356

FASLG

APT1LG1|CD178|CD95-L|CD95L|FASL|TNFSF6;Fas ligand (TNF superfamily, member 6);FASLG;Fas ligand (TNF superfamily, member 6)

APTL|CD95 ligand|apoptosis (APO-1) antigen ligand 1|apoptosis antigen ligand|fas antigen ligand|tumor necrosis factor (ligand) superfamily, member 6|tumor necrosis factor ligand superfamily member 6

1q23

protein-coding

Osteosarcoma is the most common primary bone tumour associated with childhood and adolescence. The possible role of the small leucine-rich proteoglycan, lumican, in the growth and metastasis of various cancer types has recently been investigated. In this study, the expression of lumican was examined in moderately differentiated (MG-63) and well-differentiated (Saos 2) human osteosarcoma cell lines of high and low metastatic capability, respectively. Real-time PCR, western blotting with antibodies against the protein core and keratan sulfate, and specific enzymatic digestions were the methods employed. The two human osteosarcoma cell lines were found to express and secrete lumican partly substituted with keratan sulfate glycosaminoglycans. Importantly, the non-metastatic, well-differentiated Saos 2 cells produced lumican at rates that were up to sevenfold higher than those of highly metastatic MG-63 cells. The utilization of short interfering RNA specific for the lumican gene resulted in efficient down-regulation of its mRNA levels in both cell lines. The growth of Saos 2 cells was inhibited by lumican, whereas their migration and chemotactic response to fibronectin were found to be promoted. Lumican expression was negatively correlated with the basal level of Smad 2 activation in these cells, suggesting that lumican may affect the bioavailability of Smad 2 activators. By contrast, these cellular functions of highly aggressive MG-63 cells were demonstrated not to be sensitive to a decrease in their low endogenous lumican levels. These results suggest that lumican expression may be positively correlated with the differentiation and negatively correlated with the progression of osteosarcoma.

BACKGROUND: The Real-time PCR Array System is the ideal tool for analyzing the expression of a focused panel of genes. In this study, we will analyze the gene expression profile of pediatric acute myeloid leukemia with real-time PCR arrays. METHODS: Real-time PCR array was designed and tested firstly. Then gene expression profile of 11 pediatric AML and 10 normal controls was analyzed with real-time PCR arrays. We analyzed the expression data with MEV (Multi ExperimentView) cluster software. Datasets representing genes with altered expression profile derived from cluster analyses were imported into the Ingenuity pathway Analysis Tool. RESULTS: We designed and tested 88 real-time PCR primer pairs fora quantitative gene expression analysis of key genes involved in pediatric AML. The gene expression profile of pediatric AML is significantly different from normal control; there are 19 genes up-regulated and 25 genes down-regulated in pediatric AML. To investigate possible biological interactions of differently regulated genes, datasets representing genes with altered expression profile were imported into the Ingenuity pathway Analysis Tool. The results revealed 12 significant networks. Of these networks, Cellular Development, Cellular Growth and Proliferation, tumor Morphology was the highest rated network with 36 focus molecules and the significance score of 41. The IPA analysis also groups the differentially expressed genes into biological mechanisms that are related to hematological disease, cell death, cell growth and hematological system development. In the top canonical pathways, p53 and Huntington's disease signaling came out to be the top two most significant pathways with a p value of1.5E-8 and2.95E-7, respectively. CONCLUSIONS: The present study demonstrates thegene expression profile of pediatric AML is significantly different from normal control; there are 19 genes up-regulated and 25 genes down-regulated in pediatric AML. We found some genes dyes-regulated in pediatric AML for the first time as FASLG, HDAC4, HDAC7 and some HOX family genes. IPA analysis showed the top important pathways for pediatric AML are p53 and Huntington's disease signaling.This work may provide new clues of molecular mechanism in pediatric AML.