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Meta-analysis of glioblastoma multiforme versus anaplastic astrocytoma identifies robust gene markers.

Dreyfuss JM, Johnson MD, Park PJ - Mol. Cancer (2009)

Bottom Line: We found >900 statistically significant probe sets after correction for multiple testing from the >22,000 tested.Our analysis suggests that many of the most statistically significant genes work together in a HIF1A/VEGF-regulated network to increase angiogenesis and invasion in GBM when compared to AA.These feature lists could be utilized to aid in diagnosis, prognosis, and grade reduction of high-grade gliomas and to identify genes that were not previously suspected of playing an important role in glioma biology.

View Article: PubMed Central - HTML - PubMed

Affiliation: Partners HealthCare Center for Personalized Genetic Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA. jdreyfuss1@partners.org

ABSTRACT

Background: Anaplastic astrocytoma (AA) and its more aggressive counterpart, glioblastoma multiforme (GBM), are the most common intrinsic brain tumors in adults and are almost universally fatal. A deeper understanding of the molecular relationship of these tumor types is necessary to derive insights into the diagnosis, prognosis, and treatment of gliomas. Although genomewide profiling of expression levels with microarrays can be used to identify differentially expressed genes between these tumor types, comparative studies so far have resulted in gene lists that show little overlap.

Results: To achieve a more accurate and stable list of the differentially expressed genes and pathways between primary GBM and AA, we performed a meta-analysis using publicly available genome-scale mRNA data sets. There were four data sets with sufficiently large sample sizes of both GBMs and AAs, all of which coincidentally used human U133 platforms from Affymetrix, allowing for easier and more precise integration of data. After scoring genes and pathways within each data set, we combined the statistics across studies using the nonparametric rank sum method to identify the features that differentiate GBMs and AAs. We found >900 statistically significant probe sets after correction for multiple testing from the >22,000 tested. We also used the rank sum approach to select >20 significant Biocarta pathways after correction for multiple testing out of >175 pathways examined. The most significant pathway was the hypoxia-inducible factor (HIF) pathway. Our analysis suggests that many of the most statistically significant genes work together in a HIF1A/VEGF-regulated network to increase angiogenesis and invasion in GBM when compared to AA.

Conclusion: We have performed a meta-analysis of genome-scale mRNA expression data for 289 human malignant gliomas and have identified a list of >900 probe sets and >20 pathways that are significantly different between GBM and AA. These feature lists could be utilized to aid in diagnosis, prognosis, and grade reduction of high-grade gliomas and to identify genes that were not previously suspected of playing an important role in glioma biology. More generally, this approach suggests that combined analysis of existing data sets can reveal new insights and that the large amount of publicly available cancer data sets should be further utilized in a similar manner.

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Related in: MedlinePlus

Relationship of HIF1A and VEGFA to top meta-analysis genes. Relationships from the literature among HIF1A, VEGFA, and some of the top 30 meta-analysis genes.
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Figure 3: Relationship of HIF1A and VEGFA to top meta-analysis genes. Relationships from the literature among HIF1A, VEGFA, and some of the top 30 meta-analysis genes.

Mentions: Several themes emerged from the pathway analysis. The most statistically significant gene set identified in the meta-analysis was the hypoxia-inducible factor (HIF) pathway, which has been repeatedly implicated in GBM. This pathway of 31 genes was highly upregulated in GBM and performed extremely well in all four studies, ranking #1 twice, #3, and #9. The HIF1A gene encodes a transcription factor that is induced by hypoxia and that controls the expression of a set of genes that promote angiogenesis and invasion. Importantly, several of the top 30 genes on the meta-analysis gene list are direct HIF1A transcriptional targets, including VEGFA [50,51], ADM [52], IGFBP2 [53], LDHA [54], and FN1 [55]. VEGF, in turn, induces expression of a number of collage subtypes and extracellular matrix proteins needed for the generation of new blood vessels and for invasion [56]. Among these are several additional genes listed among the top 30 on the meta-analysis list, including LAMC, COL4A1, COL4A2, COL1A2 and FN1. TMSB10 can also be regulated by VEGF [57]. When considered together, these genes suggest differential activation of the HIF1A/VEGF network in GBM when compared to AA. Figure 3 illustrates the interrelationship between HIF1A, VEGFA (whose pathway is ranked 21st), and related genes that are found among the top 30 on the meta-analysis list.


