy is the main curative method for patients with hepatocellular carcinoma (HCC) in China. Unfortunately, high recurrence rate after hepatectomy poses negative impact on the prognosis of patients. This study aimed to develop prognostic nomograms to predict early recurrence (ER) and late recurrence (LR) of HCC after curative hepatectomy.

Patients and Methods

Total of 318 HCC patients undergoing curative hepatectomy from January 2012 to January 2018 were retrospectively recruited. Potential risk factors that were significant for predicting ER and LR in univariate analysis were selected for multivariate survival model analysis using the backward stepwise method. Risk factors identified in multivariate analysis were used to develop nomograms to predict ER and LR. The nomogram was internally validated using 2,000 bootstrap samples from 75% of the original data.

Results

Among 318 patients, 164 showed postoperative recurrence, of which 140 and 24 had ER (≤2 years) and LR (>2 years), respectively. Multivariate analysis showed that age, Hong Kong Liver Cancer Stage, albumin-bilirubin, METAVIR fibrosis grade, and microvascular invasion were risk factors of ER for HCC after curative hepatectomy. The AUC of the ROC curve for ER in the development set (D-set) was 0.888 while that in the validation set (V-set) was 0.812. Neutrophil/lymphocyte ratio and glypican-3 (+) were risk factors for LR in HCC patients after curative hepatectomy. The AUC of the ROC curve for LR predictive nomogram that integrated all independent predictors was 0.831. The AUC of the ROC curve for LR in the D-set was 0.833, while that for LR in the V-set was 0.733. The C-index and AUC of ROC for the proposed nomograms were more satisfactory than three conventional HCC staging systems used in this study.

Conclusion

We developed novel nomograms to predict ER and LR of HCC patients after curative hepatectomy for clinical use to individualize follow-up and therapeutic strategies.

Nomograms to predict the long-time prognosis in patients with alpha-fetoprotein negative hepatocellular carcinoma following radical resection

Background

To develop and validate nomograms that can be used to predict outcomes in individuals suffering alpha‐fetoprotein (AFP) negative hepatocellular carcinoma (HCC) after radical resection.

Methods

A total of 509 AFP‐negative HCC patients who received hepatectomy between January 2009 and March 2013 in our center were randomized into training and validation cohorts. Nomograms for both overall and recurrence‐free survival (OS and RFS, respectively) were established based on the predictors in the training cohort. Nomograms performance and discriminative power were assessed with concordance index (C‐index) values and decision curve analyses (DCA). The results were validated in the validation cohort.

Results

Alkaline phosphatase, liver cirrhosis, tumor size, satellite lesions, microvascular invasion, and Edmondson‐Steiner grade were significantly linked to OS and RFS. Sex and tumor number were additional predictors for RFS. The OS nomogram had a C‐index value of 0.742, which was better than that for the AJCC eighth edition (0.632), BCLC system (0.553), and JIS score (0.557) (all P < .001). The RFS nomogram C‐index was 0.669, which was also superior to that of the AJCC eighth (0.608), BCLC stage (0.554), JIS score (0.551), and model of Gan et al (0.636) (P < .05 for all). Calibration curves indicated a good agreement between observed actual outcomes and predicted values. Kaplan‐Meier curves and DCA indicated that nomograms were powerful in discrimination and clinical usefulness. These results were supported by the validation cohort.

Conclusions

These nomograms presented more accurate prognostic prediction in patients with AFP‐negative HCC after hepatectomy.

Clinical value of LHPP-associated microRNAs combined with protein induced by vitamin K deficiency or antagonist-II in the diagnosis of alpha-fetoprotein-negative hepatocellular carcinoma

Background

Alpha‐fetoprotein (AFP) has received extensive attention in the differential diagnosis of hepatocellular carcinoma (HCC), especially for AFP‐negative HCC (AFP‐NHCC). The current study aimed to explore the value of targeted regulation of LHPP expression‐related microRNAs (miRs) and protein induced by vitamin K deficiency or antagonist‐II (PIVKA‐II) in the differential diagnosis of AFP‐NHCC.

Methods

A retrospective study was conducted on a testing set—including 214 AFP‐NHCC patients, 200 cirrhosis, and 210 controls, and a validation set—including 140 AFP‐NHCC patients, 134 cirrhosis, and 128 controls recruited from The First Affiliated Hospital of Hunan Normal University. Serum miRs were examined using quantitative real‐time PCR method. Serum PIVKA‐II was measured by enzyme‐linked immunosorbent assay.

