Elisa Wurmbach

Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma†

Although HCC is the third‐leading cause of cancer‐related deaths worldwide, there is only an elemental understanding of its molecular pathogenesis. In western countries, HCV infection is the main etiology underlying this cancers accelerating incidence. To characterize the molecular events of the hepatocarcinogenic process, and to identify new biomarkers for early HCC, the gene expression profiles of 75 tissue samples were analyzed representing the stepwise carcinogenic process from preneoplastic lesions (cirrhosis and dysplasia) to HCC, including 4 neoplastic stages (very early HCC to metastatic tumors) from patients with HCV infection. We identified gene signatures that accurately reflect the pathological progression of disease at each stage. Eight genes distinguish between control and cirrhosis, 24 between cirrhosis and dysplasia, 93 between dysplasia and early HCC, and 9 between early and advanced HCC. Using quantitative real‐time reverse‐transcription PCR, we validated several novel molecular tissue markers for early HCC diagnosis, specifically induction of abnormal spindle‐like, microcephaly‐associated protein, hyaluronan‐mediated motility receptor, primase 1, erythropoietin, and neuregulin 1. In addition, pathway analysis revealed dysregulation of the Notch and Toll‐like receptor pathways in cirrhosis, followed by deregulation of several components of the Jak/STAT pathway in early carcinogenesis, then upregulation of genes involved in DNA replication and repair and cell cycle in late cancerous stages. Conclusion: These findings provide a comprehensive molecular portrait of genomic changes in progressive HCV‐related HCC. (HEPATOLOGY 2007;45:938–947.)

Focused microarray analysis

We describe detailed protocols and results with an integrated platform for studying relative transcript expression, including microarray design and fabrication, analysis and calibration algorithms, and high throughput quantitative real-time PCR. This approach optimizes sensitivity and accuracy while controlling the cost of experiments. A high quality cDNA array was fabricated using a restricted number of carefully selected transcripts with each clone printed in triplicate. This focused array facilitated both repeated measurement and replicate experiments. Following normalization and differential expression analysis, we found that experiments with this array identified differentially expressed transcripts with a high degree of accuracy and with high sensitivity to low levels of differential expression. Using a calibration algorithm improved the accuracy of the array in quantifying the relative level of transcript expression. All differentially expressed transcripts identified by the array were independently tested using high throughput quantitative real-time PCR assays. This approach reliably identified transcripts having as low as 1.3-fold differences in transcript expression between RNA samples from treatment- and control groups and was applicable to highly heterogenous tissue sources such as hypothalamus and cerebral cortex.

Validated genomic approach to study differentially expressed genes in complex tissues.

Microarray-based genomic techniques allow the simultaneous determination of relative levels of expression of a large number of genes. Studies of the transcriptome in complex neurobiological systems are uniquely demanding due to the heterogeneous nature of these cells. Most brain regions contain a large variety of cell populations that are closely intermingled. The expression of any specific gene may be restricted to a subpopulation of cells, and changes in gene expression may occur in only a small fraction of the cells expressing that transcript. Due to this dilution effect, many genes of interest are expected to have relatively low levels of expression in tissue homogenates. Furthermore, biologically significant differences in expression may result in only small fold-changes. Therefore genomic approaches using brain dissections must be optimized to identify potentially regulated transcripts and differential expression should be confirmed using quantitative assays. We evaluated the effects of increasing tissue complexity on detection of regulated transcripts in focused microarray studies using a mouse cell line, mouse hypothalamus and mouse cortex. Regulated transcripts were confirmed by quantitative real-time PCR. As tissue complexity increased, distinguishing significantly regulated genes from background variation became increasingly more difficult. However, we found that cDNA microarray studies using regional brain dissections and appropriate numbers of replicates could identify genes showing less than 2-fold regulation and that most regulated genes identified fell within this range.

Mining microarrays for metabolic meaning: nutritional regulation of hypothalamic gene expression.

DNA microarray analysis has been used to investigate relative changes in the level of gene expression in the CNS, including changes that are associated with disease, injury, psychiatric disorders, drug exposure or withdrawal, and memory formation. We have used oligonucleotide microarrays to identify hypothalamic genes that respond to nutritional manipulation. In addition to commonly used microarray analysis based on criteria such as fold-regulation, we have also found that simply carrying out multiple t tests then sorting by P value constitutes a highly reliable method to detect true regulation, as assessed by real-time polymerase chain reaction (PCR), even for relatively low abundance genes or relatively low magnitude of regulation. Such analyses directly suggested novel mechanisms that mediate effects of nutritional state on neuroendocrine function and are being used to identify regulated gene products that may elucidate the metabolic pathology of obese ob/ob, lean Vgf-/Vgf-, and other models with profound metabolic impairments.