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Chenyue Zhang
Researcher at Fudan University
Publications - 62
Citations - 903
Chenyue Zhang is an academic researcher from Fudan University. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 13, co-authored 41 publications receiving 541 citations.
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Journal ArticleDOI
Bufalin suppresses hepatocellular carcinoma invasion and metastasis by targeting HIF-1α via the PI3K/AKT/mTOR pathway
Haiyong Wang,Chenyue Zhang,Litao Xu,Kun Zang,Zhouyu Ning,Feng Jiang,Huiying Chi,Xiaoyan Zhu,Zhiqiang Meng +8 more
TL;DR: The results suggest that bufalin suppresses hepatic tumor invasion and metastasis and that this process may be related to the PI3K/AKT/mTOR/ HIF-1α axis.
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Differences in Stage of Cancer at Diagnosis, Treatment, and Survival by Race and Ethnicity Among Leading Cancer Types.
TL;DR: Overall, compared with Asian patients, black patients were more likely to have metastatic disease at diagnosis, black andHispanic patients were less likely to receive definitive treatment, and white, black, and Hispanic patients had worse odds of cancer-specific and overall survival.
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Long non-coding RNA LINC00346 promotes pancreatic cancer growth and gemcitabine resistance by sponging miR-188-3p to derepress BRD4 expression.
Weidong Shi,Chenyue Zhang,Zhouyu Ning,Yongqiang Hua,Ye Li,Lianyu Chen,Luming Liu,Zhen Chen,Zhiqiang Meng +8 more
TL;DR: Long non-coding RNA LINC00346 shows the ability to promote pancreatic cancer growth and gemcitabine resistance, which is in part mediated by antagonization of miR-188-3p and induction of BRD4.
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The prognosis analysis of different metastasis pattern in patients with different breast cancer subtypes: a SEER based study.
TL;DR: It was proven that only bone metastasis was not a prognostic factor in the HR+/HER2-, HR+ /HER2+ and HR-/her2+ subgroup, and patients with brain metastasis had the worst cancer specific survival (CSS) in all the subgroups of BCS.
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A signature of tumor immune microenvironment genes associated with the prognosis of non‑small cell lung cancer.
TL;DR: The genetic signature closely related to the immune microenvironment was found to be able to predict differences in the proportion of immune cells (eosinophils, resting MCs, memory activated CD4 T cells, resting NK cells and plasma cells) in the risk model.