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Yufeng Liu
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Journal Articles

Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy

Feng Xie, Jianjun Zhang, Jiayin Wang, Alexandre Reuben, Wei Xu, Xin Yi, Frederick S. Varn, Yongsheng Ye, Junwen Cheng, Miao Yu, Yue Wang, Yufeng Liu, Mingchao Xie, Peng Du, Ke Ma, Xin Ma, Penghui Zhou, Shengli Yang, Yaobing Chen, Guoping Wang, Xuefeng Xia, Zhongxing Liao, John V. Heymach, Ignacio I. Wistuba, P. Andrew Futreal, Kai Ye, Chao Cheng, Tian Xia
Journal: Clinical Cancer Research
Clin Cancer Res (2020) 26 (12): 2908–2920.
https://doi.org/10.1158/1078-0432.CCR-19-1744
Published: 15 June 2020
View article titled, Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy
Open the PDF for Multifactorial Deep Learning Reveals Pan-Cancer Genomic Tumor Clusters with Distinct Immunogenomic Landscape and Response to Immunotherapy in another window
Includes: Supplementary data
Journal Articles

A Novel Oxoglutarate Dehydrogenase-Like Mediated miR-214/TWIST1 Negative Feedback Loop Inhibits Pancreatic Cancer Growth and Metastasis

Yao Liu, Fanzheng Meng, Jiabei Wang, Mingyang Liu, Guangchao Yang, Ruipeng Song, Tongsen Zheng, Yingjian Liang, Shugeng Zhang, Dalong Yin, Jizhou Wang, Haiyan Yang, Shangha Pan, Bo Sun, Jihua Han, Jing Sun, Yaliang Lan, Yan Wang, Xirui Liu, Mingxi Zhu, Yifeng Cui, Bo Zhang, Dehai Wu, Shuhang Liang, Yufeng Liu, Xuan Song, Zhaoyang Lu, Jingxuan Yang, Min Li, Lianxin Liu
Journal: Clinical Cancer Research
Series: Author Choice
Clin Cancer Res (2019) 25 (17): 5407–5421.
https://doi.org/10.1158/1078-0432.CCR-18-4113
Published: 03 September 2019
View article titled, A Novel Oxoglutarate Dehydrogenase-Like Mediated miR-214/TWIST1 Negative Feedback Loop Inhibits Pancreatic Cancer Growth and Metastasis
Open the PDF for A Novel Oxoglutarate Dehydrogenase-Like Mediated miR-214/TWIST1 Negative Feedback Loop Inhibits Pancreatic Cancer Growth and Metastasis in another window
Includes: Supplementary data
Journal Articles

High XRCC1 Protein Expression Is Associated with Poorer Survival in Patients with Head and Neck Squamous Cell Carcinoma

Mei-Kim Ang, Mihir R. Patel, Xiao-Ying Yin, Sneha Sundaram, Karen Fritchie, Ni Zhao, Yufeng Liu, Alex J. Freemerman, Matthew D. Wilkerson, Vonn Walter, Mark C. Weissler, William W. Shockley, Marion E. Couch, Adam M. Zanation, Trevor Hackman, Bhishamjit S. Chera, Stephen L. Harris, C. Ryan Miller, Leigh B. Thorne, Michele C. Hayward, William K. Funkhouser, Andrew F. Olshan, Carol G. Shores, Liza Makowski, D. Neil Hayes
Journal: Clinical Cancer Research
Clin Cancer Res (2011) 17 (20): 6542–6552.
https://doi.org/10.1158/1078-0432.CCR-10-1604
Published: 14 October 2011
View article titled, High XRCC1 Protein Expression Is Associated with Poorer Survival in Patients with Head and Neck Squamous Cell Carcinoma
Open the PDF for High XRCC1 Protein Expression Is Associated with Poorer Survival in Patients with Head and Neck Squamous Cell Carcinoma in another window
Includes: Supplementary data
Journal Articles

Lung Squamous Cell Carcinoma mRNA Expression Subtypes Are Reproducible, Clinically Important, and Correspond to Normal Cell Types

Matthew D. Wilkerson, Xiaoying Yin, Katherine A. Hoadley, Yufeng Liu, Michele C. Hayward, Christopher R. Cabanski, Kenneth Muldrew, C. Ryan Miller, Scott H. Randell, Mark A. Socinski, Alden M. Parsons, William K. Funkhouser, Carrie B. Lee, Patrick J. Roberts, Leigh Thorne, Philip S. Bernard, Charles M. Perou, D. Neil Hayes
Journal: Clinical Cancer Research
Clin Cancer Res (2010) 16 (19): 4864–4875.
https://doi.org/10.1158/1078-0432.CCR-10-0199
Published: 29 September 2010
View article titled, Lung Squamous Cell Carcinoma mRNA Expression Subtypes Are Reproducible, Clinically Important, and Correspond to Normal Cell Types
Open the PDF for Lung Squamous Cell Carcinoma mRNA Expression Subtypes Are Reproducible, Clinically Important, and Correspond to Normal Cell Types in another window
Includes: Supplementary data