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Zhengqin Gu

Researcher at Shanghai Jiao Tong University

Publications -  5
Citations -  187

Zhengqin Gu is an academic researcher from Shanghai Jiao Tong University. The author has contributed to research in topics: Bladder cancer & Medicine. The author has an hindex of 3, co-authored 3 publications receiving 149 citations.

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In vitro study on the binding of gemcitabine to bovine serum albumin.

TL;DR: Forescence resonance energy transfer (FRET) analysis proved high probability of energy transfer from Trp residue to the drug molecule and alterations of protein secondary structure in the presence of gemcitabine were assessed by CD UV-vis and FT-IR spectroscopy.
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Long non‑coding RNA‑GAS5 acts as a tumor suppressor in bladder transitional cell carcinoma via regulation of chemokine (C‑C motif) ligand 1 expression

TL;DR: Investigation of whether lncRNA-growth arrest-specific (GAS)5 regulated bladder cancer progression via regulation of chemokine (C-C) ligand (CCL)1 expression demonstrated that GAS5 was able to suppress bladder cancer cell proliferation, at least partially, by suppressing the expression of CCL1.
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CXCR4-mediated Stat3 activation is essential for CXCL12-induced cell invasion in bladder cancer

TL;DR: In this article, expression analysis of bladder cancer and adjacent normal tissues showed that higher CXCR4 expression was associated with Stat3 phosphorylation, and blocking Stat3 activity with the chemical inhibitor Stattic inhibited CXCL12-triggered Stat3 activation and cell invasion in T24 cells.
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Tumor-associated macrophages promote migration and invasion via modulating IL-6/STAT3 signaling in renal cell carcinoma.

TL;DR: It is revealed that high infiltration of TAMs may promote RCC cells migration, invasion, and EMT via modulating IL-6/STAT3 signaling, further suggesting a potential of novel treatment strategies targeting TAMs or IL- 6 for metastatic RCC.
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Modified Prostate Health Index Density Significantly Improves Clinically Significant Prostate Cancer (csPCa) Detection

TL;DR: A modified PHI density (mPHI) model is developed that can sensitively distinguish early-stage csPCa patients within the PSA gray zone and exceeds diagnostic performance with a better net benefit in decision curve analysis (DCA) compared with PHI or PHID.