Results 271 to 280 of about 11,841,902 (391)
Mechanism of action of immune response genes
openaire +5 more sources
This real‐world study of ROS1+ NSCLC highlights fusion diversity, treatment outcomes with crizotinib and lorlatinib, and in vitro experiments with resistance mechanisms. G2032R drives strong resistance to ROS1‐targeted TKIs, especially lorlatinib. Fusion partner location does not affect overall survival to crizotinib or lorlatinib. Findings support the
Fenneke Zwierenga +8 more
wiley +1 more source
PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context. [PDF]
Zhong G +4 more
europepmc +1 more source
YAP1::TFE3 mediates endothelial‐to‐mesenchymal plasticity in epithelioid hemangioendothelioma
The YAP1::TFE3 fusion protein drives endothelial‐to‐mesenchymal transition (EndMT) plasticity, resulting in the loss of endothelial characteristics and gain of mesenchymal‐like properties, including resistance to anoikis, increased migratory capacity, and loss of contact growth inhibition in endothelial cells.
Ant Murphy +9 more
wiley +1 more source
Autosomal dominant tubulointerstitial kidney disease-UMOD: a short review. [PDF]
Qiao P, Wang Z, Xie J.
europepmc +1 more source
Emerging role of ARHGAP29 in melanoma cell phenotype switching
This study gives first insights into the role of ARHGAP29 in malignant melanoma. ARHGAP29 was revealed to be connected to tumor cell plasticity, promoting a mesenchymal‐like, invasive phenotype and driving tumor progression. Further, it modulates cell spreading by influencing RhoA/ROCK signaling and affects SMAD2 activity. Rho GTPase‐activating protein
Beatrice Charlotte Tröster +3 more
wiley +1 more source
A sweet barrier to gene silencing: Glucose metabolism interferes with antisense oligonucleotide therapy. [PDF]
Duan S, Zhang W, Cao H.
europepmc +1 more source
We evaluated circulating tumor DNA (ctDNA) detection in advanced pancreatic cancer using DNA methylation, cell‐free DNA fragment lengths, and 5′ end motifs. Machine learning models were trained to estimate ctDNA levels from each feature and their combination.
Morten Lapin +10 more
wiley +1 more source

