Using random walks to identify cancer-associated modules in expression data
The etiology of cancer involves a complex series of genetic and environmental conditions. To better represent and study the intricate genetics of cancer onset and progression, we construct a network of biological interactions to search for groups of genes that compose cancer-related modules. Three c...
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| creator | Petrochilos, Deanna Shojaie, Ali Gennari, John Abernethy, Neil |
| description | The etiology of cancer involves a complex series of genetic and environmental conditions. To better represent and study the intricate genetics of cancer onset and progression, we construct a network of biological interactions to search for groups of genes that compose cancer-related modules. Three cancer expression datasets are investigated to prioritize genes and interactions associated with cancer outcomes. Using a graph-based approach to search for communities of phenotype-related genes in microarray data, we find modules of genes associated with cancer phenotypes in a weighted interaction network.
We implement Walktrap, a random-walk-based community detection algorithm, to identify biological modules predisposing to tumor growth in 22 hepatocellular carcinoma samples (GSE14520), adenoma development in 32 colorectal cancer samples (GSE8671), and prognosis in 198 breast cancer patients (GSE7390). For each study, we find the best scoring partitions under a maximum cluster size of 200 nodes. Significant modules highlight groups of genes that are functionally related to cancer and show promise as therapeutic targets; these include interactions among transcription factors (SPIB, RPS6KA2 and RPS6KA6), cell-cycle regulatory genes (BRSK1, WEE1 and CDC25C), modulators of the cell-cycle and proliferation (CBLC and IRS2) and genes that regulate and participate in the map-kinase pathway (MAPK9, DUSP1, DUSP9, RIPK2). To assess the performance of Walktrap to find genomic modules (Walktrap-GM), we evaluate our results against other tools recently developed to discover disease modules in biological networks. Compared with other highly cited module-finding tools, jActiveModules and Matisse, Walktrap-GM shows strong performance in the discovery of modules enriched with known cancer genes.
These results demonstrate that the Walktrap-GM algorithm identifies modules significantly enriched with cancer genes, their joint effects and promising candidate genes. The approach performs well when evaluated against similar tools and smaller overall module size allows for more specific functional annotation and facilitates the interpretation of these modules. |
| doi_str_mv | 10.1186/1756-0381-6-17 |
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We implement Walktrap, a random-walk-based community detection algorithm, to identify biological modules predisposing to tumor growth in 22 hepatocellular carcinoma samples (GSE14520), adenoma development in 32 colorectal cancer samples (GSE8671), and prognosis in 198 breast cancer patients (GSE7390). For each study, we find the best scoring partitions under a maximum cluster size of 200 nodes. Significant modules highlight groups of genes that are functionally related to cancer and show promise as therapeutic targets; these include interactions among transcription factors (SPIB, RPS6KA2 and RPS6KA6), cell-cycle regulatory genes (BRSK1, WEE1 and CDC25C), modulators of the cell-cycle and proliferation (CBLC and IRS2) and genes that regulate and participate in the map-kinase pathway (MAPK9, DUSP1, DUSP9, RIPK2). To assess the performance of Walktrap to find genomic modules (Walktrap-GM), we evaluate our results against other tools recently developed to discover disease modules in biological networks. Compared with other highly cited module-finding tools, jActiveModules and Matisse, Walktrap-GM shows strong performance in the discovery of modules enriched with known cancer genes.
These results demonstrate that the Walktrap-GM algorithm identifies modules significantly enriched with cancer genes, their joint effects and promising candidate genes. The approach performs well when evaluated against similar tools and smaller overall module size allows for more specific functional annotation and facilitates the interpretation of these modules.</description><identifier>ISSN: 1756-0381</identifier><identifier>EISSN: 1756-0381</identifier><identifier>DOI: 10.1186/1756-0381-6-17</identifier><identifier>PMID: 24128261</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Algorithms ; Analysis ; Cancer ; Cell adhesion & migration ; Cell cycle ; Colorectal cancer ; Computational biology ; DNA binding proteins ; Gene expression ; Genetic aspects ; Genetic transcription ; Genomes ; Genomics ; Hypotheses ; Liver cancer ; Methodology ; Methods ; Oncology, Experimental ; Programming languages ; Proteins ; Studies</subject><ispartof>BioData mining, 2013-10, Vol.6 (1), p.17-17, Article 17</ispartof><rights>COPYRIGHT 2013 BioMed Central Ltd.</rights><rights>2013 Petrochilos et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright © 2013 Petrochilos et al.; licensee BioMed Central Ltd. 2013 Petrochilos et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c552t-43bab8ff2e5ed7f1d5e0e1f4f94101f78a05219e43c693f2dbfe60711d8271a83</citedby><cites>FETCH-LOGICAL-c552t-43bab8ff2e5ed7f1d5e0e1f4f94101f78a05219e43c693f2dbfe60711d8271a83</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4015830/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4015830/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/24128261$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Petrochilos, Deanna</creatorcontrib><creatorcontrib>Shojaie, Ali</creatorcontrib><creatorcontrib>Gennari, John</creatorcontrib><creatorcontrib>Abernethy, Neil</creatorcontrib><title>Using random walks to identify cancer-associated modules in expression data</title><title>BioData mining</title><addtitle>BioData Min</addtitle><description>The etiology of cancer involves a complex series of genetic and environmental conditions. To better represent and study the intricate genetics of cancer onset and progression, we construct a network of biological interactions to search for groups of genes that compose cancer-related modules. Three cancer expression datasets are investigated to prioritize genes and interactions associated with cancer outcomes. Using a graph-based approach to search for communities of phenotype-related genes in microarray data, we find modules of genes associated with cancer phenotypes in a weighted interaction network.
