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An integrative analysis of meningioma tumors reveals the determinant genes and pathways of malignant transformation.

Iglesias Gómez JC, Mosquera Orgueira A - Front Oncol (2014)

Bottom Line: Thus, this study is aimed to identify the genomic and transcriptomic factors influencing evolution from benignity toward aggressive phenotypes.By applying an integrative bioinformatics pipeline combining public information on a wealth of biological layers of complexity (from genetic polymorphisms to protein interactions), this study identified a module of co-expressed genes highly correlated with tumor stage and statistically linked to several genomic regions (module Quantitative Trait Loci, mQTLs).As a result, cytoskeleton and cell-cell adhesion pathways, calcium-channels and glutamate receptors, as well as oxidoreductase and endoplasmic reticulum-associated degradation pathways were found to be the most important and redundant findings associated to meningioma progression.

View Article: PubMed Central - PubMed

Affiliation: Independent Video Editor , Santiago de Compostela , Spain.

ABSTRACT
Meningiomas are frequent central nervous system neoplasms, which despite their predominant benignity, show sporadically malignant behavior. Type 2 neurofibromatosis and polymorphisms in several genes have been associated with meningioma risk and are probably involved in its pathogenesis. Although GWAS studies have found loci related to meningioma risk, little is known about the factors determining malignant transformation. Thus, this study is aimed to identify the genomic and transcriptomic factors influencing evolution from benignity toward aggressive phenotypes. By applying an integrative bioinformatics pipeline combining public information on a wealth of biological layers of complexity (from genetic polymorphisms to protein interactions), this study identified a module of co-expressed genes highly correlated with tumor stage and statistically linked to several genomic regions (module Quantitative Trait Loci, mQTLs). Ontology analysis of the transcription hub genes identified microtubule-associated cell-cycle processes as key drivers of such network. mQTLs and single nucleotide polymorphisms associated with meningioma stage were replicated in an alternative meningioma cohort, and integration of these results with up-to-date scientific literature and several databases retrieved a list of genes and pathways with a potentially important role in meningioma malignancy. As a result, cytoskeleton and cell-cell adhesion pathways, calcium-channels and glutamate receptors, as well as oxidoreductase and endoplasmic reticulum-associated degradation pathways were found to be the most important and redundant findings associated to meningioma progression. This study presents an integrated view of the pathways involved in meningioma malignant conversion and paves the way for the development of new research lines that will improve our understanding of meningioma biology.

No MeSH data available.


Related in: MedlinePlus

Module–trait relationships plot. Spearman’s correlation between module principal components (a.k.a module eigengenes, MEs) and Age by decade (first column), Gender (second column), WHO Meningioma classification (third column), recurrence frequency (fourth column), recurrence code (recurrent vs. newly diagnosed, fifth column), recurrence after sample (sixth column), maximum Ki-67 step function (absent = 0, low = 1, medium = 2, high = 3; seventh column), sum of chromosome arm losses (eighth column), and Chromosome 22p deletion (ninth column) is shown.
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Figure 2: Module–trait relationships plot. Spearman’s correlation between module principal components (a.k.a module eigengenes, MEs) and Age by decade (first column), Gender (second column), WHO Meningioma classification (third column), recurrence frequency (fourth column), recurrence code (recurrent vs. newly diagnosed, fifth column), recurrence after sample (sixth column), maximum Ki-67 step function (absent = 0, low = 1, medium = 2, high = 3; seventh column), sum of chromosome arm losses (eighth column), and Chromosome 22p deletion (ninth column) is shown.

Mentions: Weighted gene co-expression network analysis identified 16 co-expression modules. Module–trait relationships revealed that the pink ME was highly and significantly correlated with WHO Meningioma Grade, recurrence frequency, observed recurrence after sample, Ki-67 staining, and with the total sum of chromosome arm losses (Figure 2). Boxplots of mean gene significance with WHO_Grade revealed that the Pink-module contains the most correlated genes (Figure S1A in Supplementary Material). MM (a.k.a kME) to the sixth power was determined to be in a marked 0.88 Spearman’s correlation (P-value <10−16) with intramodular connectivity (kIN), which is indicative of its usefulness to study high-level modular network properties. MM vs. GS WHO_Grade (Figure S1B in Supplementary Material) revealed a 0.72 Spearman’s correlation value (P-value ≤10−16), showing that genes importantly associated with disease stage are also the more relevant in the module. Pink-module gene-expression standard deviation was found to be inversely correlated with kIN (Spearman’s rho = −0.06, one-sided P-value = 0.09), but curiously higher levels of expression variability co-exist with low levels of connectivity (Figure S1C in Supplementary Material). For example, genes with a kIN below 2 are significantly more variable than those whose kIN value is above 2 (Wilcoxon Rank Sum Test P-value = 0.002, 95% C.I. = 7.74–36.96). This is consistent with the role of hub genes as central players in complex biological phenotypes (48). Moreover, almost 76% of the pink-module genes found in the replication cohort were significantly differentially expressed between tumor stages (FDR-adjusted P-value <0.05), and differentially expressed genes had a significantly greater connectivity (MM or kME) than the overall group of pink-module genes (Wilcoxon Rank Sum Test P-value = 0.043, 95% C.I. = 6.54 × 10−5–3.70 × 10−2).


