Cong Liu
Wuhan University
9 Papers
31 Citations
Cong Liu is an academic researcher from Wuhan University. The author has contributed to research in topics: Cancer & microRNA. The author has an hindex of 7, co-authored 9 publications. Previous affiliations of Cong Liu include RMIT University.
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Papers
Comprehensive landscape of extracellular vesicle-derived RNAs in cancer initiation, progression, metastasis and cancer immunology
Wei Hu,Cong Liu,Zhuoyue Bi,Qun Zhou,Han Zhang,Lin-Lin Li,Jian Zhang,Wei Zhu,Yang-Yi-Yan Song,Feng Zhang,Hui-Min Yang,Yongyi Bi,Qi-qiang He,Gong-Jun Tan,Gong-Jun Tan,Cheng-Cao Sun,Cheng-Cao Sun,De-Jia Li,De-Jia Li +18 more
TL;DR: Current findings regarding EV biogenesis, release and interaction with target cells as well as EV-RNA sorting are discussed, and biological roles and molecular mechanisms of EV-ncRNAs in cancer biology are highlighted.
YTHDF1 promotes mRNA degradation via YTHDF1-AGO2 interaction and phase separation.
Jiong Li,Ke Chen,Xin Dong,Ya-Ting Xu,Qi Sun,Honghong Wang,Zhen Chen,Cong Liu,Rong Liu,Zhe Yang,Xiangfei Mei,Rongyu Zhang,Liuping Chang,Zongwen Tian,Jianjun Chen,Kaiwei Liang,Chunjiang He,Mengcheng Luo +17 more
TL;DR: Zhang et al. as mentioned in this paper found that deletion of YTHDF1 led to massive RNA patches deposited in the cytoplasm, which promoted P-body formation through liquid-liquid phase separation.
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Roles of miR-200 family members in lung cancer: more than tumor suppressors.
Cong Liu,Wei Hu,Lin-Lin Li,Yu-Xuan Wang,Qun Zhou,Feng Zhang,Yiyan Songyang,Wei Zhu,Cheng-Chao Sun,De-Jia Li +9 more
TL;DR: Several miRNAs were suggested to form the network regulating EMT in lung cancer, among which, miR-200 family members play crucial roles in the suppression of EMT.
56
Long non coding RNA XIST as a prognostic cancer marker - A meta-analysis.
Qun Zhou,Wei Hu,Wei Zhu,Feng Zhang,Li Lin-lin,Cong Liu,Yiyan Songyang,Cheng-Cao Sun,Dejia Li +8 more
TL;DR: LncRNA XIST may serve as a potential biomarker to predict solid tumor prognosis and clinicopathology and can be effectively used to predict the clinical and pathological features of cancers.
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Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma.
TL;DR: The comprehensive analysis demonstrated that prognostic signatures and the prognostic model by the large-scale gene expression analysis were more robust than models built by single data based gene signatures in LUAD overall survival prediction.
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