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Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

Llorens F, Hummel M, Pastor X, Ferrer A, Pluvinet R, Vivancos A, Castillo E, Iraola S, Mosquera AM, González E, Lozano J, Ingham M, Dohm JC, Noguera M, Kofler R, del Río JA, Bayés M, Himmelbauer H, Sumoy L - BMC Genomics (2011)

Bottom Line: In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets.This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genomic Regulation (CRG)-Universitat Pompeu Fabra (UPF), Barcelona, Spain.

ABSTRACT

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.

Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.

Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

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Related in: MedlinePlus

Significant pathways and interactions among EGF-regulated geneset. (A) Core functional analysis of EGF-regulated genes derived from the RankProd test clustering around canonical pathways performed using the Ingenuity Pathway Analysis software. (B) Pathway analysis based on the Ingenuity Pathway Knowledge base. The two best ranked networks holding EGF-regulated genes derived from the RankProd test were merged showing a unique network. Up-regulated genes are indicated in red and down-regulated genes in green. The shape of the node denotes the main function of the protein encoded by the gene (see boxed inset). Continuous lines indicate interaction between the products of the genes; dashed lines indicate an indirect interaction; lines with an arrow indicate that the source gene "acts on" the target gene. Regulated genes are shown as grey boxes and non-regulated but associated with the regulation of some of these genes are shown as white nodes. Orange lines indicate new gene relationships appearing after merging different networks.
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Figure 6: Significant pathways and interactions among EGF-regulated geneset. (A) Core functional analysis of EGF-regulated genes derived from the RankProd test clustering around canonical pathways performed using the Ingenuity Pathway Analysis software. (B) Pathway analysis based on the Ingenuity Pathway Knowledge base. The two best ranked networks holding EGF-regulated genes derived from the RankProd test were merged showing a unique network. Up-regulated genes are indicated in red and down-regulated genes in green. The shape of the node denotes the main function of the protein encoded by the gene (see boxed inset). Continuous lines indicate interaction between the products of the genes; dashed lines indicate an indirect interaction; lines with an arrow indicate that the source gene "acts on" the target gene. Regulated genes are shown as grey boxes and non-regulated but associated with the regulation of some of these genes are shown as white nodes. Orange lines indicate new gene relationships appearing after merging different networks.

Mentions: To further investigate the global expression response to EGF treatment as well as to study the interaction between individual regulated genes and how they have a coordinated role in specific signaling pathways, we used the IPA (Ingenuity Pathway Analysis) software, using the 1146 genes obtained by RankProd testing (adjusted p-value: p < 0.05, median absolute fold change of all measurements: /FC/ > 1.2). Among the top molecular and cellular categories, we observed the presence of the most common functions related to EGF signaling such as cell death, cell growth and proliferation [1], being cancer the top disease. In all cases, the biological functions identified have a very high overlap in gene content. This is in agreement with the top regulated canonical pathways described by IPA which are: cell death, cancer, and cellular growth and proliferation. (Figure 6A and Table 1).


Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.

Llorens F, Hummel M, Pastor X, Ferrer A, Pluvinet R, Vivancos A, Castillo E, Iraola S, Mosquera AM, González E, Lozano J, Ingham M, Dohm JC, Noguera M, Kofler R, del Río JA, Bayés M, Himmelbauer H, Sumoy L - BMC Genomics (2011)

Significant pathways and interactions among EGF-regulated geneset. (A) Core functional analysis of EGF-regulated genes derived from the RankProd test clustering around canonical pathways performed using the Ingenuity Pathway Analysis software. (B) Pathway analysis based on the Ingenuity Pathway Knowledge base. The two best ranked networks holding EGF-regulated genes derived from the RankProd test were merged showing a unique network. Up-regulated genes are indicated in red and down-regulated genes in green. The shape of the node denotes the main function of the protein encoded by the gene (see boxed inset). Continuous lines indicate interaction between the products of the genes; dashed lines indicate an indirect interaction; lines with an arrow indicate that the source gene "acts on" the target gene. Regulated genes are shown as grey boxes and non-regulated but associated with the regulation of some of these genes are shown as white nodes. Orange lines indicate new gene relationships appearing after merging different networks.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3141672&req=5

Figure 6: Significant pathways and interactions among EGF-regulated geneset. (A) Core functional analysis of EGF-regulated genes derived from the RankProd test clustering around canonical pathways performed using the Ingenuity Pathway Analysis software. (B) Pathway analysis based on the Ingenuity Pathway Knowledge base. The two best ranked networks holding EGF-regulated genes derived from the RankProd test were merged showing a unique network. Up-regulated genes are indicated in red and down-regulated genes in green. The shape of the node denotes the main function of the protein encoded by the gene (see boxed inset). Continuous lines indicate interaction between the products of the genes; dashed lines indicate an indirect interaction; lines with an arrow indicate that the source gene "acts on" the target gene. Regulated genes are shown as grey boxes and non-regulated but associated with the regulation of some of these genes are shown as white nodes. Orange lines indicate new gene relationships appearing after merging different networks.
Mentions: To further investigate the global expression response to EGF treatment as well as to study the interaction between individual regulated genes and how they have a coordinated role in specific signaling pathways, we used the IPA (Ingenuity Pathway Analysis) software, using the 1146 genes obtained by RankProd testing (adjusted p-value: p < 0.05, median absolute fold change of all measurements: /FC/ > 1.2). Among the top molecular and cellular categories, we observed the presence of the most common functions related to EGF signaling such as cell death, cell growth and proliferation [1], being cancer the top disease. In all cases, the biological functions identified have a very high overlap in gene content. This is in agreement with the top regulated canonical pathways described by IPA which are: cell death, cancer, and cellular growth and proliferation. (Figure 6A and Table 1).

Bottom Line: In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets.This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

View Article: PubMed Central - HTML - PubMed

Affiliation: Center for Genomic Regulation (CRG)-Universitat Pompeu Fabra (UPF), Barcelona, Spain.

ABSTRACT

Background: Epidermal Growth Factor (EGF) is a key regulatory growth factor activating many processes relevant to normal development and disease, affecting cell proliferation and survival. Here we use a combined approach to study the EGF dependent transcriptome of HeLa cells by using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer.

Results: By applying a procedure for cross-platform data meta-analysis based on RankProd and GlobalAncova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We use this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we find an entirely new set of genes previously unrelated to the currently accepted EGF associated cellular functions.

Conclusions: We propose that the use of global genomic cross-validation derived from high content technologies (microarrays or deep sequencing) can be used to generate more reliable datasets. This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data.

Show MeSH
Related in: MedlinePlus