Results

Compared with adjacent tissues, LHPP protein levels in cancer tissues were significantly decreased (P < .05). Predictive software and dual‐luciferase reporter assays showed that miR‐363‐5p and miR‐765 can target LHPP expression. Serum miR‐363‐5p, miR‐765, and PIVKA‐II levels were significantly higher in AFP‐HCC patients than in cirrhosis and controls. A logistic regression model combining miR‐363‐5p, miR‐765, and PIVKA‐II was performed. This model presented a high discriminating value (AUC: 0.930, sensitivity/specificity: 79.4%/95.4%) than any single indicator. In the validation set, this model still showed a high discriminating value (AUC: 0.936, sensitivity/specificity: 83.6%/94.7%).

Conclusion

Current model combining serum miR‐363‐5p, miR‐765, and PIVKA‐II has potential significance for diagnosis of AFP‐NHCC.

Identification of Key Hydroxymethylated Genes and Transcription Factors Associated with Alpha-Fetoprotein-Negative Hepatocellular Carcinoma

DNA hydroxymethylation is one of the major epigenetic mechanisms mediating the development of several human cancers. This study aimed to identify key hydroxymethylated genes and transcription factors (TFs) associated with alpha-fetoprotein (AFP)-negative hepatocellular carcinoma (HCC) using whole-genome DNA hydroxymethylation profiling. A total of 615 differentially hydroxymethylated regions (DHMRs) were identified from AFP-negative HCC tissues compared to adjacent normal tissues. DHMR-associated genes were significantly enriched in gene ontology functions associated with actin binding, cell leading edge, and blood vessel morphogenesis and pathways such as MAPK signaling pathway, neuroactive ligand-receptor interaction, and axon guidance. Moreover, protein-protein interaction (PPI) network analysis showed that PH domain and leucine-rich repeat protein phosphatase 1 (PHLPP1) and SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily A, member 2 (SMARCA2) had higher degrees and were hub nodes. Furthermore, TF prediction analysis showed that TFs, such as nuclear factor I C (NFIC) and GATA binding protein 3 (GATA3), regulated many DHMR-associated genes. Our findings reveal that key hydroxymethylated genes such as PHLPP1 and SMARCA2, as well as TFs such as NFIC and GATA, may be involved in the development of AFP-negative HCC. These molecules may be potential biomarkers for AFP-negative HCC.

Identification of novel blood-based HCC-specific diagnostic biomarkers for human hepatocellular carcinoma

INTRODUCTION

Hepatocellular carcinoma (HCC) ranks fourth in global cancer mortality, accounting for 8.2% of all cancer deaths. Early detection of HCC has a significant impact on clinical outcomes. The aim of this study was to identify blood-based biomarkers which are HCC-specific.

METHODS

Comprehensive gene expression raw data of purified RNA of peripheral blood mononuclear cells (PBMC) was downloaded from GEO and was then analyzed. Differentially expressed genes (DEGs) in HCC were screened and the method of weighted gene co-expression network analysis was applied to identify candidate blood-based biomarkers associated with HCC.

RESULTS

Three modules closely related to HCC were screened using WGCNA. Nuclear localization signal (NLS)-bearing protein import into nucleus biological process was the most significant enriched physiological process identified by MCODE, and 3 genes (DICER1, GMPS and NCOR1) were selected as biomarkers.

CONCLUSION

In our study, three novel blood-based HCC-specific diagnostic biomarkers for human hepatocellular carcinoma were identified. These findings may contribute to the non-invasive detection of early HCC patients.

An imbalance between stellate cells and γδT cells contributes to hepatocellular carcinoma aggressiveness and recurrence

PURPOSE

The diagnostic potential of hepatic stellate cells (HSCs) and γδT cells for patients with hepatocellular carcinoma (HCC) and their synergistic contributions to the prognosis of these patients have not yet been investigated. The aim of this study was to elucidate the prognostic value of these cells in HCC.

METHODS

The prognostic significance of the ratio of HSCs to γδT cells (SGR) was assessed in a total of 339 HCC patients undergoing resection. The correlation between the circulating tumor cell (CTC) level and SGR in 71 HCC patients was determined using the CellSearch system. In vitro experiments were performed to validate the synergistic effects of HSCs and γδT cells on hepatoma cells.

