Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death, often diagnosed at advanced stages and characterized by high recurrence rates. While chronic liver inflammation and metabolic dysfunction are recognized contributors to tumorigenesis, the molecular mechanisms linking early microenvironmental stress to malignant transformation remain poorly understood. MYCN, a proto-oncogenic transcription factor, has emerged as a potential biomarker of cancer stemness, yet its functional role in hepatocarcinogenesis is unclear. Here, we elucidate the oncogenic role of MYCN and its dynamic regulation during metabolic liver tumorigenesis. Transcriptomic profiling revealed that MYCN-driven tumors exhibit features of human HCC subtypes enriched in stress-adaptive transcriptional programs. Time-resolved spatial transcriptomics further uncovered a MYCN-enriched niche characterized by epithelial–mesenchymal transition (EMT) and Wnt/β-catenin signaling, which expanded during tumor progression and was spatially proximate to transformed malignant cells.
from sddb import Catalog
cohort = Catalog().collection("geo-gse302815")
cohort.members # the 4 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-gse302815")
coll.ordered_artifacts.to_dataframe() # the 4 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-gse302815 --dest data/Licenses: 4 NCBI GEO policy
@article{Qin_2026,
title = {Oncogenic function and transcriptional dynamics of MYCN in liver tumorigenesis},
volume = {123},
ISSN = {1091-6490},
url = {http://dx.doi.org/10.1073/pnas.2521923123},
DOI = {10.1073/pnas.2521923123},
number = {8},
journal = {Proceedings of the National Academy of Sciences},
publisher = {National Academy of Sciences},
author = {Qin, Xian-Yang and Xu, Yali and Mishra, Hricha and Shirakami, Yohei and Yeh, Shiou-Hwei and Li, Chiao-Ling and Numata, Kazushi and Suenaga, Yusuke and Wei, Feifei and Ando, Reiko and Nishimura, Hajime and Furuhata, Erina and Maeda, Shiori and Furutani, Yutaka and Yanaka, Kaori and Yamamoto, Masahiro and Goto, Masanori and Takasawa, Akira and Nishikawa, Yuji and Tomita, Hiroyuki and Gailhouste, Luc and Matsuura, Tomokazu and Chen, Pei-Jer and Shimizu, Masahito and Hippo, Yoshitaka and Suzuki, Harukazu},
year = {2026},
month = Feb
}