| Title |
Galectin-3 induces ovarian cancer cell survival and chemoresistance via TLR4 signaling activation
|
|---|---|
| Published in |
Tumor Biology, April 2016
|
| DOI | 10.1007/s13277-016-5038-6 |
| Pubmed ID | |
| Authors |
Guoqing Cai, Xiangdong Ma, Biliang Chen, Yanhong Huang, Shujuan Liu, Hong Yang, Wei Zou |
| Abstract |
Paclitaxel resistance becomes common in patients with aggressive ovarian cancer and results in recurrence after conventional therapy. Galectin-3 is a multifunctional lectin associated with cell migration, cell proliferation, cell adhesion, and cell-cell interaction in tumor cells. Whether circulating galectin-3 is involved in paclitaxel resistance in ovarian cancer remains unknown. The current study investigated the effect of galectin-3 on toll-like receptor 4 (TLR4) signaling and thus paclitaxel resistance. With blood and cancer tissue samples obtained from 102 patients, we identified associations between serum galectin-3 level or TLR4 expression and paclitaxel resistance phenotype. In vitro, treatment with exogenous galectin-3 restored cell survival and migration of SKOV-3 and ES-2 cells was decreased by galectin-3 silencing and paclitaxel treatment. Furthermore, exogenous galectin-3 boosted expression of TLR4, MyD88, and p-p65, as well as interleukin (IL)-6, IL-8, and vascular endothelial growth factor (VEGF) release induced by paclitaxel. Moreover, galectin-3 inhibited the interaction between TLR4 and caveolin-1 (Cav-1) in SKOV-3 and ES-2 cells. In addition, overexpression of Cav-1 dampened the expression of MyD88 and p-p65 stimulated by galectin-3 and enhanced apoptosis in SKOV-3 cells under paclitaxel exposure. In summary, our study elucidated that exogenous galectin-3 might induce paclitaxel resistance through TLR4 signaling activation by inhibiting TLR4-Cav-1 interaction, revealing a novel insight into paclitaxel resistance induction. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 26% |
| Student > Bachelor | 3 | 10% |
| Student > Doctoral Student | 3 | 10% |
| Student > Master | 3 | 10% |
| Researcher | 3 | 10% |
| Other | 6 | 19% |
| Unknown | 5 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 16% |
| Agricultural and Biological Sciences | 5 | 16% |
| Medicine and Dentistry | 5 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 13% |
| Nursing and Health Professions | 2 | 6% |
| Other | 3 | 10% |
| Unknown | 7 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 April 2016.
All research outputs
#18,450,346
of 22,860,626 outputs
Outputs from Tumor Biology
#1,369
of 2,622 outputs
Outputs of similar age
#220,527
of 300,819 outputs
Outputs of similar age from Tumor Biology
#24
of 44 outputs
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