RESULTS

Peritumoral SGR was closely associated with overall survival (OS) and recurrence-free survival (RFS) of HCC patients after resection. In the testing cohort, two nomograms incorporating the SGR were constructed for the prediction of OS and RFS. The predictive accuracy of the two nomograms was verified by the validation cohort. CTC levels were positively correlated with SGR (r = 0.479, p < 0.001). Among the patients with CTCs > 2/7.5 ml, those with a high SGR exhibited higher early recurrence rates than those with a low SGR. In vitro experiments revealed that the secretion of INF-γ, IL-17, and TNF-α from γδT cells was increased after culture with HSC-conditioned medium. In addition, γδT cells cultured with HSC-conditioned medium decreased the proliferative and invasive abilities of hepatoma cells.

CONCLUSIONS

The peritumoral SGR is related to aggressive tumor behavior and has a powerful predictive value in HCC. Early recurrence in patients with a high peritumoral SGR might be associated with high CTC levels.

A simplified prediction model for early intrahepatic recurrence after hepatectomy for patients with unilobar hepatocellular carcinoma without macroscopic vascular invasion: An implication for adjuvant therapy and postoperative surveillance

BACKGROUND

An accurate prediction model of early recurrence of hepatocellular carcinoma (HCC) after hepatectomy is important to ascertain the postoperative adjuvant treatment and surveillance.

METHODS

This is a retrospective cohort study including 1125 patients with HCC underwent curative hepatic resection. They were randomly divided into training (n = 562) and validation (n = 563) sets. Early intrahepatic recurrence within 18 months from surgery is the primary outcome. In the training set, a prediction scoring model (Recurrent Liver Cancer Score RLCS) was developed, which was legitimised in the validation set.

RESULTS

RLCS was developed based on four clinicopathologic risk factors (serum alpha fetoprotein, tumor size, multiple tumors or satellite nodules, and microvascular invasion). Low-risk and high-risk groups had statistically significant differences in early recurrence rates (18% vs. 43.8%). The 5-year recurrence-free survival rates of low risk and high risk groups were 52.9% and 27.8%, respectively. This model showed good calibration and discriminatory ability in the validation set (c-index of 0.647).

CONCLUSION

RLCS is a user-friendly prediction scoring model which can accurately predict the occurrence of early intrahepatic recurrence of HCC. It establishes the basis of postoperative adjuvant treatment and surveillance in future studies.

Competing endogenous RNA network and prognostic nomograms for hepatocellular carcinoma patients who underwent R0 resection

The prognosis of hepatocellular carcinoma (HCC) after R0 resection is unsatisfactory due to the high rate of recurrence. In this study, we investigated the recurrence-related RNAs and the underlying mechanism. The long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) expression data and clinical information of 247 patients who underwent R0 resection patients with HCC were obtained from The Cancer Genome Atlas. Comparing the 1-year recurrence group (n = 56) with the nonrecurrence group (n = 60), we detected 34 differentially expressed lncRNAs (DElncRNAs), five DEmiRNAs, and 216 DEmRNAs. Of these, three DElncRNAs, hsa-mir-150-5p, and 11 DEmRNAs were selected for constructing the competing endogenous RNA (ceRNA) network. Next, two nomogram models were constructed based separately on the lncRNAs and mRNAs that were further selected by Cox and least absolute shrinkage and selection operator regression analysis. The two nomogram models that showed a high prediction accuracy for disease-free survival with the concordance indexes at 0.725 and 0.639. Further functional enrichment analysis of DEmRNAs showed that the mRNAs in the ceRNA network and nomogram models were associated with immune pathways. Hence, we constructed a hsa-mir-150-5p-centric ceRNA network and two effective nomogram prognostic models, and the related RNAs may be useful as potential biomarkers for predicting recurrence in patients with HCC.

