Tao Zhang

Tao Zhang


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 December 2016 in Pharmacological Reports
Verified
Journal Article
Closed access
YWYi Wang
Yi Wang
TZTao Zhang
Tao Zhang
BGBin Guo
Bin Guo
JHJianhua Huang
Jianhua Huang

1 July 2019 in Carbohydrate Polymers
Verified
Journal Article
Closed access
XLXinxin Liu
Xinxin Liu
FLFei Liu
Fei Liu
SZShuo Zhao
Shuo Zhao
BGBin Guo
Bin Guo
PLPeixue Ling
Peixue Ling
ZCZan Cui
Zan Cui

1 October 2015 in Carbohydrate Polymers
Verified
Journal Article
Closed access
QCQixin Chen
Qixin Chen
XMXifan Mei
Xifan Mei
TZTao Zhang
Tao Zhang
PLPeixue Ling
Peixue Ling
BGBin Guo
Bin Guo
FXFang Xu
Fang Xu

1 August 2017 in Carbohydrate Polymers
Verified
Journal Article
Closed access
QCQixin Chen
Qixin Chen
XSXintian Shao
Xintian Shao
PLPeixue Ling
Peixue Ling
FLFei Liu
Fei Liu
HSHuarong Shao
Huarong Shao
Fengshan WangFengshan Wang
Fengshan Wang

1 June 2019 in International Journal of Biological Macromolecules
Verified
Journal Article
Closed access
WZWei Zhang
Wei Zhang
JWJixu Wu
Jixu Wu
FZFangfang Zhang
Fangfang Zhang
XDXixi Dou
Xixi Dou
AMAibin Ma
Aibin Ma
TZTao Zhang
Tao Zhang

28 April 2020 in Medical Science Monitor
Verified
Journal Article
Closed access
JLJingyuan Li
Jingyuan Li
XLXingyuan Liu
Xingyuan Liu
ZCZan Cui
Zan Cui
TZTao Zhang
Tao Zhang
Background: We aimed to screen and identify central genetic and molecular targets involved in advancement of lung adenocarcinoma (LUAD) and to perform an integrated analysis and clinical validation. Material/Methods: The GEO2R technique was utilized to assess differentially expressed genes (DEGs) among the gene sets GSE75037, GSE85716, and GSE118370. Subsequently, gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytical methods were executed to determine related biofunctions and signaling pathways, which were annotated with tools from the Database for Annotation, Visualization and Integrated Discovery (DAVID) resource. Then, a protein-protein interaction (PPI) network complex consisting of all detected DEGs was built with the STRING web interface. Cytohubba and MCODE plug-ins for Cytoscape software and Gene Expression Profiling Interactive Analysis (GEPIA) were employed to identify the hub genes. Finally, the mRNA expression of the identified hub genes was quantitatively validated by The Cancer Genome Atlas (TCGA) database analysis and real-time quantitative polymerase chain reaction (RT-qPCR). Results: We screened 146 upregulated DEGs and 431 downregulated DEGs with the criteria of |logFC| >1 and P<0.05, and the GO analysis indicated that DEGs were implicated in mitotic nuclear division (biological process, BP), the nucleus (cellular component, CC), and protein binding (molecular function, MF) and were associated with multiple KEGG pathways, such as the p53 signaling pathway in cancer. Then, the top 8 genes that predicted significantly different outcomes in LUAD patients were filtered from the DEGs and selected as hub genes. The TCGA database analysis and RT-qPCR results demonstrated that these genes were differentially expressed with the same trends in LUAD tissues compared with normal tissues. Conclusions: Overall, we propose that 8 genes (PECAM1, CDK1, MKI67, SPP1, TOP2A, CHEK1, CCNB1, and RRM2) might be novel hub genes strongly associated with the progression and prognosis of LUAD.