Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer
Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In t...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2020-12-01
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| Series: | Cancer Control |
| Online Access: | https://doi.org/10.1177/1073274820976671 |
| _version_ | 1797772283916320768 |
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| author | Ju-Yueh Li MD Chia-Jung Li PhD Li-Te Lin MD, PhD Kuan-Hao Tsui MD, PhD |
| author_facet | Ju-Yueh Li MD Chia-Jung Li PhD Li-Te Lin MD, PhD Kuan-Hao Tsui MD, PhD |
| author_sort | Ju-Yueh Li MD |
| collection | DOAJ |
| description | Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein–protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the “Human Protein Atlas” database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics. |
| first_indexed | 2024-03-12T21:49:45Z |
| format | Article |
| id | doaj.art-f9ed4c0f85c54f05af74baa20d17259d |
| institution | Directory Open Access Journal |
| issn | 1073-2748 |
| language | English |
| last_indexed | 2024-03-12T21:49:45Z |
| publishDate | 2020-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Control |
| spelling | doaj.art-f9ed4c0f85c54f05af74baa20d17259d2023-07-26T06:36:25ZengSAGE PublishingCancer Control1073-27482020-12-012710.1177/1073274820976671Multi-Omics Analysis Identifying Key Biomarkers in Ovarian CancerJu-Yueh Li MD0Chia-Jung Li PhD1Li-Te Lin MD, PhD2Kuan-Hao Tsui MD, PhD3 Department of Nursing, Shu-Zen Junior College of Medicine and Management, Kaohsiung Institute of BioPharmaceutical Sciences, National Sun Yat-sen University, Kaohsiung Department of Obstetrics and Gynaecology, National Yang-Ming University School of Medicine, Taipei Department of Pharmacy and Master Program, College of Pharmacy and Health Care, Tajen University, Pingtung CountyOvarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein–protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the “Human Protein Atlas” database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.https://doi.org/10.1177/1073274820976671 |
| spellingShingle | Ju-Yueh Li MD Chia-Jung Li PhD Li-Te Lin MD, PhD Kuan-Hao Tsui MD, PhD Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer Cancer Control |
| title | Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer |
| title_full | Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer |
| title_fullStr | Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer |
| title_full_unstemmed | Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer |
| title_short | Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer |
| title_sort | multi omics analysis identifying key biomarkers in ovarian cancer |
| url | https://doi.org/10.1177/1073274820976671 |
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