Cytokeratin 8 and 18 expression in imprint smears of chronic viral hepatitis, autoimmune hepatitis and hepatocellular carcinoma. A preliminary study

OBJECTIVE: To investigate whether the expression of cytokeratin (CK) 8 and 18 is altered in chronic active viral hepatitis, autoimmune hepatitis and hepatocellular carcinoma. STUDY DESIGN: Cytologic imprint smears were obtained from 53 liver core biopsy specimens and were studied immunocytochemically for the expression of CK8 and 18. RESULTS: CK8-positive expression was observed in 45.5% of chronic active hepatitis B (CH-B), 20% of chronic active hepatitis C (CH-C), 90% of autoimmune hepatitis (AIH) and 83.3% of hepatocellular carcinoma (HCC) cases. CK18-positive expression was observed in 36.4% of CH-B, 26.7% of CH-C, 70% of AIH and 83.3% of HCC cases. A statistically significant association was found between CK8- and CK18-positive expression and the diagnosis of AIH and HCC. In contrast, CH-C and CH-B were associated with negative CK8 and CK18 expression. In addition, a negative [CK8(-)/CK18(-)] or imbalanced [CK8(-)/CK18(+), CK8(+)/CK18(-)] expression pattern was found in 100.0% and 81.18% of CH-C and CH-B cases, respectively, while the relative percentages of AIH and HCC cases were significantly lower (30.0% and 16.7%, respectively) (p < 0.0001). CONCLUSION: Our results indicate that CK8 and 18 expression is maintained in AIH and HCC and altered in CH-B and CH-C. The pathogenetic mechanism of this alteration remains to be clarified.     

Extract‐SAGE: An integrated platform for cross­analysis and GA­based selection of SAGE data

Serial analysis of gene expression (SAGE) is a powerful quantification technique for gene expression data. The huge amount of tag data in SAGE libraries of samples is difficult to analyze with current SAGE analysis tools. Data is often not provided in a biologically significant way for cross‐analysis and ‐comparison, thus limiting its application. Hence, an integrated software platform that can perform such a complex task is required. Here, we implement set theory for cross‐analyzing gene expression data among different SAGE libraries of tissue sources; up‐ or down‐regulated tissue‐specific tags can be identified computationally. Extract‐SAGE employs a genetic algorithm (GA) to reduce the number of genes among the SAGE libraries. Its representative tag mining will facilitate the discovery of the candidate genes with discriminating gene expression.     

Identification and prevention of a GC content bias in SAGE libraries

Serial Analysis of Gene Expression (SAGE) is becoming a widely used gene expression profiling method for the study of development, cancer and other human diseases. Investigators using SAGE rely heavily on the quantitative aspect of this method for cataloging gene expression and comparing multiple SAGE libraries. We have developed additional computational and statistical tools to assess the quality and reproducibility of a SAGE library. Using these methods, a critical variable in the SAGE protocol was identified that has the potential to bias the Tag distribution relative to the GC content of the 10 bp SAGE Tag DNA sequence. We also detected this bias in a number of publicly available SAGE libraries. It is important to note that the GC content bias went undetected by quality control procedures in the current SAGE protocol and was only identified with the use of these statistical analyses on as few as 750 SAGE Tags. In addition to keeping any solution of free DiTags on ice, an analysis of the GC content should be performed before sequencing large numbers of SAGE Tags to be confident that SAGE libraries are free from experimental bias.     

aRNA-longSAGE: a new approach to generate SAGE libraries from microdissected cells

Large-scale gene expression analyses of microdissected primary tissue are still difficult because generally only a limited amount of mRNA can be obtained from microdissected cells. The introduction of the T7-based RNA amplification technique was an important step to reduce the amount of RNA needed for such analyses. This amplification technique produces amplified antisense RNA (aRNA), which so far has precluded its direct use for serial analysis of gene expression (SAGE) library production. We describe a method, termed ‘aRNA-longSAGE’, which is the first to allow the direct use of aRNA for standard longSAGE library production. The aRNA-longSAGE protocol was validated by comparing two aRNA-longSAGE libraries with two Micro-longSAGE libraries that were generated from the same RNA preparations of two different cell lines. Using a conservative validation approach, we were able to verify 68% of the differentially expressed genes identified by aRNA-longSAGE. Furthermore, the identification rate of differentially expressed genes was roughly twice as high in our aRNA-longSAGE libraries as in the standard Micro-longSAGE libraries. Using our validated aRNA-longSAGE protocol, we were able to successfully generate longSAGE libraries from as little as 40 ng of total RNA isolated from 2000–3000 microdissected pancreatic ductal epithelial cells or cells from pancreatic intraepithelial neoplasias.