Core module biomarker identification with network exploration for breast cancer metastasis
<p>Abstract</p> <p>Background</p> <p>In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2012-01-01
|
| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/13/12 |
| _version_ | 1818646206134353920 |
|---|---|
| author | Yang Ruoting Daigle Bernie J Petzold Linda R Doyle Francis J |
| author_facet | Yang Ruoting Daigle Bernie J Petzold Linda R Doyle Francis J |
| author_sort | Yang Ruoting |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".</p> <p>Results</p> <p>We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.</p> <p>Conclusions</p> <p>COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.</p> |
| first_indexed | 2024-12-17T00:42:57Z |
| format | Article |
| id | doaj.art-712bc04edb8b4815b87820b2e3733d66 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-17T00:42:57Z |
| publishDate | 2012-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-712bc04edb8b4815b87820b2e3733d662022-12-21T22:09:59ZengBMCBMC Bioinformatics1471-21052012-01-011311210.1186/1471-2105-13-12Core module biomarker identification with network exploration for breast cancer metastasisYang RuotingDaigle Bernie JPetzold Linda RDoyle Francis J<p>Abstract</p> <p>Background</p> <p>In a complex disease, the expression of many genes can be significantly altered, leading to the appearance of a differentially expressed "disease module". Some of these genes directly correspond to the disease phenotype, (i.e. "driver" genes), while others represent closely-related first-degree neighbours in gene interaction space. The remaining genes consist of further removed "passenger" genes, which are often not directly related to the original cause of the disease. For prognostic and diagnostic purposes, it is crucial to be able to separate the group of "driver" genes and their first-degree neighbours, (i.e. "core module") from the general "disease module".</p> <p>Results</p> <p>We have developed COMBINER: COre Module Biomarker Identification with Network ExploRation. COMBINER is a novel pathway-based approach for selecting highly reproducible discriminative biomarkers. We applied COMBINER to three benchmark breast cancer datasets for identifying prognostic biomarkers. COMBINER-derived biomarkers exhibited 10-fold higher reproducibility than other methods, with up to 30-fold greater enrichment for known cancer-related genes, and 4-fold enrichment for known breast cancer susceptible genes. More than 50% and 40% of the resulting biomarkers were cancer and breast cancer specific, respectively. The identified modules were overlaid onto a map of intracellular pathways that comprehensively highlighted the hallmarks of cancer. Furthermore, we constructed a global regulatory network intertwining several functional clusters and uncovered 13 confident "driver" genes of breast cancer metastasis.</p> <p>Conclusions</p> <p>COMBINER can efficiently and robustly identify disease core module genes and construct their associated regulatory network. In the same way, it is potentially applicable in the characterization of any disease that can be probed with microarrays.</p>http://www.biomedcentral.com/1471-2105/13/12 |
| spellingShingle | Yang Ruoting Daigle Bernie J Petzold Linda R Doyle Francis J Core module biomarker identification with network exploration for breast cancer metastasis BMC Bioinformatics |
| title | Core module biomarker identification with network exploration for breast cancer metastasis |
| title_full | Core module biomarker identification with network exploration for breast cancer metastasis |
| title_fullStr | Core module biomarker identification with network exploration for breast cancer metastasis |
| title_full_unstemmed | Core module biomarker identification with network exploration for breast cancer metastasis |
| title_short | Core module biomarker identification with network exploration for breast cancer metastasis |
| title_sort | core module biomarker identification with network exploration for breast cancer metastasis |
| url | http://www.biomedcentral.com/1471-2105/13/12 |
| work_keys_str_mv | AT yangruoting coremodulebiomarkeridentificationwithnetworkexplorationforbreastcancermetastasis AT daigleberniej coremodulebiomarkeridentificationwithnetworkexplorationforbreastcancermetastasis AT petzoldlindar coremodulebiomarkeridentificationwithnetworkexplorationforbreastcancermetastasis AT doylefrancisj coremodulebiomarkeridentificationwithnetworkexplorationforbreastcancermetastasis |