Abstract
Breast cancer (BCa) is the most common malignancy in women and claudin-low breast cancer (CL-BCa) is a newly identified BCa subtype characterized by low expression of claudin 3&4&7. However, the hub genes associated with the recruitment of immune cells into CL-BCa were rarely described. This study aimed at exploring the differentially expressed hub genes associated with tumor-infiltrating immune cells in CL-BCa by a multi-approach bioinformatics analysis. The top 200 genes associated with CL-BCa were screened in the METABRIC dataset; the PPI network was constructed using STRING and Cytoscape; tumor-infiltrating immune cells were analyzed by TIMER 2.0; and the correlation of feature cytokines and claudins on survival was examined in METABRIC and TCGA datasets. Consequently, we found that the fraction of tumor-infiltrating immune cells, especially CD8+T cells and macrophages, increased in the CL-BCa. Differentially expressed cytokines (CCL5, CCL19, CXCL9 and CXCL10) and claudins (CLDN8, CLDN11 and CLDN19) were related to the overall survival, and their expression levels were also examined both in tumor tissues of CL-BCa patients by IHC and in typical CL-BCa cell lines by qPCR. Finally, the BCa patients with high expression of these DEGs (CCL5, CCL19, CXCL9, CLDN8 and CLDN11) showed a better overall survival. This study sheds light on molecular features of CL-BCa on immune microenvironments and contributes to identification of prognosis biomarkers for the CL-BCa patients.
Highlights
Breast cancer (BCa) is the most common malignancy worldwide in women with 2 million diagnosed cases and over 627,000 death cases in 2018 [1]
Hub genes of the brown module tend to be highly correlated with weight, which suggested that these hub genes might be the featured genes of claudin-low subtype (Figure 1C)
We found that the hub genes in this module were CXC-chemokines ligand 9 (CXCL9), CXCL10, CCL5, TLR8, CD8A and EOMES, which were relevant to the immune pathway and were detected by CytoHubba (Figure 1E)
Summary
Breast cancer (BCa) is the most common malignancy worldwide in women with 2 million diagnosed cases and over 627,000 death cases in 2018 [1]. Breast cancer was classified into different subtypes based on hierarchical clustering of gene expression [2]. Owing to the limitations of hierarchical clustering for the classification of individual samples, prediction analysis of microarray 50 (PAM50) was developed to identify intrinsic subtypes. Among these subtypes, four subtypes (namely, luminal A, luminal B, HER2+ and basal) exhibited unique patterns of gene. Adequate tissue samples for microarray gene expression profiling (GEP) were required for these taxonomies [4, 5]. This limitation led to the development of immunohistochemical (IHC) surrogate definitions for identifying the molecular subtypes of breast cancer [6]. It was necessary to portray BCa subtypes for enhancing the accuracy of clinical diagnosis by identifying their potential gene markers using bioinformatics approaches
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
