ArticlePeerJ2025
Exploration of prognostic genes associated with lymphangiogenesis in breast cancer based on transcriptomics and experimental verification.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
10 authors.
Funding
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Abstract
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.
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