Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes
by
Qu, Jia
, Wang, Mei-Huan
, Zhang, Hua-Wei
, Gao, Yue-Hua
in
Original Research
2025
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.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
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?
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes
by
Qu, Jia
, Wang, Mei-Huan
, Zhang, Hua-Wei
, Gao, Yue-Hua
in
Original Research
2025
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.
Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes
Journal Article
Identification of Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes
2025
Request Book From Autostore
and Choose the Collection Method
Overview
The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear.
The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters.
The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs.
The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.
Publisher
SAGE Publications,SAGE Publishing
Subject
This website uses cookies to ensure you get the best experience on our website.