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Protein expression profiling identifies a prognostic model for ovarian cancer

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机构: [1]Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College,Huazhong University of Science and Technology, Wuhan, China [2]Departmentof Obstetrics and Gynecology, National Key Clinical Specialty of Gynecology,The First People’s Hospital of Yunnan Province, The Affiliated Hospital of KunmingUniversity of Science and Technology, Kunming, China [3]Division of Pulmonaryand Critical Care Medicine, Department of Internal Medicine, TongjiHospital, Tongji Medical College, Huazhong University of Science and Technology,Wuhan, China [4]Department of Urology, The Second Affiliated Hospitalof Kunming Medical University, Kunming, China [5]Department of PancreaticSurgery, Wuhan Union Hospital, Tongji Medical College, Huazhong Universityof Science and Technology, Wuhan, China
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关键词: Ovarian cancer Prognosis Risk model Survival

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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 (GSK3 alpha/beta, 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 GSK3 alpha/beta, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.

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出版当年[2022]版:
大类 | 3 区 医学
小类 | 3 区 妇产科学 3 区 公共卫生、环境卫生与职业卫生
最新[2023]版:
大类 | 3 区 医学
小类 | 2 区 公共卫生、环境卫生与职业卫生 3 区 妇产科学
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出版当年[2021]版:
Q3 OBSTETRICS & GYNECOLOGY Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
最新[2023]版:
Q2 OBSTETRICS & GYNECOLOGY Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH

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第一作者机构: [1]Department of Critical Care Medicine, Tongji Hospital, Tongji Medical College,Huazhong University of Science and Technology, Wuhan, China
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