CD34 ؉ hematopoietic stem cells are used clinically to support cytotoxic therapy, and recent studies raised hope that they could even serve as a cellular source for nonhematopoietic tissue engineering. Here, we examined in 18 volunteers the gene expressions of 1185 genes in highly enriched bone marrow CD34 ؉ (BM-CD34 ؉ ) or granulocyte-colony-stimulating factormobilized peripheral blood CD34 ؉ (PB-CD34 ؉ ) cells by means of cDNA array technology to identify molecular causes underlying the functional differences between circulating and sedentary hematopoietic stem and progenitor cells. In total, 65 genes were significantly differentially expressed. Greater cell cycle and DNA synthesis activity of BM-CD34 ؉ than PB-CD34 ؉ cells were reflected by the 2-to 5-fold higher expression of 9 genes involved in cell cycle progression, 11 genes regulating DNA synthesis, and cell cycleinitiating transcription factor E2F-1. Conversely, 9 other transcription factors, including the differentiation blocking GATA2 and N-myc, were expressed 2 to 3 times higher in PB-CD34 ؉ cells than in BM-CD34 ؉ cells. Expression of 5 apoptosis driving genes was also 2 to 3 times greater in PB-CD34 ؉ cells, reflecting a higher apoptotic activity. In summary, our study provides a gene expression profile of primary human CD34 ؉ hematopoietic cells of the blood and marrow. Our data molecularly confirm and explain the finding that CD34 ؉ cells residing in the bone marrow cycle more rapidly, whereas circulating CD34 ؉ cells consist of a higher number of quiescent stem and progenitor cells. Moreover, our data provide novel molecular insight into stem cell physiology.

Conclusions: Gene expression profiling of human bladder cancers provides insight into the biology of bladder cancer progression and identifies patients with distinct clinical phenotypes.

our results demonstrate for the first time the complexity of the dissecting process on a molecular level. The ultimate dissection seems to be the dramatic endpoint of a long-lasting process of degradation and insufficient remodelling of the aortic wall. Altered patterns of gene expression suggest a pre-existing structural failure of the aortic wall, resulting in dissection.

Purpose: Our goal was to identify genes undergoing expressional changes shortly after the beginning of neoadjuvant chemotherapy for primary breast cancer.Experimental Design: The biopsies were taken from patients with primary breast cancer prior to any treatment and 24 hours after the beginning of the neoadjuvant chemotherapy. Expression analyses from matched pair samples representing 25 patients were carried out with Clontech filter arrays. A subcohort of those 25 paired samples were additionally analyzed with the Affymetrix GeneChip platform. All of the transcripts from both platforms were queried for expressional changes.Results: Performing hierarchical cluster analysis, we clustered pre-and posttreatment samples from individual patients more closely to each other than the samples taken from different patients. This reflects the rather low number of transcripts responding directly to the drugs used. Although transcriptional drug response occurring during therapy differed between individual patients, two genes (p21 WAF1/CIP1 and MIC-1) were up-regulated in posttreatment samples. This could be validated by semiquantitative and real-time reverse transcription-PCR. Partial leastdiscriminant analysis based on approximately 25 genes independently identified by either Clontech or Affymetrix platforms could clearly discriminate pre-and posttreatment samples. However, correlation of certain gene expression levels as well as of differential patterns and clusters as determined by a different platform was not always satisfying.Conclusions: This study has demonstrated the potential of monitoring posttreatment changes in gene expression as a measure of the pharmacodynamics of drugs. As a clinical laboratory model, it can be useful to identify patients with sensitive and reactive tumors and to help for optimized choice for sequential therapy and obviously improve relapsefree and overall survival.

Background: We compared gene expression profiles in acutely dissected aorta with those in normal control aorta.

The available clinical prognostic tools show an obvious limitation in predicting the outcome of breast cancer patients, and pathological features cannot classify tumours accurately. Microarray-based molecular classification of breast tumours or selection of gene expression panels to improve risk prediction or treatment outcomes are thought to be theoretically superior to established clinical and pathological criteria, based on guidelines such as the St Gallen and National Institute of Health consensus, or which use specific prognostic tools, such as the Nottingham Prognostic Index or Adjuvant-Online algorithm. Although two diagnostic tests based on gene expression profiling of breast cancer are commercially available, a new molecular classification and molecular forecasting of breast cancer based on expression profiling cannot outperform the standard tumour diagnostic at present. This review focuses on some important problems in the practical application of molecular profiling of breast cancer for clinical purposes.

Background: DNA microarrays are a powerful technology that can provide a wealth of gene expression data for disease studies, drug development, and a wide scope of other investigations. Because of the large volume and inherent variability of DNA microarray data, many new statistical methods have been developed for evaluating the significance of the observed differences in gene expression. However, until now little attention has been given to the characterization of dispersion of DNA microarray data.

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