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Identification of Breast Cancer Subtypes Based on Gene Expression Profiles in Breast Cancer Stroma

  • Md. Nazim Uddin
    Affiliations
    Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China

    Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China

    Big Data Research Institute, China Pharmaceutical University, Nanjing, China

    Institute of Food Science and Technology, Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhaka, Bangladesh
    Search for articles by this author
  • Xiaosheng Wang
    Correspondence
    Address for correspondence: Xiaosheng Wang, MD, PhD, Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
    Affiliations
    Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China

    Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China

    Big Data Research Institute, China Pharmaceutical University, Nanjing, China
    Search for articles by this author
Published:April 04, 2022DOI:https://doi.org/10.1016/j.clbc.2022.04.001

      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)
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