Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes
Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/24/7/6445 |
| _version_ | 1797607807320588288 |
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| author | Suyeon Lee Heewon Jung Jiwoo Park Jaegyoon Ahn |
| author_facet | Suyeon Lee Heewon Jung Jiwoo Park Jaegyoon Ahn |
| author_sort | Suyeon Lee |
| collection | DOAJ |
| description | Accurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify patient-specific cancer driver genes, we first generated patient-specific gene networks before using modified PageRank to generate feature vectors that represented the impacts genes had on the patient-specific gene network. Subsequently, the feature vectors of the good and poor prognosis groups were used to train the deep feedforward network. For the 11 cancer types in the TCGA data, the proposed method showed a significantly better prediction performance than the existing state-of-the-art methods for three cancer types (BRCA, CESC and PAAD), better performance for five cancer types (COAD, ESCA, HNSC, KIRC and STAD), and a similar or slightly worse performance for the remaining three cancer types (BLCA, LIHC and LUAD). Furthermore, the case study for the identified breast cancer and cervical squamous cell carcinoma prognostic genes and their subnetworks included several pathways associated with the progression of breast cancer and cervical squamous cell carcinoma. These results suggested that heterogeneous cancer driver information may be associated with cancer prognosis. |
| first_indexed | 2024-03-11T05:34:52Z |
| format | Article |
| id | doaj.art-67b14d87851048bd9b0bb0df83cd98d4 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-11T05:34:52Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-67b14d87851048bd9b0bb0df83cd98d42023-11-17T16:51:13ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672023-03-01247644510.3390/ijms24076445Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver GenesSuyeon Lee0Heewon Jung1Jiwoo Park2Jaegyoon Ahn3Department of Computer Science and Engineering, Incheon National University, Incheon 22012, Republic of KoreaSamsung Electronics Company Ltd., Suwon 16677, Republic of KoreaDepartment of Computer Science and Engineering, Incheon National University, Incheon 22012, Republic of KoreaDepartment of Computer Science and Engineering, Incheon National University, Incheon 22012, Republic of KoreaAccurate prediction of the prognoses of cancer patients and identification of prognostic biomarkers are both important for the improved treatment of cancer patients, in addition to enhanced anticancer drugs. Many previous bioinformatic studies have been carried out to achieve this goal; however, there remains room for improvement in terms of accuracy. In this study, we demonstrated that patient-specific cancer driver genes could be used to predict cancer prognoses more accurately. To identify patient-specific cancer driver genes, we first generated patient-specific gene networks before using modified PageRank to generate feature vectors that represented the impacts genes had on the patient-specific gene network. Subsequently, the feature vectors of the good and poor prognosis groups were used to train the deep feedforward network. For the 11 cancer types in the TCGA data, the proposed method showed a significantly better prediction performance than the existing state-of-the-art methods for three cancer types (BRCA, CESC and PAAD), better performance for five cancer types (COAD, ESCA, HNSC, KIRC and STAD), and a similar or slightly worse performance for the remaining three cancer types (BLCA, LIHC and LUAD). Furthermore, the case study for the identified breast cancer and cervical squamous cell carcinoma prognostic genes and their subnetworks included several pathways associated with the progression of breast cancer and cervical squamous cell carcinoma. These results suggested that heterogeneous cancer driver information may be associated with cancer prognosis.https://www.mdpi.com/1422-0067/24/7/6445cancer prognosiscancer driver genemachine learning |
| spellingShingle | Suyeon Lee Heewon Jung Jiwoo Park Jaegyoon Ahn Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes International Journal of Molecular Sciences cancer prognosis cancer driver gene machine learning |
| title | Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes |
| title_full | Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes |
| title_fullStr | Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes |
| title_full_unstemmed | Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes |
| title_short | Accurate Prediction of Cancer Prognosis by Exploiting Patient-Specific Cancer Driver Genes |
| title_sort | accurate prediction of cancer prognosis by exploiting patient specific cancer driver genes |
| topic | cancer prognosis cancer driver gene machine learning |
| url | https://www.mdpi.com/1422-0067/24/7/6445 |
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