Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data
This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all canc...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2014-01-01
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| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.4137/CIN.S13881 |
| _version_ | 1818300068385521664 |
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| author | Richard Neapolitan Xia Jiang |
| author_facet | Richard Neapolitan Xia Jiang |
| author_sort | Richard Neapolitan |
| collection | DOAJ |
| description | This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impact analysis (SPIA)) to the cancer genome atlas (TCGA) gene expression-level datasets. To test this hypothesis, we analyzed 20 cancer-related STPs and 6 randomly chosen STPs using the 591 cases in the TCGA ovarian carcinoma dataset, and the 102 controls in all 5 TCGA cancer datasets. We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway. The results of the two methods were highly correlated. Furthermore, many of the STPs that ranked highest according to both methods are plausibly linked to all cancers based on current knowledge. Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 ( P = 0.047), but SPIA did not ( P = 0.083). |
| first_indexed | 2024-12-13T05:01:14Z |
| format | Article |
| id | doaj.art-2bf6b99d87764becadcdb282d0ae0836 |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-13T05:01:14Z |
| publishDate | 2014-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-2bf6b99d87764becadcdb282d0ae08362022-12-21T23:58:47ZengSAGE PublishingCancer Informatics1176-93512014-01-0113s110.4137/CIN.S13881Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA DataRichard Neapolitan0Xia Jiang1Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impact analysis (SPIA)) to the cancer genome atlas (TCGA) gene expression-level datasets. To test this hypothesis, we analyzed 20 cancer-related STPs and 6 randomly chosen STPs using the 591 cases in the TCGA ovarian carcinoma dataset, and the 102 controls in all 5 TCGA cancer datasets. We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway. The results of the two methods were highly correlated. Furthermore, many of the STPs that ranked highest according to both methods are plausibly linked to all cancers based on current knowledge. Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 ( P = 0.047), but SPIA did not ( P = 0.083).https://doi.org/10.4137/CIN.S13881 |
| spellingShingle | Richard Neapolitan Xia Jiang Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data Cancer Informatics |
| title | Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data |
| title_full | Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data |
| title_fullStr | Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data |
| title_full_unstemmed | Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data |
| title_short | Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data |
| title_sort | inferring aberrant signal transduction pathways in ovarian cancer from tcga data |
| url | https://doi.org/10.4137/CIN.S13881 |
| work_keys_str_mv | AT richardneapolitan inferringaberrantsignaltransductionpathwaysinovariancancerfromtcgadata AT xiajiang inferringaberrantsignaltransductionpathwaysinovariancancerfromtcgadata |