Abstract
Background
Tumor stroma is a heterogeneous cellular component in the tumor microenvironment of
breast cancer. However, very few studies have explored the identification of breast
cancer subtypes based on highly heterogeneous tumor stromal signatures.
Materials and Methods
Using a combined dataset composed of 8 gene expression profiling datasets for breast
tumor stroma, we clustered breast cancers based on the expression levels of 100 genes
whose expression values were most variable across all samples. Furthermore, we investigated
the molecular features of the breast cancer subtypes identified.
Results
We identified 2 breast cancer subtypes, termed SBCS-1 and SBCS-2. We found that the
contents of stroma and immune cells were lower in SBCS-1 than in SBCS-2, while the
proportion of tumor cells was higher in SBCS-1. SBCS-1 was enriched in cancer-associated
pathways, including ribosomes, cell cycle, RNA degradation, RNA polymerase, DNA replication,
oxidative phosphorylation, proteasome, spliceosome, and glycolysis/gluconeogenesis.
SBCS-2 was enriched in pathways of graft versus host disease, type 1 diabetes mellitus,
intestinal immune network for IgA production, allograft rejection, and steroid hormone
biosynthesis. Moreover, many oncogenic biological processes were highly activated
in SBCS-1, including proliferation, stemness, epithelial-to-mesenchymal transition
(EMT), and angiogenesis. Gene co-expression network analysis identified prognostic
hub genes, transcription factor encoding genes (PFDN5 and EZH2), and protein kinase encoding gene (AURKA) in a gene module highly enriched in SBCS-1.
Conclusion
Based on the gene expression profiles in breast cancer stroma, breast cancer can be
divided into 2 subtypes, which have significantly different molecular, and clinical
characteristics. The identification of new subtypes of breast cancer has clinical
implications for the management of this disease.
Keywords
Abbreviations:
SBCS-1 (stromal breast cancer subtype 1), SBCS-2 (stromal breast cancer subtype 2), TME (tumor microenvironment), NCBI (National Center for Biotechnology Information), TCGA (The Cancer Genome Atlas), GDC (Genomic Data Commons), RSEM (RNA-Seq by expectation-maximization), ssGSEA (single-sample gene-set enrichment analysis), TILs (tumor-infiltrating lymphocytes), GSEA (gene set enrichment analysis), EMT (epithelial-mesenchymal transition), WGCNA (weighted correlation network analysis), KEGG (Kyoto Encyclopedia of Genes and Genomes), TF (transcription factor), RFS (recurrence-free survival)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: April 04, 2022
Accepted:
April 1,
2022
Received in revised form:
March 21,
2022
Received:
October 27,
2021
Identification
Copyright
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