Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Cyclosporine A (CsA) is an immunosuppressant primarily used at a higher dosage in transplant medicine and autoimmune diseases with a higher success rate. At lower doses, CsA exhibits immunomodulatory properties. CsA has also been reported to inhibit breast cancer cell growth by downregulating the expression of pyruvate kinase. However, differential dose-response effects of CsA in cell growth, colonization, apoptosis, and autophagy remain largely unidentified in breast cancer cells. Herein, we showed the cell growth-inhibiting effects of CsA by preventing cell colonization and enhancing DNA damage and apoptotic index at a relatively lower concentration of 2 µM in MCF-7 breast cancer cells. However, at a higher concentration of 20 µM, CsA leads to differential expression of autophagy-related genes ATG1, ATG8, and ATG9 and apoptosis-associated markers, such as Bcl-2, Bcl-XL, Bad, and Bax, indicating a dose-response effect on differential cell death mechanisms in MCF-7 cells. This was confirmed in the protein-protein interaction network of COX-2 (PTGS2), a prime target of CsA, which had close interactions with Bcl-2, p53, EGFR, and STAT3. Furthermore, we investigated the combined effect of CsA with SHP2/PI3K-AKT inhibitors showing significant MCF-7 cell growth reduction, suggesting its potential to use as an adjuvant during breast cancer therapy.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10787-023-01247-4DOI Listing

Publication Analysis

Top Keywords

breast cancer
16
cell growth
12
differential dose-response
8
apoptosis autophagy
8
mcf-7 cells
8
csa
8
effects csa
8
cancer cells
8
cell
6
differential
4

Similar Publications

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.

View Article and Find Full Text PDF

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 PDF

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.

View Article and Find Full Text PDF

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 PDF

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.

View Article and Find Full Text PDF