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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

CAT plots. Concordance At the Top plots comparing ranked probe set lists from pairs of studies (top left) and from each study to the meta-analysis probe set list (top right), and similarly for pathways (bottom row).
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Figure 2: CAT plots. Concordance At the Top plots comparing ranked probe set lists from pairs of studies (top left) and from each study to the meta-analysis probe set list (top right), and similarly for pathways (bottom row).

Mentions: The top row of Figure 2 displays these findings through Concordance At the Top (CAT) plots [40], which quantify the concordance of two lists along list ranks. These plots have a straightforward interpretation. For example, if two gene lists share in common 80 of their top 100 genes, then at rank 100 their concordance would be 80%. Hence, the gene plots in Figure 2 show that no study dominates the final meta-analysis gene list and that these studies' gene lists, although far more concordant than would be expected by chance, contain ample heterogeneity. This coupling of apparent heterogeneity with abundant meta-analytic significance points to a wealth of information only available through a powerful meta-analysis.


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

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

CAT plots. Concordance At the Top plots comparing ranked probe set lists from pairs of studies (top left) and from each study to the meta-analysis probe set list (top right), and similarly for pathways (bottom row).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: CAT plots. Concordance At the Top plots comparing ranked probe set lists from pairs of studies (top left) and from each study to the meta-analysis probe set list (top right), and similarly for pathways (bottom row).
Mentions: The top row of Figure 2 displays these findings through Concordance At the Top (CAT) plots [40], which quantify the concordance of two lists along list ranks. These plots have a straightforward interpretation. For example, if two gene lists share in common 80 of their top 100 genes, then at rank 100 their concordance would be 80%. Hence, the gene plots in Figure 2 show that no study dominates the final meta-analysis gene list and that these studies' gene lists, although far more concordant than would be expected by chance, contain ample heterogeneity. This coupling of apparent heterogeneity with abundant meta-analytic significance points to a wealth of information only available through a powerful meta-analysis.

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