Surgery is the most effective treatment for breast cancer patients. However, some patients developed recurrence and distant metastasis after surgery. Adjuvant therapy is considered for high-risk patients depending on several prognostic markers, and lymphovascular invasion has become one of such prognostic markers that help physicians to identify the risk for distant metastasis and recurrence. However, the mechanism of lymphovascular invasion in breast cancer remains unknown. This study aims to unveil the genes and pathways that may involve in lymphovascular invasion in breast cancer. In total, 108 breast cancer samples were collected during surgery and microarray analysis was performed. Significance analysis of the microarrays and limma package for R were used to examine differentially expressed genes between lymphovascular invasion-positive and lymphovascular invasion-negative cases. Network and pathway analyses were mapped using the Ingenuity Pathway Analysis and the Database for Annotation, Visualization and Integrated Discovery. In total, 86 differentially expressed genes, including 37 downregulated genes and 49 upregulated genes were identified in lymphovascular invasion-positive patients. Among these genes, TNFSF11, IL6ST, and EPAS1 play important roles in cytokine-receptor interaction, which is the most enriched pathway related to lymphovascular invasion. Moreover, the results also suggested that an imbalance between extracellular matrix components and tumor micro-environment could induce lymphovascular invasion. Our study evaluated the underlying mechanisms of lymphovascular invasion, which may further help to assess the risk of breast cancer progression and identify potential targets of adjuvant treatment.
ASJC Scopus subject areas
- Cancer Research
Klahan, S., Wong, H. S. C., Tu, S. H., Chou, W. H., Zhang, Y. F., Ho, T. F., Liu, C. Y., Yih, S. Y., Lu, H. F., Chen, S. C. C., Huang, C. C., & Chang, W. C. (2017). Identification of genes and pathways related to lymphovascular invasion in breast cancer patients: A bioinformatics analysis of gene expression profiles. Tumor Biology, 39(6). https://doi.org/10.1177/1010428317705573