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The RNA-binding protein Apobec1 complementation factor (A1CF) regulates posttranscriptional ApoB mRNA editing, but the range of RNA targets and the long-term effect of altered A1CF expression on liver function are unknown. Here we studied hepatocyte-specific A1cf-transgenic ([A1cf.sup.+/Tg]), [A1cf.sup.+/Tg] [Apobec1.sup.-/-], and [A1cf.sup.-/-] mice fed chow or high-fat/high-fructose diets using RNA-Seq, RNA CLIP-Seq, and tissue microarrays from human hepatocellular cancer (HCC). [A1cf.sup.+/Tg] mice exhibited increased hepatic proliferation and steatosis, with increased lipogenic gene expression (Mogatl, Mogat2, Cidea, Cd36) associated with shifts in polysomal RNA distribution. Aged [A1cf.sup.+/Tg] mice developed spontaneous fibrosis, dysplasia, and HCC, and this development was accelerated on a high-fat/high-fructose diet and was independent of Apobec1. RNA-Seq revealed increased expression of mRNAs involved in oxidative stress (Gstm3, Gpx3, Cbr3), inflammatory response (Il19, Cxcl14, Tnfa, Ly6c), extracellular matrix organization (Mmp2, Col1a1, Col4a1), and proliferation (Kif20a, M[cm.sup.2], Mcm4, Mcm6), and a subset of mRNAs (including Sox4, Sox9, Cdh1) were identified in RNA CLIP-Seq. Increased A1CF expression in human HCC correlated with advanced fibrosis and with reduced survival in a subset with nonalcoholic fatty liver disease. In conclusion, we show that hepatic A1CF overexpression selectively alters polysomal distribution and mRNA expression, promoting lipogenic, proliferative, and inflammatory pathways leading to HCC., Introduction The increasing worldwide prevalence of nonalcoholic fatty liver disease (NAFLD) is projected to result in a corresponding increase in major progressive outcomes, including nonalcoholic steatohepatitis, fibrosis, and hepatocellular cancer [...]

Metastasis is a major cause of mortality in cancer patients. However, the mechanisms governing the metastatic process remain elusive, and few accurate biomarkers exist for predicting whether metastasis will occur, something that would be invaluable for guiding therapy. We report here that the carboxypeptidase E gene (CPE) is alternatively spliced in human tumors to yield an N-terminal truncated protein (CPE-ΔN) that drives metastasis. mRNA encoding CPE-ΔN was found to be elevated in human metastatic colon, breast, and hepatocellular carcinoma (HCC) cell lines. In HCC cells, cytosolic CPE-ΔN was translocated to the nucleus and interacted with histone deacetylase 1/2 to upregulate expression of the gene encoding neural precursor cell expressed, developmentally downregulated gene 9 (Nedd9)--which has been shown to promote melanoma metastasis. Nedd9 upregulation resulted in enhanced in vitro proliferation and invasion. Quantification of mRNA encoding CPE-ΔN in HCC patient samples predicted intrahepatic metastasis with high sensitivity and specificity, independent of cancer stage. Similarly, high CPE-ΔN mRNA copy numbers in resected pheochromocytomas/paragangliomas (PHEOs/PGLs), rare neuroendocrine tumors, accurately predicted future metastasis or recurrence. Thus, CPE-ΔN induces tumor metastasis and should be investigated as a potentially powerful biomarker for predicting future metastasis and recurrence in HCC and PHEO/PGL patients., Introduction Cancer mortality often results from metastatic disease and is not the direct effect of the primary tumor. With advances in cancer treatment, control of the primary tumor can be [...]

The Editorial Board has recently been informed that the NIH examined the primary data from pheochromocytoma patients used to construct Table 4. Evidence shows that the data on the pheochromocytoma [...]

Abstract Background The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model. Methods Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction. Results HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis. Conclusion When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.

Abstract Background Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters. Methods We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set Results Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7). Conclusion This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment.