文献类型：期刊文献

英文题名：Novel Model for Comprehensive Assessment of Robust Prognostic Gene Signature in Ovarian Cancer Across Different Independent Datasets

作者：Bing, Zhitong[1,2,3];Yao, Yuxiang[4];Xiong, Jie[5];Tian, Jinhui[1,2];Guo, Xiangqian[6];Li, Xiuxia[1,2,7];Zhang, Jingyun[1,2];Shi, Xiue[8];Zhang, Yanying[9];Yang, Kehu[1,2,8,9]

第一作者：Bing, Zhitong

通信作者：Yang, KH[1];Yang, KH[2];Yang, KH[3];Zhang, YY[4];Yang, KH[4]

机构：[1]Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, Lanzhou, Gansu, Peoples R China;[2]Key Lab Evidence Based Med & Knowledge Translat G, Lanzhou, Gansu, Peoples R China;[3]Chinese Acad Sci, Inst Modern Phys, Dept Computat Phys, Lanzhou, Gansu, Peoples R China;[4]Lanzhou Univ, Sch Phys Sci & Technol, Lanzhou, Gansu, Peoples R China;[5]Changsha Univ, Dept Appl Math, Changsha, Hunan, Peoples R China;[6]Henan Univ, Sch Basic Med, Med Bioinformat Inst, Kaifeng, Henan, Peoples R China;[7]Lanzhou Univ, Sch Publ Hlth, Lanzhou, Gansu, Peoples R China;[8]Inst Evidence Based Rehabil Med Gansu Prov, Lanzhou, Gansu, Peoples R China;[9]Gansu Univ Chinese Med, Dept Pharmacol & Toxicol Tradit Chinese Med, Lanzhou, Gansu, Peoples R China

第一机构：Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, Lanzhou, Gansu, Peoples R China

通信机构：[1]corresponding author), Lanzhou Univ, Evidence Based Med Ctr, Sch Basic Med Sci, Lanzhou, Gansu, Peoples R China;[2]corresponding author), Key Lab Evidence Based Med & Knowledge Translat G, Lanzhou, Gansu, Peoples R China;[3]corresponding author), Inst Evidence Based Rehabil Med Gansu Prov, Lanzhou, Gansu, Peoples R China;[4]corresponding author), Gansu Univ Chinese Med, Dept Pharmacol & Toxicol Tradit Chinese Med, Lanzhou, Gansu, Peoples R China.|[10735]甘肃中医药大学;

年份：2019

卷号：10

外文期刊名：FRONTIERS IN GENETICS

收录：;Scopus(收录号：2-s2.0-85074161109);WOS:【SCI-EXPANDED(收录号:WOS:000497418300001)】；

语种：英文

外文关键词：ovarian cancer; prognosis index; Cox regression; gene signature; robust prognostic model

摘要：Different analytical methods or models can often find completely different prognostic biomarkers for the same cancer. In the study of prognostic molecular biomarkers of ovarian cancer (OvCa), different studies have reported a variety of prognostic gene signatures. In the current study, based on geometric concepts, the linearity-clustering phase diagram with integrated P-value (LCP) method was used to comprehensively consider three indicators that are commonly employed to estimate the quality of a prognostic gene signature model. The three indicators, namely, concordance index, area under the curve, and level of the hazard ratio were determined via calculation of the prognostic index of various gene signatures from different datasets. As evaluation objects, we selected 13 gene signature models (Cox regression model) and 16 OvCa genomic datasets (including gene expression information and follow-up data) from published studies. The results of LCP showed that three models were universal and better than other models. In addition, combining the three models into one model showed the best performance in all datasets by LCP calculation. The combination gene signature model provides a more reliable model and could be validated in various datasets of OvCa. Thus, our method and findings can provide more accurate prognostic biomarkers and effective reference for the precise clinical treatment of OvCa.