Dataset Information

Analyzing the gene expression profile of pediatric acute myeloid leukemia with real-time PCR arrays.

ABSTRACT:

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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 Experiment View) 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 for a 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 of 1.5E-8 and2.95E-7, respectively.

Conclusions

The present study demonstrates 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. 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.

SUBMITTER: Yan-Fang T

PROVIDER: S-EPMC3495223 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Publications

Analyzing the gene expression profile of pediatric acute myeloid leukemia with real-time PCR arrays.

Yan-Fang Tao T Dong Wu W Li Pang P Wen-Li Zhao Z Jun Lu L Na Wang W Jian Wang W Xing Feng F Yan-Hong Li L Jian Ni N Jian Pan P

Cancer cell international 20120908 1

<h4>Unlabelled</h4><h4>Background</h4>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.<h4>Methods</h4>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 Exper ...[more]

PMID: 22958424

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Project description:Acute myeloid leukemia (AML) is the most common and severe form of acute leukemia diagnosed in adults. Owing to its heterogeneity, AML is divided into classes associated with different treatment outcomes and specific gene expression profiles. Based on previous studies on AML, in this study, we designed and generated an AML-array containing 900 oligonucleotide probes complementary to human genes implicated in hematopoietic cell differentiation and maturation, proliferation, apoptosis and leukemic transformation. The AML-array was used to hybridize 118 samples from 33 patients with AML of the M1 and M2 subtypes of the French-American?British (FAB) classification and 15 healthy volunteers (HV). Rigorous analysis of the microarray data revealed that 83 genes were differentially expressed between the patients with AML and the HV, including genes not yet discussed in the context of AML pathogenesis. The most overexpressed genes in AML were STMN1, KITLG, CDK6, MCM5, KRAS, CEBPA, MYC, ANGPT1, SRGN, RPLP0, ENO1 and SET, whereas the most underexpressed genes were IFITM1, LTB, FCN1, BIRC3, LYZ, ADD3, S100A9, FCER1G, PTRPE, CD74 and TMSB4X. The overexpression of the CPA3 gene was specific for AML with mutated NPM1 and FLT3. Although the microarray-based method was insufficient to differentiate between any other AML subgroups, quantitative PCR approaches enabled us to identify 3 genes (ANXA3, S100A9 and WT1) whose expression can be used to discriminate between the 2 studied AML FAB subtypes. The expression levels of the ANXA3 and S100A9 genes were increased, whereas those of WT1 were decreased in the AML-M2 compared to the AML-M1 group. We also examined the association between the STMN1, CAT and ABL1 genes, and the FLT3 and NPM1 mutation status. FLT3+/NPM1- AML was associated with the highest expression of STMN1, and ABL1 was upregulated in FLT3+ AML and CAT in FLT3- AML, irrespectively of the NPM1 mutation status. Moreover, our results indicated that CAT and WT1 gene expression levels correlated with the response to therapy. CAT expression was highest in patients who remained longer under complete remission, whereas WT1 expression increased with treatment resistance. On the whole, this study demonstrates that the AML-array can potentially serve as a first-line screening tool, and may be helpful for the diagnosis of AML, whereas the differentiation between AML subgroups can be more successfully performed with PCR-based analysis of a few marker genes.

Dataset Information

Analyzing the gene expression profile of pediatric acute myeloid leukemia with real-time PCR arrays.

Unlabelled

Background

Methods

Results

Conclusions

Publications

Analyzing the gene expression profile of pediatric acute myeloid leukemia with real-time PCR arrays.

Similar Datasets

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