Identification of a glycolysis‐related gene signature for survival prediction of ovarian cancer patients
Abstract Background Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV. Methods The expression profiles of glycolysis‐related genes (GRGs) and...
Guardado en:
Autores principales: | Dai Zhang, Yiche Li, Si Yang, Meng Wang, Jia Yao, Yi Zheng, Yujiao Deng, Na Li, Bajin Wei, Ying Wu, Zhen Zhai, Zhijun Dai, Huafeng Kang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Wiley
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f21e41a493894f62885d5ec701973153 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Establishment of A Nomogram for Predicting the Prognosis of Soft Tissue Sarcoma Based on Seven Glycolysis-Related Gene Risk Score
por: Yuhang Liu, et al.
Publicado: (2021) -
Extracellular Vesicle Molecular Signatures Characterize Metastatic Dynamicity in Ovarian Cancer
por: Amber Gonda, et al.
Publicado: (2021) -
An Aging-Related Gene Signature-Based Model for Risk Stratification and Prognosis Prediction in Breast Cancer
por: Yuan J, et al.
Publicado: (2021) -
Comprehensive Analysis of the Tumor Microenvironment and Ferroptosis-Related Genes Predict Prognosis with Ovarian Cancer
por: Xiao-xue Li, et al.
Publicado: (2021) -
Identification of Methylation Immune Subtypes and Establishment of a Prognostic Signature for Gliomas Using Immune-Related Genes
por: Zhengang Hu, et al.
Publicado: (2021)