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 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.

Integrative Data Mining Pipeline for Identification of a Protein- Based Prognostic Signature in Lung Squamous Cell Carcinoma

“…We calculated a risk score for each patient based on this model and evaluated the performance of the prognostic to Kaplan-Meier survival analysis and time-dependent ROC curve analysis, which demonstrated signi cant prognostic performance in training, and validation as well as on the entire dataset. Recently, various proteins-based prognostic models in malignancies have been successfully validated protein prognostic model in malignancies, including breast cancer [14], ovarian cancer [15], stomach adenocarcinoma [16], bladder urothelial carcinoma [17], head and neck squamous cell carcinoma [18]. The four-protein prognostic signature developed in our study comprised three proteins (Histone-H3, HSP27_pS82, and CHK2) that were protective and one protein (PAXILLIN) that was related to bleak prognosis, which are not captured by genomics or transcriptomics.…”