Abstract
Breast cancer progression is associated with dysregulated expression of the immunoglobulin superfamily (IgSF) genes that are involved in cell-cell recognition, binding and adhesion. Despite widespread evidence that many IgSF genes could serve as effective biomarkers, this potential has not been realized because the studies have focused mostly on individual genes and not the entire network. To gain a global perspective of the IgSF-related biomarkers, we constructed an IgSF-directed neighbor network (IDNN) and an IgSF-directed driver network (IDDN) by integrating multiple levels of data, including IgSF genes, breast cancer driver genes, protein-protein interaction (PPI) networks and gene expression profiling data. Our study shows that IgSF genes in the PPI network have important topological features related to cancer. Most IgSF genes are either cancer driver genes themselves or associated with them. We also identified a 21-gene IgSF network module with enriched mutations that are associated with overall survival based on 450 breast cancer patient samples extracted from The Cancer Genome Atlas (TCGA) and multiple independent microarray validation datasets. These results highlight the potential of IgSF genes as novel diagnostic, prognostic and therapeutic targets for breast cancer.
Highlights
Breast cancer is the leading cause of cancer death among women worldwide
We identified a 21-gene immunoglobulin superfamily (IgSF) network module with enriched mutations that are associated with overall survival based on 450 breast cancer patient samples extracted from The Cancer Genome Atlas (TCGA) and multiple independent microarray validation datasets
We found a greater degree of genes that were both IgSF and breast cancer driver genes in the IgSF-directed neighbor network (IDNN) suggesting a intricate link between the IgSF genes and breast cancer (Figure 1C, Supplementary Figure S1)
Summary
Breast cancer is the leading cause of cancer death among women worldwide. In Chinese women, breast cancer is the most prevalent form of cancer with more than 1.6 million people diagnosed and 1.2 million people dying every year. The most common type of breast cancer is invasive ductal carcinoma (IDC) that can spread from the ducts or the lobules to the surrounding tissue. Studies have shown that the genetic diversity in breast cancer impacts response to treatment and patient outcomes. This is exemplified by the estrogen receptor negative (ER−) and positive (ER+) subtypes that have different prognostic gene signatures and responses to treatment [4]. There is scope to identify novel signatures that can enhance predicting the prognostic and clinical behavior
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