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Prediction of early hepatocellular carcinoma recurrence using germinal center kinase-like kinase.

ABSTRACT: Germinal center kinase-like kinase (GLK) is a key controller of autoimmunity. In this study, we assessed the clinical relevance and tumorigenic effects of GLK in hepatocellular carcinoma (HCC). Using immunohistochemistry, we showed that the GLK proportion score increased in both cancerous and adjacent non-cancerous liver tissue from patients with HCC recurrence. A Kaplan-Meier analysis revealed that patients with a wide distribution of GLK in non-cancerous liver tissue had a higher rate of HCC recurrence than those with very low or no GLK expression. Multivariate Cox regression analyses indicated that a high GLK proportion score in non-cancerous liver tissue was an independent predictor of early HCC recurrence after resection. Lentiviral vector-mediated overexpression of GLK activated the nuclear factor kappa B (NF?B) signaling cascade and accelerated cell cycle progression in primary human hepatocytes, thereby promoting proliferation. An increase in GLK expression coincided with NF?B activation and enhanced expression of proliferating cell nuclear antigen in HCC tissue. Our findings demonstrate a potential hepatocarcinogenic effect of GLK and the feasibility of using GLK to predict early HCC recurrence.

SUBMITTER: Ho CH

PROVIDER: S-EPMC5226546 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Prediction of early hepatocellular carcinoma recurrence using germinal center kinase-like kinase.

Ho Cheng-Hsun CH Chuang Huai-Chia HC Wu I-Chin IC Tsai Hung-Wen HW Lin Yih-Jyh YJ Sun Hung-Yu HY Young Kung-Chia KC Chiu Yen-Cheng YC Cheng Pin-Nan PN Liu Wen-Chun WC Tan Tse-Hua TH Chang Ting-Tsung TT

Oncotarget 20160801 31

Germinal center kinase-like kinase (GLK) is a key controller of autoimmunity. In this study, we assessed the clinical relevance and tumorigenic effects of GLK in hepatocellular carcinoma (HCC). Using immunohistochemistry, we showed that the GLK proportion score increased in both cancerous and adjacent non-cancerous liver tissue from patients with HCC recurrence. A Kaplan-Meier analysis revealed that patients with a wide distribution of GLK in non-cancerous liver tissue had a higher rate of HCC r ...[more]

PMID: 27343552

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Project description:BackgroundEarly recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-operation HCC patients' risk of early recurrence and their response to adjuvant therapy.MethodsIn this study, we downloaded four gene expression datasets (GSE14520, GSE54236, GSE87630, and GSE109211) from the Gene Expression Omnibus database and identified 34 common differentially expressed genes associated with HCC dysregulation and response to adjuvant sorafenib. Then, we constructed a novel 11-messenger RNA predictive model by using ROC curves analysis, univariate Cox regression analysis, and LASSO Cox regression analysis. Furthermore, we validated the predictive values of the risk model in GSE14520 and TCGA-LIHC cohorts by using Kaplan-Meier survival analysis, multivariable Cox regression analysis, and decision curve analysis, respectively.ResultsThe risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib. Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895-7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155-4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001-6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78-5.596]). The AUC values of the risk score model for predicting tumor early recurrence were 0.746 and 0.618, and that of the risk score model for predicting the response to adjuvant sorafenib were 0.722 and 0.708 in the different cohort, respectively. Multivariable Cox regression analysis and decision curve analysis also showed that the risk score model was superior to and independent of other clinicopathologic characteristics. Moreover, the risk score model had excellent abilities to predict the overall survival and HCC recurrence of patients with the same tumor stage category.ConclusionsOur risk model is a reliable and superior predictive tool. With this model, we could optimize the risk stratification based on early tumor recurrence and could evaluate the response of patients to adjuvant sorafenib after liver resection.

