An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules
Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-04-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00366/full |
| _version_ | 1819111115796250624 |
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| author | Yi-Cheng Gao Xiong-Hui Zhou Wen Zhang |
| author_facet | Yi-Cheng Gao Xiong-Hui Zhou Wen Zhang |
| author_sort | Yi-Cheng Gao |
| collection | DOAJ |
| description | Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on the hypothesis that the gene co-expression network may reveal relationships among genes, some communities in the network could influence the prognosis of cancer patients and all the prognosis-related communities could fully reveal the prognosis of cancer patients. To predict the prognosis for cancer patients in each subtype, we adopted an ensemble classifier based on the gene co-expression network of the corresponding subtype. Using the gene expression data of ovarian cancer patients in TCGA (The Cancer Genome Atlas), three subtypes were identified. Survival analysis showed that patients in different subtypes had different survival risks. Three ensemble classifiers were constructed for each subtype. Leave-one-out and independent validation showed that our method outperformed control and literature methods. Furthermore, the function annotation of the communities in each subtype showed that some communities were cancer-related. Finally, we found that the current drug targets can partially support our method. |
| first_indexed | 2024-12-22T03:52:29Z |
| format | Article |
| id | doaj.art-b6cc40fc502e4b81830bf88e6f903f88 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-22T03:52:29Z |
| publishDate | 2019-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-b6cc40fc502e4b81830bf88e6f903f882022-12-21T18:39:58ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-04-011010.3389/fgene.2019.00366447699An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene ModulesYi-Cheng GaoXiong-Hui ZhouWen ZhangDue to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on the hypothesis that the gene co-expression network may reveal relationships among genes, some communities in the network could influence the prognosis of cancer patients and all the prognosis-related communities could fully reveal the prognosis of cancer patients. To predict the prognosis for cancer patients in each subtype, we adopted an ensemble classifier based on the gene co-expression network of the corresponding subtype. Using the gene expression data of ovarian cancer patients in TCGA (The Cancer Genome Atlas), three subtypes were identified. Survival analysis showed that patients in different subtypes had different survival risks. Three ensemble classifiers were constructed for each subtype. Leave-one-out and independent validation showed that our method outperformed control and literature methods. Furthermore, the function annotation of the communities in each subtype showed that some communities were cancer-related. Finally, we found that the current drug targets can partially support our method.https://www.frontiersin.org/article/10.3389/fgene.2019.00366/fullprognosis geneovarian cancersubtypegene co-expression networkensemble classifier |
| spellingShingle | Yi-Cheng Gao Xiong-Hui Zhou Wen Zhang An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules Frontiers in Genetics prognosis gene ovarian cancer subtype gene co-expression network ensemble classifier |
| title | An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules |
| title_full | An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules |
| title_fullStr | An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules |
| title_full_unstemmed | An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules |
| title_short | An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules |
| title_sort | ensemble strategy to predict prognosis in ovarian cancer based on gene modules |
| topic | prognosis gene ovarian cancer subtype gene co-expression network ensemble classifier |
| url | https://www.frontiersin.org/article/10.3389/fgene.2019.00366/full |
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