Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers
Abstract Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome A...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
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| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-025-01819-6 |
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| _version_ | 1823861939678216192 |
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| author | Aidi Lin Feifei Xue Chenxiang Pan Lijiao Li |
| author_facet | Aidi Lin Feifei Xue Chenxiang Pan Lijiao Li |
| author_sort | Aidi Lin |
| collection | DOAJ |
| description | Abstract Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome Atlas (TCGA), we identified significant prognostic genes through differential expression and survival analysis, integrating these with clinical features and immune landscape assessments including immune cell infiltration and checkpoint expression. The risk score effectively predicted patient survival, distinguishing between high and low-risk groups with significant outcome differences. High-risk patients demonstrated poor prognosis, greater immune checkpoint expression, and higher tumor mutational burdens (TMB), suggesting potential responsiveness to immunotherapy. The model's predictive capacity was validated across multiple cohorts, showing consistent performance in survival prediction and treatment response. Calibration curves and decision curve analysis confirmed the model's clinical utility. This study highlights the potential of an integrated approach to enhance personalized treatment strategies in ovarian cancer, aiming to improve patient management and outcomes. |
| format | Article |
| id | doaj-art-0c31b01b55984a4487a99ab44534635e |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-0c31b01b55984a4487a99ab44534635e2025-02-09T12:43:25ZengSpringerDiscover Oncology2730-60112025-02-0116111610.1007/s12672-025-01819-6Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markersAidi Lin0Feifei Xue1Chenxiang Pan2Lijiao Li3Wenzhou Central HospitalWenzhou Central HospitalWenzhou Central HospitalWenzhou Central HospitalAbstract Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome Atlas (TCGA), we identified significant prognostic genes through differential expression and survival analysis, integrating these with clinical features and immune landscape assessments including immune cell infiltration and checkpoint expression. The risk score effectively predicted patient survival, distinguishing between high and low-risk groups with significant outcome differences. High-risk patients demonstrated poor prognosis, greater immune checkpoint expression, and higher tumor mutational burdens (TMB), suggesting potential responsiveness to immunotherapy. The model's predictive capacity was validated across multiple cohorts, showing consistent performance in survival prediction and treatment response. Calibration curves and decision curve analysis confirmed the model's clinical utility. This study highlights the potential of an integrated approach to enhance personalized treatment strategies in ovarian cancer, aiming to improve patient management and outcomes.https://doi.org/10.1007/s12672-025-01819-6Ovarian cancerPrognostic modelImmune infiltrationTumor mutational burdenPersonalized medicine |
| spellingShingle | Aidi Lin Feifei Xue Chenxiang Pan Lijiao Li Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers Discover Oncology Ovarian cancer Prognostic model Immune infiltration Tumor mutational burden Personalized medicine |
| title | Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers |
| title_full | Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers |
| title_fullStr | Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers |
| title_full_unstemmed | Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers |
| title_short | Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers |
| title_sort | integrative prognostic modeling of ovarian cancer incorporating genetic clinical and immunological markers |
| topic | Ovarian cancer Prognostic model Immune infiltration Tumor mutational burden Personalized medicine |
| url | https://doi.org/10.1007/s12672-025-01819-6 |
| work_keys_str_mv | AT aidilin integrativeprognosticmodelingofovariancancerincorporatinggeneticclinicalandimmunologicalmarkers AT feifeixue integrativeprognosticmodelingofovariancancerincorporatinggeneticclinicalandimmunologicalmarkers AT chenxiangpan integrativeprognosticmodelingofovariancancerincorporatinggeneticclinicalandimmunologicalmarkers AT lijiaoli integrativeprognosticmodelingofovariancancerincorporatinggeneticclinicalandimmunologicalmarkers |