Ovarian cancer is one of the leading causes of death in females in the world. High-grade serous ovarian carcinoma (HGSOC) is the most common histological subtype, and the platinum-resistance is a clinical challenge. Moreover, JAK1-related inflammation may contribute to the platinum-resistance. This study investigated the spatial gene expression profiles of JAK1-high and -low expressed HGSOC.
from sddb import Catalog
cohort = Catalog().collection("geo-gse274657")
cohort.members # the 3 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="geo-gse274657")
coll.ordered_artifacts.to_dataframe() # the 3 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse274657 --dest data/Licenses: 3 NCBI GEO policy
@article{Suzuki_2025,
title = {Overcoming platinum-resistant ovarian cancer targeting the activated JAK-STAT pathways via extracellular vesicles},
volume = {8},
ISSN = {2399-3642},
url = {http://dx.doi.org/10.1038/s42003-025-08771-9},
DOI = {10.1038/s42003-025-08771-9},
number = {1},
journal = {Communications Biology},
publisher = {Springer Science and Business Media LLC},
author = {Suzuki, Kazuhiro and Yokoi, Akira and Yoshida, Kosuke and Suzuki, Hironori and Kitagawa, Masami and Asano-Inami, Eri and Matsuo, Seiko and Yoshihara, Masato and Tamauchi, Satoshi and Yoshikawa, Nobuhisa and Niimi, Kaoru and Sudo, Tamotsu and Yamaguchi, Satoshi and Yamamoto, Yusuke and Kajiyama, Hiroaki},
year = {2025},
month = Aug
}