We implement Walktrap, a random-walk-based community detection algorithm, to identify biological modules predisposing to tumor growth in 22 hepatocellular carcinoma samples (GSE14520), adenoma development in 32 colorectal cancer samples (GSE8671), and prognosis in 198 breast cancer patients (GSE7390). For each study, we find the best scoring partitions under a maximum cluster size of 200 nodes. Significant modules highlight groups of genes that are functionally related to cancer and show promise as therapeutic targets; these include interactions among transcription factors (SPIB, RPS6KA2 and RPS6KA6), cell-cycle regulatory genes (BRSK1, WEE1 and CDC25C), modulators of the cell-cycle and proliferation (CBLC and IRS2) and genes that regulate and participate in the map-kinase pathway (MAPK9, DUSP1, DUSP9, RIPK2). To assess the performance of Walktrap to find genomic modules (Walktrap-GM), we evaluate our results against other tools recently developed to discover disease modules in biological networks. Compared with other highly cited module-finding tools, jActiveModules and Matisse, Walktrap-GM shows strong performance in the discovery of modules enriched with known cancer genes.
These results demonstrate that the Walktrap-GM algorithm identifies modules significantly enriched with cancer genes, their joint effects and promising candidate genes. The approach performs well when evaluated against similar tools and smaller overall module size allows for more specific functional annotation and facilitates the interpretation of these modules.</description><subject>Algorithms</subject><subject>Analysis</subject><subject>Cancer</subject><subject>Cell adhesion & migration</subject><subject>Cell cycle</subject><subject>Colorectal cancer</subject><subject>Computational biology</subject><subject>DNA binding proteins</subject><subject>Gene expression</subject><subject>Genetic aspects</subject><subject>Genetic transcription</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Hypotheses</subject><subject>Liver cancer</subject><subject>Methodology</subject><subject>Methods</subject><subject>Oncology, Experimental</subject><subject>Programming languages</subject><subject>Proteins</subject><subject>Studies</subject><issn>1756-0381</issn><issn>1756-0381</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNptkk1v1DAQhiMEoqVw5YgscSmHFI-_kr0gVRUfFZWQgJ4trz1eXBJ7sRNo_z2OKEsXVT54NH7mnfHobZrnQE8AevUaOqlayntoVQvdg-Zwl3h4Jz5onpRyRaliVPLHzQETwHqm4LD5eFlC3JBsoksj-WWG74VMiQSHcQr-hlgTLebWlJJsMBM6MiY3D1hIiASvtxlLCSkSZybztHnkzVDw2e191Fy-e_v17EN78en9-dnpRWulZFMr-Nqse-8ZSnSdByeRInjhVwIo-K43VDJYoeBWrbhnbu1R0Q7A9awD0_Oj5s0f3e28HtHZOmo2g97mMJp8o5MJev8lhm96k35qQUH2nFaB41uBnH7MWCY9hmJxGEzENBcNQgkJnAGv6Mv_0Ks051i_VynZqzoy7_5RGzOgDtGn2tcuovpUctGpFROL1sk9VD0Ox2BTRB9qfq_g1V5BZSa8njZmLkWff_l8r7jNqZSMfrcPoHqxil7coBc36CWuBS_ubnGH__UG_w3_2bcX</recordid><startdate>20131015</startdate><enddate>20131015</enddate><creator>Petrochilos, Deanna</creator><creator>Shojaie, Ali</creator><creator>Gennari, John</creator><creator>Abernethy, Neil</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>8AL</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>5PM</scope></search><sort><creationdate>20131015</creationdate><title>Using random walks to identify cancer-associated modules in expression data</title><author>Petrochilos, Deanna ; 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To better represent and study the intricate genetics of cancer onset and progression, we construct a network of biological interactions to search for groups of genes that compose cancer-related modules. Three cancer expression datasets are investigated to prioritize genes and interactions associated with cancer outcomes. Using a graph-based approach to search for communities of phenotype-related genes in microarray data, we find modules of genes associated with cancer phenotypes in a weighted interaction network.
We implement Walktrap, a random-walk-based community detection algorithm, to identify biological modules predisposing to tumor growth in 22 hepatocellular carcinoma samples (GSE14520), adenoma development in 32 colorectal cancer samples (GSE8671), and prognosis in 198 breast cancer patients (GSE7390). For each study, we find the best scoring partitions under a maximum cluster size of 200 nodes. Significant modules highlight groups of genes that are functionally related to cancer and show promise as therapeutic targets; these include interactions among transcription factors (SPIB, RPS6KA2 and RPS6KA6), cell-cycle regulatory genes (BRSK1, WEE1 and CDC25C), modulators of the cell-cycle and proliferation (CBLC and IRS2) and genes that regulate and participate in the map-kinase pathway (MAPK9, DUSP1, DUSP9, RIPK2). To assess the performance of Walktrap to find genomic modules (Walktrap-GM), we evaluate our results against other tools recently developed to discover disease modules in biological networks. Compared with other highly cited module-finding tools, jActiveModules and Matisse, Walktrap-GM shows strong performance in the discovery of modules enriched with known cancer genes.
These results demonstrate that the Walktrap-GM algorithm identifies modules significantly enriched with cancer genes, their joint effects and promising candidate genes. The approach performs well when evaluated against similar tools and smaller overall module size allows for more specific functional annotation and facilitates the interpretation of these modules.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>24128261</pmid><doi>10.1186/1756-0381-6-17</doi><tpages>1</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Analysis Cancer Cell adhesion & migration Cell cycle Colorectal cancer Computational biology DNA binding proteins Gene expression Genetic aspects Genetic transcription Genomes Genomics Hypotheses Liver cancer Methodology Methods Oncology, Experimental Programming languages Proteins Studies |
| title | Using random walks to identify cancer-associated modules in expression data |
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