Systems biology approach to identify gene network signatures for colorectal cancer
In this work, we integrated prior knowledge from GWAS studies or gene signatures (curated gene sets from MSigDB and/or GeneSigDB), gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from microarray data. We demonstrated how to apply th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-05-01
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| Series: | Frontiers in Genetics |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00080/full |
| _version_ | 1819046720140476416 |
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| author | Madhankumar eSonachalam Jeffrey eShen Hui eHuang Xiaogang eWu |
| author_facet | Madhankumar eSonachalam Jeffrey eShen Hui eHuang Xiaogang eWu |
| author_sort | Madhankumar eSonachalam |
| collection | DOAJ |
| description | In this work, we integrated prior knowledge from GWAS studies or gene signatures (curated gene sets from MSigDB and/or GeneSigDB), gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from microarray data. We demonstrated how to apply this approach into discovering gene network signatures for colorectal cancer (CRC) from microarray datasets at three levels - genome, transcriptome, and proteome. First, we use GSEA to analyze the microarray data through enriching differential genes in different CRC-related gene sets from two publicly-available up-to-date gene set databases - Molecular Signatures Database (MSigDB) and Gene Signatures Database (GeneSigDB). Second, we compare the enriched gene sets through enrichment score (ES), false-discovery rate (FDR) and nominal p-value. Third, we construct an integrated protein-protein interaction (PPI) network through connecting these enriched genes by using a human annotated and predicted protein interaction (HAPPI) database, with a confidence score (CS) labeled for each interaction. Finally, we map differential expression values onto the constructed network to build a comprehensive network model containing visualized genome, transcriptome, and proteome data. The results show that although MSigDB is more suitable for GSEA analysis than GeneSigDB, the integrated PPI network connecting the enriched genes from both MSigDB and GeneSigDB can provide more complete view for discovering gene signatures. We also find several important sub-network signatures for colorectal cancer, such as TP53 sub-network, PCNA sub-network and IL8 sub-network, corresponding to apoptosis, DNA repair, and immune response respectively. |
| first_indexed | 2024-12-21T10:48:57Z |
| format | Article |
| id | doaj.art-f8f8a5508bc444d2b829e2fc2c84473c |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-21T10:48:57Z |
| publishDate | 2012-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-f8f8a5508bc444d2b829e2fc2c84473c2022-12-21T19:06:44ZengFrontiers Media S.A.Frontiers in Genetics1664-80212012-05-01310.3389/fgene.2012.0008021698Systems biology approach to identify gene network signatures for colorectal cancerMadhankumar eSonachalam0Jeffrey eShen1Hui eHuang2Xiaogang eWu3Indiana University-Purdue University IndianapolisIndiana University-Purdue University IndianapolisIndiana University-Purdue University IndianapolisIndiana University-Purdue University IndianapolisIn this work, we integrated prior knowledge from GWAS studies or gene signatures (curated gene sets from MSigDB and/or GeneSigDB), gene set enrichment analysis (GSEA), and gene/protein network modeling together to identify gene network signatures from microarray data. We demonstrated how to apply this approach into discovering gene network signatures for colorectal cancer (CRC) from microarray datasets at three levels - genome, transcriptome, and proteome. First, we use GSEA to analyze the microarray data through enriching differential genes in different CRC-related gene sets from two publicly-available up-to-date gene set databases - Molecular Signatures Database (MSigDB) and Gene Signatures Database (GeneSigDB). Second, we compare the enriched gene sets through enrichment score (ES), false-discovery rate (FDR) and nominal p-value. Third, we construct an integrated protein-protein interaction (PPI) network through connecting these enriched genes by using a human annotated and predicted protein interaction (HAPPI) database, with a confidence score (CS) labeled for each interaction. Finally, we map differential expression values onto the constructed network to build a comprehensive network model containing visualized genome, transcriptome, and proteome data. The results show that although MSigDB is more suitable for GSEA analysis than GeneSigDB, the integrated PPI network connecting the enriched genes from both MSigDB and GeneSigDB can provide more complete view for discovering gene signatures. We also find several important sub-network signatures for colorectal cancer, such as TP53 sub-network, PCNA sub-network and IL8 sub-network, corresponding to apoptosis, DNA repair, and immune response respectively.http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00080/fullMicroarray Analysiscolorectal cancernetwork biologygene set enrichment analysisgene expression signatures |
| spellingShingle | Madhankumar eSonachalam Jeffrey eShen Hui eHuang Xiaogang eWu Systems biology approach to identify gene network signatures for colorectal cancer Frontiers in Genetics Microarray Analysis colorectal cancer network biology gene set enrichment analysis gene expression signatures |
| title | Systems biology approach to identify gene network signatures for colorectal cancer |
| title_full | Systems biology approach to identify gene network signatures for colorectal cancer |
| title_fullStr | Systems biology approach to identify gene network signatures for colorectal cancer |
| title_full_unstemmed | Systems biology approach to identify gene network signatures for colorectal cancer |
| title_short | Systems biology approach to identify gene network signatures for colorectal cancer |
| title_sort | systems biology approach to identify gene network signatures for colorectal cancer |
| topic | Microarray Analysis colorectal cancer network biology gene set enrichment analysis gene expression signatures |
| url | http://journal.frontiersin.org/Journal/10.3389/fgene.2012.00080/full |
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