Back

Metastatic niche mediated activation of metastasis initiating cells in ovarian cancer through miR-193b-3p downregulation via the ERK/EZH2/DNMT1 axis

Dasari, S.; Wang, J.; Cheng, F.; Melissa Halprin, M.; Pepin, D.; Yang-Hartwich, Y.; Mitra, A. K.

2025-06-20 cancer biology
10.1101/2025.06.17.660246 bioRxiv
Show abstract

Extensive metastasis at the time of diagnosis is a major contributor to the poor prognosis of ovarian cancer (OC) patients. There is a critical need to better understand the mechanism of regulation of metastasis to develop effective treatment strategies targeting the process. Metastasis initiating cells (MICs) have cancer stem cell-like properties along with the ability to invade. Their potential role in OC is unique as the dissemination from the primary tumors involves passive processes like exfoliation. However, the role of MICs during OC metastatic colonization is critical and poorly understood. Using an organotypic 3D culture model of the human omentum, we have studied the productive crosstalk between OC MICs and the metastatic microenvironment. We report the role of miR-193b-3p, a clinically relevant metastasis suppressor microRNA, which is downregulated in the OC by paracrine signals from the microenvironment, inducing the MIC phenotype. Using heterotypic coculture models, conditioned medium experiments, secretome analysis, inhibition, and rescue experiments, we show that bFGF and IGFBP6 secreted by mesothelial cells in the microenvironment induce miR-193b-3p downregulation in OC MICs via the ERK/EZH2/DNMT1 axis. The miR-193b-3p downregulation induced an increased expression of its target cyclin D1, which imparted a cancer stem cell phenotype. Urokinase, another target of miR-193b-3p, induced invasive growth. Together, these targets impart the MIC phenotype to the OC cells. miR-193b-3p replacement therapy could suppress metastasis in a patient derived xenograft model of OC metastasis, indicating the translational potential of this approach to target MICs in OC patients.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

1
eLife
5828 papers in training set
Top 17%
6.6%
2
Scientific Reports
3612 papers in training set
Top 11%
6.6%
3
Nature Communications
5641 papers in training set
Top 26%
6.2%
4
Cell Reports
1498 papers in training set
Top 6%
6.2%
5
Communications Biology
993 papers in training set
Top 2%
4.8%
6
Oncogenesis
12 papers in training set
Top 0.1%
4.2%
7
Cancers
213 papers in training set
Top 1%
4.2%
8
Molecular Cancer Research
49 papers in training set
Top 0.2%
4.2%
9
iScience
1154 papers in training set
Top 5%
4.0%
10
Cell Death & Disease
126 papers in training set
Top 0.7%
3.5%
50% of probability mass above
11
Journal of Experimental & Clinical Cancer Research
25 papers in training set
Top 0.1%
3.4%
12
Cancer Research
130 papers in training set
Top 1%
2.6%
13
Science Advances
1243 papers in training set
Top 13%
2.6%
14
npj Precision Oncology
53 papers in training set
Top 0.5%
2.6%
15
Molecular Cancer
16 papers in training set
Top 0.1%
1.9%
16
Oncogene
85 papers in training set
Top 1.0%
1.9%
17
Neoplasia
23 papers in training set
Top 0.3%
1.7%
18
Cells
249 papers in training set
Top 3%
1.7%
19
International Journal of Cancer
49 papers in training set
Top 0.7%
1.5%
20
Molecular Cancer Therapeutics
40 papers in training set
Top 0.7%
1.1%
21
Cancer Research Communications
51 papers in training set
Top 1%
1.1%
22
Cancer Letters
35 papers in training set
Top 1.0%
1.0%
23
Heliyon
152 papers in training set
Top 8%
0.8%
24
The EMBO Journal
309 papers in training set
Top 7%
0.8%
25
Life Science Alliance
285 papers in training set
Top 8%
0.8%
26
The Journal of Pathology
26 papers in training set
Top 0.9%
0.8%
27
Cell Communication and Signaling
51 papers in training set
Top 1%
0.8%
28
Molecular Oncology
55 papers in training set
Top 1%
0.8%
29
Cell Death Discovery
58 papers in training set
Top 2%
0.6%
30
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 46%
0.6%