98%
921
2 minutes
20
Epithelial-mesenchymal transition (EMT) is a cellular plasticity program critical for embryonic development and tissue regeneration, and aberrant EMT is associated with disease including cancer. The high degree of plasticity in the mammary epithelium is reflected in extensive heterogeneity among breast cancers. Here, we have analyzed RNA-sequencing data from three different mammary epithelial cell line-derived EMT models and identified a robust mammary EMT gene expression signature that separates breast cancers into distinct subgroups. Most strikingly, the basal-like breast cancers form two subgroups displaying partial-EMT and post-EMT gene expression patterns. We present evidence that key EMT-associated transcription factors play distinct roles at different stages of EMT in mammary epithelial cells.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10726128 | PMC |
http://dx.doi.org/10.3389/fonc.2023.1249895 | DOI Listing |
JMIR Cancer
September 2025
Cancer Patients Europe, Rue de l'Industrie 24, Brussels, 1000, Belgium.
Background: Breast cancer is the most common cancer among women and a leading cause of mortality in Europe. Early detection through screening reduces mortality, yet participation in mammography-based programs remains suboptimal due to discomfort, radiation exposure, and accessibility issues. Thermography, particularly when driven by artificial intelligence (AI), is being explored as a noninvasive, radiation-free alternative.
View Article and Find Full Text PDFEpidemiol Serv Saude
September 2025
Universidade Estadual do Norte do Paraná, Programa de Pós-Graduação em Enfermagem em Atenção Primária à Saúde Bandeirantes, PR, Brazil.
Objectives: To analyze the temporal trend and identify spatial clusters of breast cancer mortality in Paraná state between 2012 and 2021.
Methods: This was a time series study, with spatial analysis of breast cancer mortality rates in the 399 municipalities of Paraná. Data were selected from the Mortality Information System.
Cien Saude Colet
August 2025
Programa de Pós-Graduação em Nutrição e Saúde, Universidade Estadual do Ceará. R. Betel 1958, Itaperi. 60714-230 Fortaleza CE Brasil.
This study aimed to evaluate mortality due to female breast cancer attributable to overweight and obesity and to estimate the number of preventable deaths with a reduction in the Body Mass Index in Brazil. An ecological study was carried out with investigation of information on overweight, obesity, sociodemographic characteristics based on a national survey carried out in 2013-14; breast cancer mortality rate in 2019 using the Online Atlas of Mortality and Relative Risk Meta-Analyses. The Potential Impact Fraction analysis was carried out, considering the following counterfactual scenarios related to the reduction in BMI: Scenario A - population contingent of women that make up the prevalence of overweight and obesity now composes the prevalence of eutrophy; Scenario B - population contingent of women that make up the prevalence of overweight starts to make up the prevalence of eutrophy; Scenario C - population contingent of women that make up the prevalence of obesity becomes part of the prevalence of overweight.
View Article and Find Full Text PDFSci Transl Med
September 2025
Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Precision Cancer Medicine Center, Fudan University Shanghai Cancer Center, Shanghai 200032, P. R. China.
Triple-negative breast cancers (TNBCs) lack predictive biomarkers to guide immunotherapy, especially during early-stage disease. To address this issue, we used single-cell RNA sequencing, bulk transcriptomics, and pathology assays on samples from 171 patients with early-stage TNBC receiving chemotherapy with or without immunotherapy. Our investigation identified an enriched subset of interferon (IFN)-induced CD8 T cells in early TNBC samples, which predict immunotherapy nonresponsiveness.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
The tumor microenvironment is a dynamic eco system where cellular interactions drive cancer progression. However, inferring cell-cell communication from non-spatial scRNA-seq data remains challenging due to incomplete li gand-receptor databases and noisy cell type annotations. H ere, we propose scGraphDap, a graph neural network frame work that integrates functional state pseudo-labels and graph structure learning to improve both cell type annotation an d CCC inference.
View Article and Find Full Text PDF