Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data
Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, ove...
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| Format: | Article |
| Language: | English |
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BioMed Central
2018-12-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gi-2018-16-4-e32.pdf |
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| author | Seokho Jeong Lydia Mok Se Ik Kim TaeJin Ahn Yong-Sang Song Taesung Park |
| author_facet | Seokho Jeong Lydia Mok Se Ik Kim TaeJin Ahn Yong-Sang Song Taesung Park |
| author_sort | Seokho Jeong |
| collection | DOAJ |
| description | Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient’s prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer. |
| format | Article |
| id | doaj-art-f028a3603888436cbc29f7ecefd90bec |
| institution | Kabale University |
| issn | 2234-0742 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | BioMed Central |
| record_format | Article |
| series | Genomics & Informatics |
| spelling | doaj-art-f028a3603888436cbc29f7ecefd90bec2025-02-02T22:25:26ZengBioMed CentralGenomics & Informatics2234-07422018-12-0116410.5808/GI.2018.16.4.e32534Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing DataSeokho Jeong0Lydia Mok1Se Ik Kim2TaeJin Ahn3Yong-Sang Song4Taesung Park5 Department of Statistics, Seoul National University, Seoul 08826, Korea Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Korea Department of Life Science, Handong Global University, Pohang 37554, Korea Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Korea Department of Statistics, Seoul National University, Seoul 08826, KoreaOvarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient’s prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.http://genominfo.org/upload/pdf/gi-2018-16-4-e32.pdfovarian neoplasmspenalized Cox regressionprediction modelRNA sequencing data |
| spellingShingle | Seokho Jeong Lydia Mok Se Ik Kim TaeJin Ahn Yong-Sang Song Taesung Park Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data Genomics & Informatics ovarian neoplasms penalized Cox regression prediction model RNA sequencing data |
| title | Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data |
| title_full | Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data |
| title_fullStr | Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data |
| title_full_unstemmed | Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data |
| title_short | Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data |
| title_sort | ovarian cancer prognostic prediction model using rna sequencing data |
| topic | ovarian neoplasms penalized Cox regression prediction model RNA sequencing data |
| url | http://genominfo.org/upload/pdf/gi-2018-16-4-e32.pdf |
| work_keys_str_mv | AT seokhojeong ovariancancerprognosticpredictionmodelusingrnasequencingdata AT lydiamok ovariancancerprognosticpredictionmodelusingrnasequencingdata AT seikkim ovariancancerprognosticpredictionmodelusingrnasequencingdata AT taejinahn ovariancancerprognosticpredictionmodelusingrnasequencingdata AT yongsangsong ovariancancerprognosticpredictionmodelusingrnasequencingdata AT taesungpark ovariancancerprognosticpredictionmodelusingrnasequencingdata |