Project description:OBJECTIVE:To evaluate the performance of predicting early recurrence using preoperative factors only in comparison with using both pre-/postoperative factors. MATERIALS AND METHODS:We retrospectively reviewed 549 patients who had undergone curative resection for single hepatcellular carcinoma (HCC) within Milan criteria. Multivariable analysis was performed to identify pre-/postoperative high-risk factors of early recurrence after hepatic resection for HCC. Two prediction models for early HCC recurrence determined by stepwise variable selection methods based on Akaike information criterion were built, either based on preoperative factors alone or both pre-/postoperative factors. Area under the curve (AUC) for each receiver operating characteristic curve of the two models was calculated, and the two curves were compared for non-inferiority testing. The predictive models of early HCC recurrence were internally validated by bootstrap resampling method. RESULTS:Multivariable analysis on preoperative factors alone identified aspartate aminotransferase/platelet ratio index (OR, 1.632; 95% CI, 1.056-2.522; p = 0.027), tumor size (OR, 1.025; 95% CI, 0.002-1.049; p = 0.031), arterial rim enhancement of the tumor (OR, 2.350; 95% CI, 1.297-4.260; p = 0.005), and presence of nonhypervascular hepatobiliary hypointense nodules (OR, 1.983; 95% CI, 1.049-3.750; p = 0.035) on gadoxetic acid-enhanced magnetic resonance imaging as significant factors. After adding postoperative histopathologic factors, presence of microvascular invasion (OR, 1.868; 95% CI, 1.155-3.022; p = 0.011) became an additional significant factor, while tumor size became insignificant (p = 0.119). Comparison of the AUCs of the two models showed that the prediction model built on preoperative factors alone was not inferior to that including both pre-/postoperative factors {AUC for preoperative factors only, 0.673 (95% confidence interval [CI], 0.623-0.723) vs. AUC after adding postoperative factors, 0.691 (95% CI, 0.639-0.744); p = 0.0013}. Bootstrap resampling method showed that both the models were valid. CONCLUSION:Risk stratification solely based on preoperative imaging and laboratory factors was not inferior to that based on postoperative histopathologic risk factors in predicting early recurrence after curative resection in within Milan criteria single HCC patients.

Project description:Background:Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies with poor prognosis. There are many selectable treatments with good prognosis in Barcelona Clinic Liver Cancer- (BCLC-) 0, A, and B HCC patients, but the most crucial factor affecting survival is the high recurrence rate after treatments. Therefore, it is of great significance to predict the recurrence of BCLC-0, BCLC-A, and BCLC-B HCC patients. Aim:To develop a gene signature to enhance the prediction of recurrence among HCC patients. Materials and Methods:The RNA expression data and clinical data of HCC patients were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to screen primarily prognostic biomarkers in GSE14520. Multivariate Cox regression analysis was introduced to verify the prognostic role of these genes. Ultimately, 5 genes were demonstrated to be related with the recurrence of HCC patients and a gene signature was established. GSE76427 was adopted to further verify the accuracy of gene signature. Subsequently, a nomogram based on gene signature was performed to predict recurrence. Gene functional enrichment analysis was conducted to investigate the potential biological processes and pathways. Results:We identified a five-gene signature which performs a powerful predictive ability in HCC patients. In the training set of GSE14520, area under the curve (AUC) for the five-gene predictive signature of 1, 2, and 3 years were 0.813, 0.786, and 0.766. Then, the relative operating characteristic (ROC) curves of five-gene predictive signature were verified in the GSE14520 validation set, the whole GSE14520, and GSE76427, showed good performance. A nomogram comprising the five-gene signature was built so as to show a good accuracy for predicting recurrence-free survival of HCC patients. Conclusion:The novel five-gene signature showed potential feasibility of recurrence prediction for early-stage HCC.

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Prediction of early hepatocellular carcinoma recurrence using germinal center kinase-like kinase.

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Prediction of early hepatocellular carcinoma recurrence using germinal center kinase-like kinase.

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