Asset Details
Do you wish to reserve the book?
Bioinformatics analysis identifies dysregulation of miR-548F-3p and its hub gene in triple-negative breast cancer
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Bioinformatics analysis identifies dysregulation of miR-548F-3p and its hub gene in triple-negative breast cancer
Do you wish to request the book?
Bioinformatics analysis identifies dysregulation of miR-548F-3p and its hub gene in triple-negative breast cancer
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Bioinformatics analysis identifies dysregulation of miR-548F-3p and its hub gene in triple-negative breast cancer
2025
Overview
Triple-negative breast cancer (TNBC), which affects 15-20% of cases, lacks targeted therapies and poses challenges in treatment. MicroRNAs (miRNAs) are potential biomarkers and therapeutic targets in breast cancer. To unravel its unique regulatory role, this study focused on miRNA microarray analysis, particularly miR-548F-3p, in TNBC samples.
Using the GSE76275 dataset, gene expression profiles were analyzed using the Affymetrix Human Genome U133 Plus 2.0 Array. Differentially expressed genes (DEGs) were identified using robust preprocessing. Weighted gene co-expression network analysis (WGCNA) explored gene modules and identified hub genes co-expressed with miR-548F-3p. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted. Survival analysis was used to assess the prognostic impact of the identified genes.
The study found 224 up-regulated DEGs, with miR-548F-3p exhibiting significant down-regulation. MultimiR identified 400 genes that were targeted by miR-548F-3p. WGCNA revealed a blue co-expression module, with 356 genes targeted by miR-548F-3p. A Venn diagram identified common genes, including VANGL2, BRCC3, ANP32E, and ANLN. Functional enrichment highlighted crucial pathways in TNBC pathogenesis, including mitotic spindle organization, spindle assembly checkpoint signaling, cell cycle, and amino acid (serine) metabolism. PPI network analysis identified hub genes, including FOXM1, KIF23, and CDC20. VANGL2, BRCC3, ANP32E, and ANLN were significantly associated with patient outcomes in survival analysis.
This analysis highlighted TNBC's molecular landscape, emphasizing miR-548F-3p's regulatory role. The identified genes, VANGL2, BRCC3, ANP32E, and ANLN, offer insights into TNBC pathogenesis and potential therapeutic targets, laying the foundation for understanding their clinical implications in the intricate landscape of TNBC.
Publisher
Mashhad University of Medical Sciences