miR-501 acts as an independent prognostic factor that promotes the epithelial-mesenchymal transition through targeting JDP2 in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), the second common cancer, was a kind of primary liver cancer with high incidence. miR-501, identified as a novel regulator, was acted as a potential biomarker in several diseases. JDP2, acted as a repressor of AP-1 complex, was a member of the basic leucine zipper (bZIP) transcription factor family. RT-qPCR was applied to evaluate miR-501 and JDP2 expression level and we found that miR-501 was upregulated in HCC tissues and cells. miR-501 ectopic expression promoted HCC cell invasion and epithelial-mesenchymal transition (EMT), while low expression present the opposite results. JDP2 was downregulated in HCC tissues and cells, and overexpressed JDP2 facilitated HCC cell invasion and EMT. Furthermore, luciferase reporter assay indicated that JDP2 was a target of miR-501 and altered miR-501 expression the JPD2 mRNA may changed. The expression of miR-501 and JDP2 had negative connection in HCC tissues. In addition, Kaplan-Meier method revealed that miR-501 upregulation or JDP2 downregulation predicted poor prognosis in HCC patients. miR-501 promoted cell invasion and EMT by regulated JDP2 in hepatocellular carcinoma. The newly identified miR-501/JDP2 axis provides novel insight into the pathogenesis of hepatocellular carcinoma.

The prognostic significance of pretreatment serum γ-glutamyltranspeptidase in primary liver cancer: a meta-analysis and systematic review

Aim: To assess the prognostic value of the pretreatment serum γ-glutamyltranspeptidase (GGT) level in patients with primary liver cancer (PLC). Methods: Relevant studies were systematically searched online on Web of Science, PubMed, and Embase databases published until 9 October 2018. The end points were overall survival (OS), recurrence-free survival (RFS), and disease-free survival (DFS). Meta-analysis was conducted using hazard ratio (HR), and its 95% confidence interval (CI) as effect measure. Results: A total of 33 eligible studies with 9238 patients with PLC were included in this meta-analysis. The synthesized analysis showed that that higher serum GGT level was significantly related to poorer OS (HR: 1.79, 95% CI: 1.66–1.93, P<0.01), RFS (HR: 1.60, 95% CI: 1.46–1.77, P<0.01), and DFS (HR: 1.52, 95% CI: 1.33–1.73, P<0.01) of patients with PLC. Subgroup analyses demonstrated that the negative prognostic impact of higher serum GGT level on OS and RFS was still of significance regardless of ethnicity, pathological type, sample size, cut-off value, first-line treatment, and analysis type. Conclusion: The pretreatment serum GGT might be a predictive factor of poor prognosis for PLC patients.

A model combining TNM stage and tumor size shows utility in predicting recurrence among patients with hepatocellular carcinoma after resection

Objective

Hepatocellular carcinoma (HCC) recurrence is a clinical challenge. An accurate prediction system for patients with HCC is needed, since the choice of HCC treatment strategies is very important.

Patients and methods

A total of 804 patients with HCC who underwent curative resection at Sun Yat-sen University Cancer Center were included in this study. Demographics, clinicopathological data, and follow-up information were collected.

Results

A logistic regression analysis was conducted to investigate the relationships between clinical features and HCC recurrence. Tumor size (OR=1.454, 95% CI: 1.047–2.020, P=0.026) and TNM stage (OR=1.360, 95% CI: 1.021–1.813, P=0.036) were independent predictors of HCC recurrence after curative resection. Therefore, the following equation was established to predict HCC recurrence: 0.308×TNM+0.374×tumor size–0.639. The equation score was 0.53±0.23 in patients who experienced HCC recurrence compared with 0.47±0.24 in other patients. A similar trend was observed in patients who survived after the last follow-up, compared with those who did not, with scores of 0.37±0.26 vs 0.52±0.22, respectively (P<0.001). The Kaplan–Meier analysis showed that patients with HCC with equation values >0.5 had significantly worse outcomes than those with equation values ≤0.5 (P<0.001) for overall survival (OS) and recurrence (P=0.043). Multivariate Cox analyses showed that tumor multiplicity (P=0.039), involucrum (P=0.029), vascular invasion (P<0.001), and equation value (P<0.001) were independent prognostic variables for OS, whereas tumor multiplicity (P=0.01), tumor differentiation (P=0.007), vascular invasion (P<0.001), involucrum (P=0.01), and equation value (P<0.001) were independent prognostic variables for HCC recurrence.

Conclusion

We established a novel and effective equation for predicting the probability of recurrence and OS after curative resection. Patients with a high recurrence score, based on this equation, should undergo additional high-end imaging examinations.