Construction of a molecular prognostic risk score model of epithelial ovarian cancer based on GEO datebase
Journal Title: Journal of Air Force Medical University - Year 2023, Vol 44, Issue 9
Abstract
Objective To explore the prognostic characteristic genes of epithelial ovarian cancer and construct a molecular prognosis risk score model. Methods The GSE26712 dataset was downloaded from the GEO database and included expression data and corresponding clinical data from 185 patients with epithelial ovarian cancer and 10 normal control tissues. Epithelial ovarian cancer patients were divided into training group ( 129 cases) and validation group (56 cases) . Differentially expressed genes were screened in the GSE26712 dataset and prognosis-related genes were identified from the training group. LASSO and stepwise regression were performed on the common genes to screen out the best prognostic genes. Multivariate Cox proportional risk regression was used to determine the regression coefficients of prognostic characteristic genes, and the risk score model was constructed. Receiver operating characteristic curve (ROC), Kaplan-Meier (KM) survival analysis and 10-fold cross-validation were used to assess their predictive ability. Results Five genes including IGFBP4, IGF2, TLR2, DIAPH2 and AADAC were screened to be significantly related to the prognosis of ovarian cancer. According to the Cox coefficients of five genes, a risk score model was constructed to predict the prognosis. The KM survival curve showed that the prognosis of the high risk group was worse (P < 0. 000 1). ROC curve showed that the 1-, 3-, and 5-year areas under the curve were 0. 813, 0. 876, 0. 895, respectively. Conclusion The risk score model constructed in this study can better predict the prognosis of patients with epithelial ovarian cancer, providing a reliable prognostic assessment tool for clinicians and assisting clinical treatment decision-making.
Authors and Affiliations
WANG Xingguo, MA Gang, DONG Jian, XU Zhiyang, LIU Shujuan
Keywords
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