The communication between tumor-derived elements and stroma in the metastatic niche has a critical role in facilitating cancer metastasis. Yet, the mechanisms tumor cells use to control metastatic niche formation are not fully understood. Here we report that in the lung metastatic niche, high-metastatic hepatocellular carcinoma (HCC) cells exhibit a greater capacity to convert normal fibroblasts to cancer-associated fibroblasts (CAFs) than low-metastatic HCC cells. We show high-metastatic HCC cells secrete exosomal miR-1247-3p that directly targets B4GALT3, leading to activation of β1-integrin–NF-κB signaling in fibroblasts. Activated CAFs further promote cancer progression by secreting pro-inflammatory cytokines, including IL-6 and IL-8. Clinical data show high serum exosomal miR-1247-3p levels correlate with lung metastasis in HCC patients. These results demonstrate intercellular crosstalk between tumor cells and fibroblasts is mediated by tumor-derived exosomes that control lung metastasis of HCC, providing potential targets for prevention and treatment of cancer metastasis.

BackgroundHypoxia-inducible factor 1 alpha (HIF-1α) and vascular endothelial growth factor (VEGF) are frequently overexpressed in numerous types of cancers and are known to be important regulators of angiogenesis. Until now, few studies have been carried out to investigate the prognostic role of these factors in solid tumors, especially in colorectal cancer (CRC). The purpose of this study was to evaluate the expression of HIF-1α and VEGF in CRC tissues, and to analyze the association of these two factors with several clinical and pathological characteristics, and patients' survival.MethodsParaffin-embedded tissue samples were retrospectively collected from 71 CRC patients, who received surgical resection between 2001 and 2002, with a median follow-up of 5 years. We examined the patterns of expression of HIF-1α and VEGF by immunohistochemistry method. Statistical analysis was performed with univariate tests and multivariate Cox proportional hazards model to evaluate the differences.ResultsExpression of HIF-1α and VEGF was positively observed in 54.93% and 56.34% among the patients, respectively. HIF-1α and VEGF status were significantly associated with tumor stage, lymph nodes and liver metastases (P < 0.05). Expression of both HIF-1α and VEGF remained significantly associated with overall survival (OS) (P < 0.01), and HIF-1α was positively correlative to VEGF in CRC (r = 0.72, P < 0.001).ConclusionsHIF-1α and VEGF could be used as biomarkers indicating tumors in advanced stage and independently implied poor prognosis in patients with CRC. Treatment that inhibits HIF-1α might be a promising targeted approach in CRC to exhibit its potential to improve outcomes in future perspective, just as VEGF targeting has proved to be.

The process of eukaryotic gene expression involves a diverse number of steps including transcription, RNA processing, transport, translation, and mRNA turnover. A critical step in understanding this process will be the development of mathematical models that quantitatively describe and predict the behavior of this complex system. We have simulated eukaryotic mRNA turnover in a linear multicomponent model based on the known mRNA decay pathways in yeast. Using rate constants based on experimental data for the yeast unstable MFA2 and stable PGK1 transcripts, the computational modeling reproduces experimental observations after minor adjustments. Subsequent analysis and a series of in silico experiments led to several conclusions. First, we demonstrate that mRNA half-life as commonly measured underestimates the average life span of an mRNA. Second, due to the properties of the pathways, the measurement of a half-life can predominantly measure different steps in the decay network. A corollary of this fact is that different mRNAs will be affected differentially by changes in specific rate constants. Third, the way to obtain the largest change of levels of mRNA for the smallest changes in rate is by changing the rate of deadenylation, where a large amount of regulation of mRNA decay occurs. Fourth, the 3'-to-5' degradation of mRNA shows mRNA-specific rates of degradation that are dependent on the 5' structure of the mRNA. These programs can be run over the Web, are adaptable to other eukaryotes, and provide outputs as graphs and virtual northern gels, which can be directly compared to experimental data. Therefore, this model constitutes a useful tool for the quantitative analysis of the process and control of mRNA degradation in eukaryotic cells.

The aim of this study is to find the potential biomarkers from the rat hepatocellular carcinoma (HCC) disease model by using a non-target metabolomics method, and test their usefulness in early human HCC diagnosis. The serum metabolic profiling of the diethylnitrosamine-induced rat HCC model, which presents a stepwise histopathological progression that is similar to human HCC, was performed using liquid chromatography-mass spectrometry. Multivariate data analysis methods were utilized to identify the potential biomarkers. Three metabolites, taurocholic acid, lysophosphoethanolamine 16:0, and lysophosphatidylcholine 22:5, were defined as "marker metabolites," which can be used to distinguish the different stages of chemical hepatocarcinogenesis. These metabolites represented the abnormal metabolism during the progress of hepatocarcinogenesis, which could also be found in patients. To test their diagnosis potential 412 sera from 262 patients with HCC, 76 patients with cirrhosis and 74 patients with chronic hepatitis B were collected and studied, it was found that 3 marker metabolites were effective for the discrimination of small liver tumor (solitary nodules of less than 2 cm in diameter) patients, achieved a sensitivity of 80.5% and a specificity of 80.1%,which is better than those of ␣-fetoprotein (53 and 64%, respectively). Moreover, they were also effective for the discrimination of all HCCs and chronic liver disease patients, which could achieve a sensitivity of 87.5% and a specificity of 72.3%, better than those of ␣-fetoprotein (61.2 and 64%). These results indicate metabolomics method has