Wei Tan1, Shiyi Liu1, Zhimin Deng1, Fangfang Dai1, Mengqin Yuan1, Wei Hu2, Bingshu Li3, Yanxiang Cheng4. 1. Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430060, China. 2. Department of Obstetrics and Gynecology Ultrasound, Renmin Hospital of Wuhan University, Wuhan, 430060, China. 3. Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430060, China. 416728484@qq.com. 4. Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430060, China. yanxiangCheng@whu.edu.cn.
Abstract
PURPOSE: The aim of the study was to construct a risk score model based on m6A-related targets to predict overall survival and immunotherapy response in ovarian cancer. METHODS: The gene expression profiles of 24 m6A regulators were extracted. Survival analysis screened 9 prognostic m6A regulators. Next, consensus clustering analysis was applied to identify clusters of ovarian cancer patients. Furthermore, 47 phenotype-related differentially expressed genes, strongly correlated with 9 prognostic m6A regulators, were screened and subjected to univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression. Ultimately, a nomogram was constructed which presented a strong ability to predict overall survival in ovarian cancer. RESULTS: CBLL1, FTO, HNRNPC, METTL3, METTL14, WTAP, ZC3H13, RBM15B and YTHDC2 were associated with worse overall survival (OS) in ovarian cancer. Three m6A clusters were identified, which were highly consistent with the three immune phenotypes. What is more, a risk model based on seven m6A-related targets was constructed with distinct prognosis. In addition, the low-risk group is the best candidate population for immunotherapy. CONCLUSION: We comprehensively analyzed the m6A modification landscape of ovarian cancer and detected seven m6A-related targets as an independent prognostic biomarker for predicting survival. Furthermore, we divided patients into high- and low-risk groups with distinct prognosis and select the optimum population which may benefit from immunotherapy and constructed a nomogram to precisely predict ovarian cancer patients' survival time and visualize the prediction results.
PURPOSE: The aim of the study was to construct a risk score model based on m6A-related targets to predict overall survival and immunotherapy response in ovarian cancer. METHODS: The gene expression profiles of 24 m6A regulators were extracted. Survival analysis screened 9 prognostic m6A regulators. Next, consensus clustering analysis was applied to identify clusters of ovarian cancer patients. Furthermore, 47 phenotype-related differentially expressed genes, strongly correlated with 9 prognostic m6A regulators, were screened and subjected to univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression. Ultimately, a nomogram was constructed which presented a strong ability to predict overall survival in ovarian cancer. RESULTS: CBLL1, FTO, HNRNPC, METTL3, METTL14, WTAP, ZC3H13, RBM15B and YTHDC2 were associated with worse overall survival (OS) in ovarian cancer. Three m6A clusters were identified, which were highly consistent with the three immune phenotypes. What is more, a risk model based on seven m6A-related targets was constructed with distinct prognosis. In addition, the low-risk group is the best candidate population for immunotherapy. CONCLUSION: We comprehensively analyzed the m6A modification landscape of ovarian cancer and detected seven m6A-related targets as an independent prognostic biomarker for predicting survival. Furthermore, we divided patients into high- and low-risk groups with distinct prognosis and select the optimum population which may benefit from immunotherapy and constructed a nomogram to precisely predict ovarian cancer patients' survival time and visualize the prediction results.
Authors: David A Barbie; Pablo Tamayo; Jesse S Boehm; So Young Kim; Susan E Moody; Ian F Dunn; Anna C Schinzel; Peter Sandy; Etienne Meylan; Claudia Scholl; Stefan Fröhling; Edmond M Chan; Martin L Sos; Kathrin Michel; Craig Mermel; Serena J Silver; Barbara A Weir; Jan H Reiling; Qing Sheng; Piyush B Gupta; Raymond C Wadlow; Hanh Le; Sebastian Hoersch; Ben S Wittner; Sridhar Ramaswamy; David M Livingston; David M Sabatini; Matthew Meyerson; Roman K Thomas; Eric S Lander; Jill P Mesirov; David E Root; D Gary Gilliland; Tyler Jacks; William C Hahn Journal: Nature Date: 2009-10-21 Impact factor: 49.962