An integrative analysis of meningioma tumors reveals the determinant genes and pathways of malignant transformation.

Iglesias Gómez JC, Mosquera Orgueira A - Front Oncol (2014)

Module–trait relationships plot. Spearman’s correlation between module principal components (a.k.a module eigengenes, MEs) and Age by decade (first column), Gender (second column), WHO Meningioma classification (third column), recurrence frequency (fourth column), recurrence code (recurrent vs. newly diagnosed, fifth column), recurrence after sample (sixth column), maximum Ki-67 step function (absent = 0, low = 1, medium = 2, high = 3; seventh column), sum of chromosome arm losses (eighth column), and Chromosome 22p deletion (ninth column) is shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4066933&req=5

Figure 2: Module–trait relationships plot. Spearman’s correlation between module principal components (a.k.a module eigengenes, MEs) and Age by decade (first column), Gender (second column), WHO Meningioma classification (third column), recurrence frequency (fourth column), recurrence code (recurrent vs. newly diagnosed, fifth column), recurrence after sample (sixth column), maximum Ki-67 step function (absent = 0, low = 1, medium = 2, high = 3; seventh column), sum of chromosome arm losses (eighth column), and Chromosome 22p deletion (ninth column) is shown.
Mentions: Weighted gene co-expression network analysis identified 16 co-expression modules. Module–trait relationships revealed that the pink ME was highly and significantly correlated with WHO Meningioma Grade, recurrence frequency, observed recurrence after sample, Ki-67 staining, and with the total sum of chromosome arm losses (Figure 2). Boxplots of mean gene significance with WHO_Grade revealed that the Pink-module contains the most correlated genes (Figure S1A in Supplementary Material). MM (a.k.a kME) to the sixth power was determined to be in a marked 0.88 Spearman’s correlation (P-value <10−16) with intramodular connectivity (kIN), which is indicative of its usefulness to study high-level modular network properties. MM vs. GS WHO_Grade (Figure S1B in Supplementary Material) revealed a 0.72 Spearman’s correlation value (P-value ≤10−16), showing that genes importantly associated with disease stage are also the more relevant in the module. Pink-module gene-expression standard deviation was found to be inversely correlated with kIN (Spearman’s rho = −0.06, one-sided P-value = 0.09), but curiously higher levels of expression variability co-exist with low levels of connectivity (Figure S1C in Supplementary Material). For example, genes with a kIN below 2 are significantly more variable than those whose kIN value is above 2 (Wilcoxon Rank Sum Test P-value = 0.002, 95% C.I. = 7.74–36.96). This is consistent with the role of hub genes as central players in complex biological phenotypes (48). Moreover, almost 76% of the pink-module genes found in the replication cohort were significantly differentially expressed between tumor stages (FDR-adjusted P-value <0.05), and differentially expressed genes had a significantly greater connectivity (MM or kME) than the overall group of pink-module genes (Wilcoxon Rank Sum Test P-value = 0.043, 95% C.I. = 6.54 × 10−5–3.70 × 10−2).

Bottom Line: Thus, this study is aimed to identify the genomic and transcriptomic factors influencing evolution from benignity toward aggressive phenotypes.By applying an integrative bioinformatics pipeline combining public information on a wealth of biological layers of complexity (from genetic polymorphisms to protein interactions), this study identified a module of co-expressed genes highly correlated with tumor stage and statistically linked to several genomic regions (module Quantitative Trait Loci, mQTLs).As a result, cytoskeleton and cell-cell adhesion pathways, calcium-channels and glutamate receptors, as well as oxidoreductase and endoplasmic reticulum-associated degradation pathways were found to be the most important and redundant findings associated to meningioma progression.

View Article: PubMed Central - PubMed

Affiliation: Independent Video Editor , Santiago de Compostela , Spain.

ABSTRACT
Meningiomas are frequent central nervous system neoplasms, which despite their predominant benignity, show sporadically malignant behavior. Type 2 neurofibromatosis and polymorphisms in several genes have been associated with meningioma risk and are probably involved in its pathogenesis. Although GWAS studies have found loci related to meningioma risk, little is known about the factors determining malignant transformation. Thus, this study is aimed to identify the genomic and transcriptomic factors influencing evolution from benignity toward aggressive phenotypes. By applying an integrative bioinformatics pipeline combining public information on a wealth of biological layers of complexity (from genetic polymorphisms to protein interactions), this study identified a module of co-expressed genes highly correlated with tumor stage and statistically linked to several genomic regions (module Quantitative Trait Loci, mQTLs). Ontology analysis of the transcription hub genes identified microtubule-associated cell-cycle processes as key drivers of such network. mQTLs and single nucleotide polymorphisms associated with meningioma stage were replicated in an alternative meningioma cohort, and integration of these results with up-to-date scientific literature and several databases retrieved a list of genes and pathways with a potentially important role in meningioma malignancy. As a result, cytoskeleton and cell-cell adhesion pathways, calcium-channels and glutamate receptors, as well as oxidoreductase and endoplasmic reticulum-associated degradation pathways were found to be the most important and redundant findings associated to meningioma progression. This study presents an integrated view of the pathways involved in meningioma malignant conversion and paves the way for the development of new research lines that will improve our understanding of meningioma biology.

No MeSH data available.


Related in: MedlinePlus