Xiaoyu Ji1, Guangdi Chu2, Yulong Chen1, Jinwen Jiao1, Teng Lv1, Qin Yao3. 1. Department of Obstetrics and Gynecology, Affiliated Hospital of Qingdao University, No. 1677 Wutaishan Road, Qingdao, 266000, China. 2. Department of Urology, Affiliated Hospital of Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, China. 3. Department of Obstetrics and Gynecology, Affiliated Hospital of Qingdao University, No. 1677 Wutaishan Road, Qingdao, 266000, China. dr_yaoqin@126.com.
Abstract
OBJECTIVE: Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict the survival of patients with common CC. METHODS AND RESULTS: The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas and The Cancer Genome Atlas database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan-Meier analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the area under curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins. CONCLUSION: A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.
OBJECTIVE: Cervical cancer (CC) is one of the most common types of malignant female cancer, and its incidence and mortality are not optimistic. Protein panels can be a powerful prognostic factor for many types of cancer. The purpose of our study was to investigate a proteomic panel to predict the survival of patients with common CC. METHODS AND RESULTS: The protein expression and clinicopathological data of CC were downloaded from The Cancer Proteome Atlas and The Cancer Genome Atlas database, respectively. We selected the prognosis-related proteins (PRPs) by univariate Cox regression analysis and found that the results of functional enrichment analysis were mainly related to apoptosis. We used Kaplan-Meier analysis and multivariable Cox regression analysis further to screen PRPs to establish a prognostic model, including BCL2, SMAD3, and 4EBP1-pT70. The signature was verified to be independent predictors of OS by Cox regression analysis and the area under curves. Nomogram and subgroup classification were established based on the signature to verify its clinical application. Furthermore, we looked for the co-expressed proteins of three-protein panel as potential prognostic proteins. CONCLUSION: A proteomic signature independently predicted OS of CC patients, and the predictive ability was better than the clinicopathological characteristics. This signature can help improve prediction for clinical outcome and provides new targets for CC treatment.
Authors: Xiaoxia Hu; Julie K Schwarz; James S Lewis; Phyllis C Huettner; Janet S Rader; Joseph O Deasy; Perry W Grigsby; Xiaowei Wang Journal: Cancer Res Date: 2010-02-02 Impact factor: 12.701