98%
921
2 minutes
20
Background: Breast cancer is the leading cause of cancer-related deaths among women worldwide. Deciphering the molecular mechanisms of breast cancer is crucial for developing targeted therapeutic approaches.
Methods: This study analyzed gene expression profiles from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) in breast cancer. Mendelian randomization (MR) analysis was then employed using publicly available eQTL databases to evaluate potential causal relationships between these DEGs and breast cancer. Enrichment analyses were further conducted to explore their functional significance. Furthermore, external validation of co-expressed genes was conducted using The Cancer Genome Atlas (TCGA) database. In vitro functional assays and drug sensitivity analyses were performed on selected target genes to validate their roles in breast cancer pathogenesis and treatment.
Results: A total of 1052 upregulated and 1380 downregulated genes were identified in breast cancer. Additionally, MR analysis revealed 12 significant co-expressed genes potentially contributing to breast cancer pathogenesis. These genes were primarily enriched in lipid metabolism and immune responses via regulating microRNA functions and AMPK signaling. Validation through the TCGA database confirmed differential expression of these genes in breast cancer tissues. Strikingly, functional assays of the less-reported genes DNASE2 and ATOH8 demonstrated their involvement in breast cancer pathogenesis through modulating proliferation, migration, and invasion of cancer cells. Notably, several commonly used clinical drugs for breast cancer management, such as 5-Fluorouracil, exhibited dramatically increased sensitivity to DNASE2 and ATOH8 expression.
Conclusions: Our study provides novel insights into the molecular basis of breast cancer pathogenesis and identifies promising therapeutic strategies for this condition.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12025194 | PMC |
http://dx.doi.org/10.3390/biology14040405 | DOI Listing |
JCO Glob Oncol
May 2025
Department of Biochemistry, All India Institute of Medical Sciences, New Delhi, India.
Purpose: Breast cancer remains a significant public health challenge globally, as well as in India, where it is the most frequently diagnosed cancer in females. Significant disparities in incidence, mortality, and access to health care across India's sociodemographically diverse population highlight the need for increased awareness, policy reform, and research.
Design: This review consolidates data from national cancer registries, global cancer databases, and institutional findings from a tertiary care center to examine the epidemiology, clinical challenges, and management gaps specific to India.
J Med Screen
September 2025
The Cancer Registry of Norway, Department of Screening programs, Norwegian Institute of Public Health, Oslo, Norway.
ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.
View Article and Find Full Text PDFJ Natl Cancer Inst
September 2025
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, United States.
Background: Among childhood cancer survivors, germline rare variants in autosomal dominant cancer susceptibility genes (AD CSGs) could increase subsequent neoplasm (SNs) risks, but risks for rarer SNs and by age at onset are not well understood.
Methods: We pooled the Childhood Cancer Survivor Study and St Jude Lifetime Cohort (median follow-up = 29.7 years, range 7.
PLoS One
September 2025
Institute of Computational Science and Technology, Guangzhou University, Guangzhou, China.
MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.
Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.