Hypoxia-inducible factor-1 alpha, in association with inflammation, angiogenesis and MYC, is a critical prognostic factor in patients with HCC after surgery

<p>Abstract</p> <p>Background</p> <p>Despite well-studied tumor hypoxia in laboratory, little is known about the association with other pathophysiological events in the clinical view. We investigated the prognostic value of hypoxia-inducible factor-1 alpha (HIF-1alpha) in hepatocellular carcinoma (HCC), and its correlations with inflammation, angiogenesis and MYC oncogene.</p> <p>Methods</p> <p>In a random series of 110 HCC patients, the mRNA of HIF-1alpha, inflammation related factors (COX-2, MMP7 and MMP9), angiogenesis related factors (VEGF and PDGFRA) and MYC in tumor tissue were detected by real-time RT-PCR and HIF-1alpha protein was assessed by immunohistochemistry. The correlations between HIF-1alpha mRNA and the factors mentioned previously, the relationship between HIF-1alpha and clinicopathologic features, and the prognostic value were analyzed.</p> <p>Results</p> <p>The expression of both HIF-1alpha mRNA and protein in HCC were independent prognostic factors for overall survival (OS) (<it>P </it>= 0.012 and <it>P </it>= 0.021, respectively) and disease-free survival (DFS) (<it>P </it>= 0.004 and <it>P </it>= 0.007, respectively) as well. Besides, the high expression of HIF-1alpha mRNA and protein proposed an advanced BCLC stage and more incidence of vascular invasion. The mRNA of HIF-1alpha had significantly positive correlations to that of COX-2, PDGFRA, MMP7, MMP9, MYC, except VEGF. In addition to HIF-1alpha, COX-2 and PDGFRA were also independent prognosticators for OS (<it>P </it>= 0.004 and <it>P </it>= 0.010, respectively) and DFS (<it>P </it>= 0.010 and <it>P </it>= 0.038, respectively).</p> <p>Conclusion</p> <p>HIF-1alpha in HCC plays an important role in predicting patient outcome. It may influence HCC biological behaviors and affect the tumor inflammation, angiogenesis and act in concert with the oncogene MYC. Attaching importance to HIF-1alpha in HCC may improve the prognostic and therapeutic technique.</p

Functional Analysis: Evaluation of Response Intensities - Tailoring ANOVA for Lists of Expression Subsets

Background: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. Results: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. Conclusions: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect

Gene Expression Programs in Response to Hypoxia: Cell Type Specificity and Prognostic Significance in Human Cancers

BACKGROUND: Inadequate oxygen (hypoxia) triggers a multifaceted cellular response that has important roles in normal physiology and in many human diseases. A transcription factor, hypoxia-inducible factor (HIF), plays a central role in the hypoxia response; its activity is regulated by the oxygen-dependent degradation of the HIF-1α protein. Despite the ubiquity and importance of hypoxia responses, little is known about the variation in the global transcriptional response to hypoxia among different cell types or how this variation might relate to tissue- and cell-specific diseases. METHODS AND FINDINGS: We analyzed the temporal changes in global transcript levels in response to hypoxia in primary renal proximal tubule epithelial cells, breast epithelial cells, smooth muscle cells, and endothelial cells with DNA microarrays. The extent of the transcriptional response to hypoxia was greatest in the renal tubule cells. This heightened response was associated with a uniquely high level of HIF-1α RNA in renal cells, and it could be diminished by reducing HIF-1α expression via RNA interference. A gene-expression signature of the hypoxia response, derived from our studies of cultured mammary and renal tubular epithelial cells, showed coordinated variation in several human cancers, and was a strong predictor of clinical outcomes in breast and ovarian cancers. In an analysis of a large, published gene-expression dataset from breast cancers, we found that the prognostic information in the hypoxia signature was virtually independent of that provided by the previously reported wound signature and more predictive of outcomes than any of the clinical parameters in current use. CONCLUSIONS: The transcriptional response to hypoxia varies among human cells. Some of this variation is traceable to variation in expression of the HIF1A gene. A gene-expression signature of the cellular response to hypoxia is associated with a significantly poorer prognosis in breast and ovarian cancer