Systemic analyses using large‐scale genomic profiles have successfully identified cancer‐driving somatic copy number variations (SCNVs) loci. However, functions of vast focal SCNVs in “protein‐coding gene desert” regions are largely unknown. The integrative analysis of long noncoding RNA (lncRNA) expression profiles with SCNVs in hepatocellular carcinoma (HCC) led us to identify the recurrent deletion of lncRNA‐PRAL (p53 regulation‐associated lncRNA) on chromosome 17p13.1, whose genomic alterations were significantly associated with reduced survival of HCC patients. We found that lncRNA‐PRAL could inhibit HCC growth and induce apoptosis in vivo and in vitro through p53. Subsequent investigations indicated that the three stem‐loop motifs at the 5′ end of lncRNA‐PRAL facilitated the combination of HSP90 and p53 and thus competitively inhibited MDM2‐dependent p53 ubiquitination, resulting in enhanced p53 stability. Additionally, in vivo lncRNA‐PRAL delivery efficiently reduced intrinsic tumors, indicating its potential therapeutic application. Conclusions: lncRNA‐PRAL, one of the key cancer‐driving SCNVs, is a crucial stimulus for HCC growth and may serve as a potential target for antitumor therapy. (Hepatology 2016;63:850‐863)

BackgroundDownregulation of Aldolase B (ALDOB) has been reported in hepatocellular carcinoma. However, its clinical significance and its role in pathogenesis of HCC remain largely unknown.MethodsWe analyzed the expression of ALDOB and its clinical features in a large cohort of 313 HCC patients using tissue microarray and immunohistochemistry. Moreover, the function of stably overexpressed ALDOB in HCC cells was explored in vitro and in vivo. Gene expression microarray analysis was performed on ALDOB-overexpressing SMMC7721 cells to elucidate its mechanism of action.ResultsALDOB downregulation in HCC was significantly correlated with aggressive characteristics including absence of encapsulation, increased tumor size (>5 cm) and early recurrence. ALDOB downregulation was indicative of a shorter recurrence-free survival (RFS) and overall survival (OS) for all HCC patients and early-stage HCC patients (BCLC 0-A and TNM I stage patients). Multiple analyses revealed that ALDOB downregulation was an independent risk factor of RFS and OS. Stable expression of ALDOB in HCC cell lines reduced cell migration in vitro and inhibited lung metastasis, intrahepatic metastasis, and reduced circulating tumor cells in vivo. Mechanistically, we found that cells stably expressing ALDOB show elevated Ten–Eleven Translocation 1 (TET1) expression. Moreover, ALDOB expressing cells have higher levels of methylglyoxal than do control cells, which can upregulate TET1 expression.ConclusionThe downregulation of ALDOB could indicate a poor prognosis for HCC patients, and therefore, ALDOB might be considered a prognostic biomarker for HCC, especially at the early stage. In addition, ALDOB inhibits the invasive features of cell lines partly through TET1 expression.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-015-0437-7) contains supplementary material, which is available to authorized users.

Background: Early recurrence is common for hepatocellular carcinoma (HCC) after surgical resection, being the leading cause of death. Traditionally, the COX proportional hazard (CPH) models based on linearity assumption have been used to predict early recurrence, but predictive performance is limited.Machine learning models offer a novel methodology and have several advantages over CPH models.Hence, the purpose of this study was to compare random survival forests (RSF) model with CPH models in prediction of early recurrence for HCC patients after curative resection.Methods: A total of 4,758 patients undergoing curative resection from two medical centers were included. Fifteen features including age, gender, etiology, platelet count, albumin, total bilirubin, AFP, tumor size, tumor number, microvascular invasion, macrovascular invasion, Edmondson-Steiner grade, tumor capsular, satellite nodules and liver cirrhosis were used to construct the RSF model in training cohort. Discrimination, calibration, clinical usefulness and overall performance were assessed and compared with other models. Results: Five hundred survival trees were used to generate the RFS model. The five highest Variable Importance (VIMP) were tumor size, macrovascular invasion, microvascular invasion, tumor number and AFP. In training, internal and external validation cohort, the C-index of RSF model were 0.725 [standard errors (SE) =0.005], 0.762 (SE =0.011) and 0.747 (SE =0.016), respectively; the Gönen & Heller's K of RSF model were 0.684 (SE =0.005), 0.711 (SE =0.008) and 0.697 (SE =0.014), respectively; the timedependent AUC (2 years) of RSF model were 0.818 (SE =0.008), 0.823 (SE =0.014) and 0.785 (SE =0.025), respectively. The RSF model outperformed ERASL model, Korean model, AJCC TNM stage, BCLC stage and Chinese stage. The RSF model is capable of stratifying patients into three different risk groups (low-risk, intermediate-risk, high-risk groups) in the training and two validation cohorts (all P<0.0001). A web-based prediction tool was built to facilitate clinical application (https://recurrenceprediction.shinyapps.io/surgery_ predict/). Conclusions:The RSF model is a reliable tool to predict early recurrence for patients with HCC after curative resection because it exhibited superior performance compared with other models. This novel model will be helpful to guide postoperative follow-up and adjuvant therapy.

Background:The prognosis for patients with hepatocellular carcinoma (HCC) after liver resection ranges widely and is unsatisfactory. This study aimed to develop two novel nomograms that combined tumor characteristics and inflammation-related indexes to predict overall survival (OS) and recurrence-free survival (RFS). Methods: In total, 3,071 patients who underwent radical resection were recruited. Independent risk factors were identified by Cox regression analysis and used to conduct prognostic nomograms. The C-index, timedependent areas under the receiver operating characteristic curve (time-dependent AUC), decision curve analysis (DCA), and calibration curves were used to assess the performance of the nomograms. Results: Multivariate analysis revealed that alpha-fetoprotein (AFP), resection margin, neutrophil times γ-glutamyl transpeptidase-to-lymphocyte ratio (NrLR), platelet-to-lymphocyte ratio (PLR), γ-glutamyl transpeptidase-to-platelet ratio (GPR), tumor size, tumor number, microvascular invasion, and Edmondson-Steiner grade were the independent risk factors associated with OS. The independent risk factors associated with RFS were hepatitis, AFP, albumin-bilirubin (ALBI), NrLR, PLR, PNI, GPR, tumor size, tumor number, microvascular invasion, and Edmondson-Steiner grade. The C-index of the nomograms in the training and validation cohort were 0.71 [95% confidence interval (

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