Protein expression profiling identifies a prognostic model for ovarian cancer
Abstract Background Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress. Methods Using the data archived in the TCPA and TCGA databases, proteins having sign...
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BMC
2022-07-01
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| Series: | BMC Women's Health |
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| Online Access: | https://doi.org/10.1186/s12905-022-01876-x |
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| author | Luyang Xiong Jiahong Tan Yuchen Feng Daoqi Wang Xudong Liu Yun Feng Shusheng Li |
| author_facet | Luyang Xiong Jiahong Tan Yuchen Feng Daoqi Wang Xudong Liu Yun Feng Shusheng Li |
| author_sort | Luyang Xiong |
| collection | DOAJ |
| description | Abstract Background Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress. Methods Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis. Results 394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication. Conclusions The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management. |
| format | Article |
| id | doaj-art-359a8cfa08844d22a0944f0926f99a78 |
| institution | DOAJ |
| issn | 1472-6874 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Women's Health |
| spelling | doaj-art-359a8cfa08844d22a0944f0926f99a782025-08-20T03:06:50ZengBMCBMC Women's Health1472-68742022-07-0122111310.1186/s12905-022-01876-xProtein expression profiling identifies a prognostic model for ovarian cancerLuyang Xiong0Jiahong Tan1Yuchen Feng2Daoqi Wang3Xudong Liu4Yun Feng5Shusheng Li6Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Obstetrics and Gynecology, National Key Clinical Specialty of Gynecology, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and TechnologyDivision of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Urology, The Second Affiliated Hospital of Kunming Medical UniversityDepartment of Pancreatic Surgery, Wuhan Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologyDepartment of Obstetrics and Gynecology, National Key Clinical Specialty of Gynecology, The First People’s Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and TechnologyDepartment of Critical Care Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and TechnologyAbstract Background Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress. Methods Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis. Results 394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication. Conclusions The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.https://doi.org/10.1186/s12905-022-01876-xOvarian cancerPrognosisRisk modelSurvival |
| spellingShingle | Luyang Xiong Jiahong Tan Yuchen Feng Daoqi Wang Xudong Liu Yun Feng Shusheng Li Protein expression profiling identifies a prognostic model for ovarian cancer BMC Women's Health Ovarian cancer Prognosis Risk model Survival |
| title | Protein expression profiling identifies a prognostic model for ovarian cancer |
| title_full | Protein expression profiling identifies a prognostic model for ovarian cancer |
| title_fullStr | Protein expression profiling identifies a prognostic model for ovarian cancer |
| title_full_unstemmed | Protein expression profiling identifies a prognostic model for ovarian cancer |
| title_short | Protein expression profiling identifies a prognostic model for ovarian cancer |
| title_sort | protein expression profiling identifies a prognostic model for ovarian cancer |
| topic | Ovarian cancer Prognosis Risk model Survival |
| url | https://doi.org/10.1186/s12905-022-01876-x |
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