Results 281 to 290 of about 48,044,330 (330)

Investigating the cell of origin and novel molecular targets in Merkel cell carcinoma: a historic misnomer

Molecular Oncology, EarlyView.
This study indicates that Merkel cell carcinoma (MCC) does not originate from Merkel cells, and identifies gene, protein & cellular expression of immune‐linked and neuroendocrine markers in primary and metastatic Merkel cell carcinoma (MCC) tumor samples, linked to Merkel cell polyomavirus (MCPyV) status, with enrichment of B‐cell and other immune cell
Richie Jeremian, Sriraam Sivachandran, Melissa Galati, Brandon Ramchatesingh, Hibo Rijal, Johnny Hanna, Elena Netchiporouk, May Chergui, Margaret Redpath, Samy Abou Setah, Ivan V. Litvinov   +10 more
wiley   +1 more source

Harnessing genomic prediction in Brassica napus through a nested association mapping population. [PDF]

Plant Genome
Perumal S, Higgins EE, Sra S, Khedikar Y, Moore J, Chaudary R, Haile T, Koh CS, Vail S, Robinson SJ, Horner K, Hope B, Klein-Gebbinck H, Herrmann DT, Navabi K, Sharpe AG, Parkin IAP.   +16 more
europepmc   +1 more source

The influence of ROS1 fusion partners and resistance mechanisms in ROS1‐TKI‐treated non‐small cell lung cancer patients

Molecular Oncology, EarlyView.
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, Christa Dijkhuizen, Patrick Korthuis, Wim Timens, Harry Groen, Jeroen Hiltermann, Anke van den Berg, Lyndsay Drayer, Anthonie van der Wekken   +8 more
wiley   +1 more source

Living well? The unintended consequences of highly popular commercial fitness apps through social listening using Machine-Assisted Topic Analysis: Evidence from X. [PDF]

Br J Health Psychol
Sheen F, Porter L, Papakonstantinou T, Ceka M, Bondaronek P.   +4 more
europepmc   +1 more source

YAP1::TFE3 mediates endothelial‐to‐mesenchymal plasticity in epithelioid hemangioendothelioma

Molecular Oncology, EarlyView.
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, Samuel Hartzler, Paula A. Vargas Carranza, Shyaman Jayasundara, Madison E. Yates, Nimod D. Janson, Bozhi Liu, Annaleigh Benton, Majid Kazemian, Jason A. Hanna   +9 more
wiley   +1 more source

Association of antipsychotic drug target genes and risk of lung cancer: a Mendelian randomization study. [PDF]

Discov Oncol
Xiang J, Xiang G, Zhan M, Zhang L, Zeng H, Song X.   +5 more
europepmc   +1 more source

Emerging role of ARHGAP29 in melanoma cell phenotype switching

Molecular Oncology, EarlyView.
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, Melanie Kappelmann‐Fenzl, Anja Katrin Bosserhoff, Nicole Rachinger   +3 more
wiley   +1 more source

Network meta-analysis of high tibial osteotomy combined with different bone defect filler materials for medial compartment knee osteoarthritis. [PDF]

J Orthop Surg Res
Pei Z, Tian A, Zhang B, Lian Z, Ma X.
europepmc   +1 more source

Tumor‐agnostic detection of circulating tumor DNA in patients with advanced pancreatic cancer using targeted DNA methylation sequencing and cell‐free DNA fragmentomics

Molecular Oncology, EarlyView.
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, Kjersti Tjensvoll, Karin Hestnes Edland, Satu Oltedal, Herish Garresori, Bjørnar Gilje, Saga Ekedal, Trygve Eftestøl, Jan Terje Kvaløy, Filip Janku, Oddmund Nordgård   +10 more
wiley   +1 more source