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Your search keyword '"Guoyan Tian"' showing total 2 results
Start Over Author "Guoyan Tian" Publisher wiley
2 results on '"Guoyan Tian"'

1. Myelofibrosis predicts deep molecular response 4.5 in chronic myeloid leukaemia patients initially treated with imatinib: An extensive, multicenter and retrospective study to develop a prognostic model

Author

Tian Zeng, Xiudi Yang, Yi Wang, Dijiong Wu, Weiying Feng, Ying Lu, Xiaoqiong Zhu, Lirong Liu, Mei Zhou, Li Zhang, Yanping Shao, Honglan Qian, Feng Zhu, Yu Chen, Dan Cao, Li Huang, Xiaoning Feng, Lili Chen, Gang Zhang, Jing Le, Weiguo Zhu, Yongming Xia, Yanxia Han, Yongqing Jia, Guoyan Tian, Hui Zhou, Linjuan Xu, Xiufeng Yin, Qinli Tang, Yuefeng Zhang, Guoli Yao, Xianghua Lang, Kaifeng Zhang, Xiujie Zhou, Junbin Guo, Jinming Tu, Jianzhi Zhao, Gongqiang Wu, Huiqi Zhang, Xiao Wu, Qiulian Luo, Lihong Cao, Binbin Chu, Wei Jiang, Haiying Wu, Liansheng Huang, Meiwei Hu, Muqing He, Jingjing Zhu, Hongyan Tong, Jie Jin, and Jian Huang

Subjects
Medicine (General), R5-920
Published
2024
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2. Development and validation of a 10‐gene prognostic signature for acute myeloid leukaemia

Author

Tingting Tang, Guoyan Tian, Xiaohui Chen, Zuyi Yang, Ning Li, Liang Zhang, and Jun Shang

Subjects
Male, 0301 basic medicine, Oncology, Multivariate statistics, medicine.medical_specialty, Poor prognosis, Kaplan-Meier Estimate, Disease-Free Survival, nomogram, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Biomarkers, Tumor, gene expression profiling, medicine, Humans, acute myeloid leukaemia, Proportional Hazards Models, Framingham Risk Score, Prognostic signature, Proportional hazards model, business.industry, Univariate, Original Articles, Cell Biology, Middle Aged, Nomogram, Prognosis, Neoplasm Proteins, Gene Expression Regulation, Neoplastic, Leukemia, Myeloid, Acute, Nomograms, 030104 developmental biology, 030220 oncology & carcinogenesis, Molecular Medicine, Female, Original Article, Myeloid leukaemia, Transcriptome, business, signature
Abstract

Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta‐training, meta‐testing and validation sets. The meta‐training set was used to build risk prediction model, and the other four data sets were employed for validation. By log‐rank test and univariate COX regression analysis as well as LASSO‐COX, AML patients were divided into high‐risk and low‐risk groups based on AML risk score (AMLRS) which was constituted by 10 survival‐related genes. In meta‐training, meta‐testing and validation sets, the patient in the low‐risk group all had a significantly longer OS (overall survival) than those in the high‐risk group (P

Published
2020
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