Wei Hu
RMIT University
8 Papers
31 Citations
Wei Hu is an academic researcher from RMIT University. The author has contributed to research in topics: Carcinogenesis & Cancer. The author has an hindex of 8, co-authored 8 publications. Previous affiliations of Wei Hu include Wuhan 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.
Correction to: FOXC1-mediated LINC00301 facilitates tumor progression and triggers an immune-suppressing microenvironment in non-small cell lung cancer by regulating the HIF1α pathway
Cheng-Cao Sun,Cheng-Cao Sun,Wei Zhu,Shu-Jun Li,Wei Hu,Jian Zhang,Yue Zhuo,Han Zhang,Juan Wang,Yu Zhang,Shao-Xin Huang,Qi-Qiang He,De-Jia Li +12 more
TL;DR: Bioinformatics prediction and in vitro and in vivo experiments revealed that LINC00301 facilitates cell proliferation, releases NSCLC cell cycle arrest, promotes cell migration and invasion, and suppresses cell apoptosis inNSCLC.
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.
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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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