Meta-analysis of glioblastoma multiforme versus anaplastic astrocytoma identifies robust gene markers.

Dreyfuss JM, Johnson MD, Park PJ - Mol. Cancer (2009)

Relationship of HIF1A and VEGFA to top meta-analysis genes. Relationships from the literature among HIF1A, VEGFA, and some of the top 30 meta-analysis genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2743637&req=5

Figure 3: Relationship of HIF1A and VEGFA to top meta-analysis genes. Relationships from the literature among HIF1A, VEGFA, and some of the top 30 meta-analysis genes.
Mentions: Several themes emerged from the pathway analysis. The most statistically significant gene set identified in the meta-analysis was the hypoxia-inducible factor (HIF) pathway, which has been repeatedly implicated in GBM. This pathway of 31 genes was highly upregulated in GBM and performed extremely well in all four studies, ranking #1 twice, #3, and #9. The HIF1A gene encodes a transcription factor that is induced by hypoxia and that controls the expression of a set of genes that promote angiogenesis and invasion. Importantly, several of the top 30 genes on the meta-analysis gene list are direct HIF1A transcriptional targets, including VEGFA [50,51], ADM [52], IGFBP2 [53], LDHA [54], and FN1 [55]. VEGF, in turn, induces expression of a number of collage subtypes and extracellular matrix proteins needed for the generation of new blood vessels and for invasion [56]. Among these are several additional genes listed among the top 30 on the meta-analysis list, including LAMC, COL4A1, COL4A2, COL1A2 and FN1. TMSB10 can also be regulated by VEGF [57]. When considered together, these genes suggest differential activation of the HIF1A/VEGF network in GBM when compared to AA. Figure 3 illustrates the interrelationship between HIF1A, VEGFA (whose pathway is ranked 21st), and related genes that are found among the top 30 on the meta-analysis list.

Bottom Line: We found >900 statistically significant probe sets after correction for multiple testing from the >22,000 tested.Our analysis suggests that many of the most statistically significant genes work together in a HIF1A/VEGF-regulated network to increase angiogenesis and invasion in GBM when compared to AA.These feature lists could be utilized to aid in diagnosis, prognosis, and grade reduction of high-grade gliomas and to identify genes that were not previously suspected of playing an important role in glioma biology.

View Article: PubMed Central - HTML - PubMed

Affiliation: Partners HealthCare Center for Personalized Genetic Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA. jdreyfuss1@partners.org

ABSTRACT

Background: Anaplastic astrocytoma (AA) and its more aggressive counterpart, glioblastoma multiforme (GBM), are the most common intrinsic brain tumors in adults and are almost universally fatal. A deeper understanding of the molecular relationship of these tumor types is necessary to derive insights into the diagnosis, prognosis, and treatment of gliomas. Although genomewide profiling of expression levels with microarrays can be used to identify differentially expressed genes between these tumor types, comparative studies so far have resulted in gene lists that show little overlap.

Results: To achieve a more accurate and stable list of the differentially expressed genes and pathways between primary GBM and AA, we performed a meta-analysis using publicly available genome-scale mRNA data sets. There were four data sets with sufficiently large sample sizes of both GBMs and AAs, all of which coincidentally used human U133 platforms from Affymetrix, allowing for easier and more precise integration of data. After scoring genes and pathways within each data set, we combined the statistics across studies using the nonparametric rank sum method to identify the features that differentiate GBMs and AAs. We found >900 statistically significant probe sets after correction for multiple testing from the >22,000 tested. We also used the rank sum approach to select >20 significant Biocarta pathways after correction for multiple testing out of >175 pathways examined. The most significant pathway was the hypoxia-inducible factor (HIF) pathway. Our analysis suggests that many of the most statistically significant genes work together in a HIF1A/VEGF-regulated network to increase angiogenesis and invasion in GBM when compared to AA.

Conclusion: We have performed a meta-analysis of genome-scale mRNA expression data for 289 human malignant gliomas and have identified a list of >900 probe sets and >20 pathways that are significantly different between GBM and AA. These feature lists could be utilized to aid in diagnosis, prognosis, and grade reduction of high-grade gliomas and to identify genes that were not previously suspected of playing an important role in glioma biology. More generally, this approach suggests that combined analysis of existing data sets can reveal new insights and that the large amount of publicly available cancer data sets should be further utilized in a similar manner.

Show MeSH
Related in: MedlinePlus