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Objective: CircRNAs are emerging as vital regulators in a variety of cancers. However, the expression pattern and potential mechanism of circRNAs in triple-negative breast cancer remain unclear. In this study, we aim to systematically investigate circRNAs alteration in triple-negative breast cancer tissues.
Methods: Microarray and bioinformatics analyses were used to identify circRNAs expression in cancer tissues. qRT-PCR was conducted to measure the expression of RNAs. Cell Counting Kit-8, wound-healing and transwell assays were conducted to investigate the function of circRNAs. Dual-luciferase reporter assay was performed to validate target binding.
Results: Hsa_circ_0131242 was highly expressed in both cancer tissues and cell lines compared to control. Subsequently, statistical analyses revealed that high expression of hsa_circ_0131242 was positively correlated with advanced tumor stages and poorer clinical features in cancer patients. Hsa_circ_0131242 knockdown could suppress the progression of breast cancer cells. Bioinformatics prediction and luciferase reporter assay showed that hsa_circ_0131242 acted as a sponge for hsa-miR-2682. Moreover, co-transfection of hsa-miR-2682 inhibitor and si-hsa_circ_0131242 rescued cell proliferation and migration in BT549 and MDA-MB-468 cell lines.
Conclusion: Our study identified hsa_circ_0131242 expression in TNBC for the first time and found that hsa_circ_0131242 may promote triple-negative breast cancer progression by sponging hsa-miR-2682.
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http://dx.doi.org/10.2147/OTT.S246957 | 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.