Limits...
Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression.

Shi Z, Derow CK, Zhang B - BMC Syst Biol (2010)

Bottom Line: Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories.IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules.Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression.

View Article: PubMed Central - HTML - PubMed

Affiliation: Advanced Computing Center for Research & Education, Vanderbilt University, Nashville, TN 37240, USA.

ABSTRACT

Background: Gene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability.

Results: We have developed a novel algorithm Iterative Clique Enumeration (ICE) for identifying relatively independent maximal cliques as co-expression modules and a module-based approach to the analysis of gene expression data. Applying this approach on a public breast cancer dataset identified 19 modules whose expression levels were significantly correlated with tumor grade. The correlations were reproducible for 17 modules in an independent breast cancer dataset, and the reproducibility was considerably higher than that based on individual genes or modules identified by other algorithms. Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories. Specifically, modules related to cell proliferation and immune response were up-regulated in high-grade tumors while those related to cell adhesion was down-regulated. Further analyses showed that transcription factors NYFB, E2F1/E2F3, NRF1, and ELK1 were responsible for the up-regulation of the cell proliferation modules. IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules. Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression. The module without GO enrichment was found to be associated with a potential genomic gain in 8q21-23 in high-grade tumors. The 17-module signature of breast tumor progression clustered patients into subgroups with significantly different relapse-free survival times. Namely, patients with lower cell proliferation and higher cell adhesion levels had significantly lower risk of recurrence, both for all patients (p = 0.004) and for those with grade 2 tumors (p = 0.017).

Conclusions: The ICE algorithm is effective in identifying relatively independent co-expression modules from gene co-expression networks and the module-based approach illustrated in this study provides a robust, interpretable, and mechanistic characterization of transcriptional changes.

Show MeSH

Related in: MedlinePlus

Expression profile of genes in the chromosome region 8q21-23. Expression data for genes in the chromosome region 8q21-23 were collected from the GSE2109 dataset and visualized in a heat map. Rows present genes and columns represent samples. Genes are ordered by their chromosome location and colored-coded on the left by three cytogenetic bands 8q21, 8q22, and 8q23. Genes in module_36 are marked in red and labeled on the left. Samples are color-coded on the top by tumor grade, where blue, cyan, and pink correspond to grades 1, 2, and 3, respectively. The color scale bar at the bottom shows the relative gene expression level (0 is the mean expression level of a given gene).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2902438&req=5

Figure 5: Expression profile of genes in the chromosome region 8q21-23. Expression data for genes in the chromosome region 8q21-23 were collected from the GSE2109 dataset and visualized in a heat map. Rows present genes and columns represent samples. Genes are ordered by their chromosome location and colored-coded on the left by three cytogenetic bands 8q21, 8q22, and 8q23. Genes in module_36 are marked in red and labeled on the left. Samples are color-coded on the top by tumor grade, where blue, cyan, and pink correspond to grades 1, 2, and 3, respectively. The color scale bar at the bottom shows the relative gene expression level (0 is the mean expression level of a given gene).

Mentions: To check whether this enrichment was due to the genomic gain of 8q21-23 in high-grade tumors, we plotted the gene expression data for all genes in this region based on the GSE2109_breast dataset (Figure 5). It is clear from the figure that not only the 13 genes, the majority of genes in this region showed consistent and elevated expression in high-grade tumors. Previously, genomic gain at 8q22 in breast tumor samples has been reported by independent groups and has been associated with poor-prognosis [37,38]. A recent study identified MTDH, one gene in module_36, as the most significant functional mediator of this poor-prognosis genomic gain [37]. Our results indicate that the genomic gain may affect a broader region including 8q21, 8q22, and 8q23. Interestingly, a published bioinformatics study using 12 independent human breast cancer microarray studies comprising 1422 tumor samples also identified 8q21-23 as a potential aberrant chromosomal region [39]. It is not clear why this region is consistently duplicated in high-grade tumors. However, because Module_36 is moderately correlated with the cell proliferation modules (Figure 4), we hypothesize that this genomic instability may be associated with increased cell proliferation.


Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression.

Shi Z, Derow CK, Zhang B - BMC Syst Biol (2010)

Expression profile of genes in the chromosome region 8q21-23. Expression data for genes in the chromosome region 8q21-23 were collected from the GSE2109 dataset and visualized in a heat map. Rows present genes and columns represent samples. Genes are ordered by their chromosome location and colored-coded on the left by three cytogenetic bands 8q21, 8q22, and 8q23. Genes in module_36 are marked in red and labeled on the left. Samples are color-coded on the top by tumor grade, where blue, cyan, and pink correspond to grades 1, 2, and 3, respectively. The color scale bar at the bottom shows the relative gene expression level (0 is the mean expression level of a given gene).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902438&req=5

Figure 5: Expression profile of genes in the chromosome region 8q21-23. Expression data for genes in the chromosome region 8q21-23 were collected from the GSE2109 dataset and visualized in a heat map. Rows present genes and columns represent samples. Genes are ordered by their chromosome location and colored-coded on the left by three cytogenetic bands 8q21, 8q22, and 8q23. Genes in module_36 are marked in red and labeled on the left. Samples are color-coded on the top by tumor grade, where blue, cyan, and pink correspond to grades 1, 2, and 3, respectively. The color scale bar at the bottom shows the relative gene expression level (0 is the mean expression level of a given gene).
Mentions: To check whether this enrichment was due to the genomic gain of 8q21-23 in high-grade tumors, we plotted the gene expression data for all genes in this region based on the GSE2109_breast dataset (Figure 5). It is clear from the figure that not only the 13 genes, the majority of genes in this region showed consistent and elevated expression in high-grade tumors. Previously, genomic gain at 8q22 in breast tumor samples has been reported by independent groups and has been associated with poor-prognosis [37,38]. A recent study identified MTDH, one gene in module_36, as the most significant functional mediator of this poor-prognosis genomic gain [37]. Our results indicate that the genomic gain may affect a broader region including 8q21, 8q22, and 8q23. Interestingly, a published bioinformatics study using 12 independent human breast cancer microarray studies comprising 1422 tumor samples also identified 8q21-23 as a potential aberrant chromosomal region [39]. It is not clear why this region is consistently duplicated in high-grade tumors. However, because Module_36 is moderately correlated with the cell proliferation modules (Figure 4), we hypothesize that this genomic instability may be associated with increased cell proliferation.

Bottom Line: Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories.IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules.Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression.

View Article: PubMed Central - HTML - PubMed

Affiliation: Advanced Computing Center for Research & Education, Vanderbilt University, Nashville, TN 37240, USA.

ABSTRACT

Background: Gene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability.

Results: We have developed a novel algorithm Iterative Clique Enumeration (ICE) for identifying relatively independent maximal cliques as co-expression modules and a module-based approach to the analysis of gene expression data. Applying this approach on a public breast cancer dataset identified 19 modules whose expression levels were significantly correlated with tumor grade. The correlations were reproducible for 17 modules in an independent breast cancer dataset, and the reproducibility was considerably higher than that based on individual genes or modules identified by other algorithms. Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories. Specifically, modules related to cell proliferation and immune response were up-regulated in high-grade tumors while those related to cell adhesion was down-regulated. Further analyses showed that transcription factors NYFB, E2F1/E2F3, NRF1, and ELK1 were responsible for the up-regulation of the cell proliferation modules. IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules. Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression. The module without GO enrichment was found to be associated with a potential genomic gain in 8q21-23 in high-grade tumors. The 17-module signature of breast tumor progression clustered patients into subgroups with significantly different relapse-free survival times. Namely, patients with lower cell proliferation and higher cell adhesion levels had significantly lower risk of recurrence, both for all patients (p = 0.004) and for those with grade 2 tumors (p = 0.017).

Conclusions: The ICE algorithm is effective in identifying relatively independent co-expression modules from gene co-expression networks and the module-based approach illustrated in this study provides a robust, interpretable, and mechanistic characterization of transcriptional changes.

Show MeSH
Related in: MedlinePlus