Background: Ductal carcinoma in situ (DCIS) of the breast is an early stage of breast cancer, and preventing its progression to invasive ductal carcinoma (IDC) is crucial for the early detection and treatment of breast cancer. Although single-cell transcriptome analysis technology has been widely used in breast cancer research, the biological mechanisms underlying the transition from DCIS to IDC remain poorly understood.
Results: We identified eight cell types through cell annotation, finding significant differences in T cell proportions between DCIS and IDC. Using this as a basis, we performed pseudotime analysis on T cell subpopulations, revealing that differentially expressed genes primarily regulate immune cell migration and modulation. By intersecting WGCNA results of T cells highly correlated with the subtypes and the differentially expressed genes, we identified six key genes: FGFBP2, GNLY, KLRD1, TYROBP, PRF1, and NKG7. Excluding PRF1, the other five genes were significantly associated with overall survival in breast cancer, highlighting their potential as prognostic biomarkers.
Conclusions: We identified immune cells that may play a role in the progression from DCIS to IDC and uncovered five key genes that can serve as prognostic markers for breast cancer. These findings provide insights into the mechanisms underlying the transition from DCIS to IDC, offering valuable perspectives for future research. Additionally, our results contribute to a better understanding of the biological processes involved in breast cancer progression.
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http://dx.doi.org/10.1186/s12967-024-05706-6 | DOI Listing |
IUBMB Life
January 2025
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang, China.
Triple-negative breast cancer (TNBC) remains a significant global health challenge, emphasizing the need for precise identification of patients with specific therapeutic targets and those at high risk of metastasis. This study aimed to identify novel therapeutic targets for personalized treatment of TNBC patients by elucidating their roles in cell cycle regulation. Using weighted gene co-expression network analysis (WGCNA), we identified 83 hub genes by integrating gene expression profiles with clinical pathological grades.
View Article and Find Full Text PDFCancer
February 2025
General Medicine Service, VA Puget Sound Health Care System, Seattle, Washington, USA.
Background: Breast cancer screening (BCS) inequities are evident at national and local levels, and many health systems want to address these inequities, but may lack data about contributing factors. The objective of this study was to inform health system interventions through an exploratory analysis of potential multilevel contributors to BCS inequities using health system data.
Methods: The authors conducted a cross-sectional analysis within a large academic health system including 19,774 individuals who identified as Black (n = 1445) or White (n = 18,329) race and were eligible for BCS.
J Adv Nurs
January 2025
Anesthesiology Department, Hebei Province Cangzhou Hospital of Integrated Traditional and Western Medicine, Cangzhou, Hebei, China.
Cancer
February 2025
Departmental Unit of Molecular and Genomic Diagnostics, Genomics Core Facility, G-STeP, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy.
Background: To date, 11 DNA polymerase epsilon (POLE) pathogenic variants have been declared "hotspot" mutations. Patients with endometrial cancer (EC) characterized by POLE hotspot mutations (POLEmut) have exceptional survival outcomes. Whereas international guidelines encourage deescalation of adjuvant treatment in early-stage POLEmut EC, data regarding safety in POLEmut patients with unfavorable characteristics are still under investigation.
View Article and Find Full Text PDFStat Med
February 2025
Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA.
Multi-gene panel testing allows efficient detection of pathogenic variants in cancer susceptibility genes including moderate-risk genes such as ATM and PALB2. A growing number of studies examine the risk of breast cancer (BC) conferred by pathogenic variants of these genes. A meta-analysis combining the reported risk estimates can provide an overall estimate of age-specific risk of developing BC, that is, penetrance for a gene.
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