Manganese superoxide dismutase in breast cancer: from molecular mechanisms of gene regulation to biological and clinical significance.

Free Radic Biol Med

Centre de Recherche en Automatique de Nancy, UMR 7039 CNRS, Faculté des Sciences et Technologies, Université de Lorraine, 54506 Vandoeuvre-lès-Nancy Cedex, France.

Published: December 2014

Breast cancer is one of the most common malignancies of all cancers in women worldwide. Many difficulties reside in the prediction of tumor metastatic progression because of the lack of sufficiently reliable predictive biological markers, and this is a permanent preoccupation for clinicians. Manganese superoxide dismutase (MnSOD) may represent a rational candidate as a predictive biomarker of breast tumor metastatic progression, because its gene expression is profoundly altered between early and advanced breast cancer, in contrast to expression in the normal mammary gland. In this review, we report the characterization of some gene polymorphisms and molecular mechanisms of SOD2 gene regulation, which allows a better understanding of how MnSOD is decreased in early breast cancer and increased in advanced breast cancer. Several studies display the biological significance of MnSOD level in proliferation as well as in invasive and angiogenic abilities of breast tumor cells by controlling superoxide anion radical (O2(•-)) and hydrogen peroxide (H2O2). Particularly, they report how these reactive oxygen species may activate some signaling pathways involved in breast tumor growth. Emerging understanding of these findings provides an interesting framework for guiding translational research and suggests a way to define precisely the clinical interest of MnSOD as a prognostic and/or predicting marker in breast cancer, by associating with some regulators involved in SOD2 gene regulation and other well-known biomarkers, in addition to the typical clinical parameters.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.freeradbiomed.2014.08.026DOI Listing

Publication Analysis

Top Keywords

breast cancer
24
gene regulation
12
breast tumor
12
breast
9
manganese superoxide
8
superoxide dismutase
8
molecular mechanisms
8
tumor metastatic
8
metastatic progression
8
advanced breast
8

Similar Publications

Background: Artificial sweeteners (AS) have been widely utilized in the food, beverage, and pharmaceutical industries for decades. While numerous publications have suggested a potential link between AS and diseases, particularly cancer, controversy still surrounds this issue. This study aims to investigate the association between AS consumption and cancer risk.

View Article and Find Full Text PDF

Background: TP53 variant classification benefits from the availability of large-scale functional data for missense variants generated using cDNA-based assays. However, absence of comprehensive splicing assay data for TP53 confounds the classification of the subset of predicted missense and synonymous variants that are also predicted to alter splicing. Our study aimed to generate and apply splicing assay data for a prioritised group of 59 TP53 predicted missense or synonymous variants that are also predicted to affect splicing by either SpliceAI or MaxEntScan.

View Article and Find Full Text PDF

Adjuvant endocrine therapy (AET) is essential for improving survival and reducing mortality and recurrence rates in breast cancer (BrCa) patients. However, the adherence to AET among BrCa patients is poor, and there is no scale to measure adherence to AET or the reasons for non-adherence among BrCa patients in mainland China. The aim of this study was to assess the psychometric properties of the simple Chinese version of the Medication Adherence Reasons (MAR) scale in BrCa patients undergoing AET.

View Article and Find Full Text PDF

Background: Benign and malignant breast tumors differ in their microvasculature morphology and distribution. Histologic biomarkers of malignant breast tumors are also correlated with the microvasculature. There is a lack of imaging technology for evaluating the microvasculature.

View Article and Find Full Text PDF

Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

BMC Med Imaging

January 2025

Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.

Problem: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manmade visual details that are complex to generalize across ultrasound images utilizing distinct techniques.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!