Related Experiment Video For Breast cancer
Updated: Sep 9, 2025
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Exploration of prognostic genes associated with lymphangiogenesis in breast cancer based on transcriptomics and
Chen Liu1, Tuo Zhang2, Fushen Luo2
1Department of Clinical Laboratory, The Third Affiliated Hospital of Qiqihar Medical University, Qiqihar, Heilongjiang, China.
Background:
Breast cancer (BC), a malignant neoplasm resulting from the uncontrolled proliferation of mammary epithelial cells, is predominantly driven by pathogenic breast cancer gene (BRCA) 1/2 mutations in hereditary cases. Previous studies have implicated lymphangiogenesis in the progression of BC. This research aimed to identify prognostic genes associated with lymphangiogenesis in BC and explore their underlying biological mechanisms.
Methods:
Publicly available datasets were utilized to identify differentially expressed genes (DEGs). Lymphangiogenesis-related genes (LRGs) were sourced from public databases, and candidate genes were determined through the intersection of DEGs and LRGs. Univariate Cox regression analysis and machine learning algorithms were employed to select prognostic genes and develop a prognostic model. Further analyses, including a nomogram, Gene Set Enrichment Analysis (GSEA), immune cell infiltration analysis, and drug sensitivity predictions, were conducted based on the identified prognostic genes. Finally, reverse transcription quantitative polymerase chain reaction (PCR) (RT-qPCR) was performed to evaluate the expression levels of these genes.
Results:
By intersecting 9,577 DEGs with 179 LRGs, 109 candidate genes were identified. Ultimately, four prognostic genes-ZIC2, CD24, CEBPD, and CCL19-were selected, and a prognostic model was established. The model demonstrated robust performance upon evaluation and validation, with the nomogram confirming its strong predictive ability. Notably, the prognostic genes were found to influence pathways such as the cell cycle and EGFR ligands, as well as immune cells like activated CD4 T cells. Additionally, drugs like AUY922 and AZ628 showed considerable potential in treating BC. RT-qPCR results for these four genes in clinical samples aligned with the bioinformatics findings.
Conclusion:
This study identified and validated four prognostic genes-ZIC2, CD24, CEBPD, and CCL19-that are associated with BC and may provide novel targets for diagnostic and therapeutic strategies.
