Ovarian cancer is the most lethal gynecological malignancy. Recurrence and chemoresistance are tough challenges leading to poor prognosis. HJURP is a molecular chaperone of CENP-A, which is associated with aggressive progression in multiple tumors. However, the function of HJURP in ovarian cancer has not been elucidated. In our study, we found HJURP was over-expressed in ovarian cancer and high expression of HJURP was correlated to unfavorable prognosis. HJURP knockdown could inhibit proliferation, metastasis and induce G0/G1 stagnation of ovarian cancer cells. Besides, next-generation sequencing (NGS) unveiled that WEE1 was down-regulated by silencing HJURP. Further mechanistic research revealed that HJURP regulated WEE1 through MYC, and luciferase assay indicated that MYC was a transcription factor of WEE1. Additionally, we investigated that silencing HJURP increased sensitivity of ovarian cancer cells to cisplatin via MYC/WEE1 axis, and HJURP participated in DNA repair of cisplatin-induced damage. More interestingly, silencing HJURP could enhance sensitivity of ovarian cancer cells to AZD1775 and improve the synergistic effect of cisplatin plus AZD1775 combined therapy. Collectively, our data displays that HJURP promotes tumor progression and chemoresistance of ovarian cancer, and HJURP has potential to be a novel therapeutic target in the combined treatment with cisplatin and AZD1775 in ovarian cancer.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8771849PMC
http://dx.doi.org/10.7150/ijbs.65589DOI Listing

Publication Analysis

Top Keywords

ovarian cancer
36
hjurp
13
cancer cells
12
silencing hjurp
12
ovarian
9
cancer
9
hjurp promotes
8
prognosis hjurp
8
sensitivity ovarian
8
cisplatin azd1775
8

Similar Publications

Endometriosis is a chronic disease characterised by the presence of endometrial tissue outside the uterine cavity, affecting 5-15% of women, especially those of reproductive age. The disease may manifest itself as dysmenorrhoea, dyspareunia, sterility and chronic pelvic pain, among other symptoms. Although it is not malignant, it shares some characteristics with cancer and can lead to epithelial ovarian carcinoma.

View Article and Find Full Text PDF

Background: SL-172154 is a hexameric fusion protein adjoining the extracellular domain of SIRPα to the extracellular domain of CD40L via an inert IgG-derived Fc domain. In preclinical studies, a murine equivalent SIRPα-Fc-CD40L fusion protein provided superior antitumor immunity in comparison to CD47- and CD40-targeted antibodies. A first-in-human phase I trial of SL-172154 was conducted in patients with platinum-resistant ovarian cancer.

View Article and Find Full Text PDF

CD63 as a potential biomarker for patients with ovarian cancer.

Eur J Obstet Gynecol Reprod Biol

January 2025

Department of Obstetrics and Gynecology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, 2-5-1 Shikata-cho kitaku, Okayama 700-8558, Japan. Electronic address:

Introduction: Exosomes play an important role in regulating physiological processes and mediating the systemic dissemination of various types of cancer. We investigated the association of exosomal tetraspanins CD9, CD63, and CD81 in patients with ovarian cancer (OC).

Material And Methods: We measured the plasma tetraspanins CD9, CD63, and CD81 by enzyme-linked immunosorbent assay in 91 patients who underwent treatment for OC between April 2018 and March 2024.

View Article and Find Full Text PDF

Purpose: The increase of oral tumor therapies (OTT) poses new challenges in patient care. Within CAMPA (Care improvement for advanced or metastatic breast and ovarian cancer patients treated with PARP-inhibitors), additional nursing support for patients treated with PARP-inhibitors was developed.

Methods: Additional nursing support (1 year) was evaluated in breast and gynecooncological cancer patients at an academic and a non-academic outreach center.

View Article and Find Full Text PDF

Graphical models are powerful tools to investigate complex dependency structures in high-throughput datasets. However, most existing graphical models make one of two canonical assumptions: (i) a homogeneous graph with a common network for all subjects or (ii) an assumption of normality, especially in the context of Gaussian graphical models. Both assumptions are restrictive and can fail to hold in certain applications such as proteomic networks in cancer.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!