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. 2021 Feb 3:12:610087.
doi: 10.3389/fgene.2021.610087. eCollection 2021.

Stratification of Estrogen Receptor-Negative Breast Cancer Patients by Integrating the Somatic Mutations and Transcriptomic Data

Jie Hou  1 , Xiufen Ye  1 , Yixing Wang  1 , Chuanlong Li  1
Affiliations

Stratification of Estrogen Receptor-Negative Breast Cancer Patients by Integrating the Somatic Mutations and Transcriptomic Data

Jie Hou et al. Front Genet. .

Abstract

Patients with estrogen receptor-negative breast cancer generally have a worse prognosis than estrogen receptor-positive patients. Nevertheless, a significant proportion of the estrogen receptor-negative cases have favorable outcomes. Identifying patients with a good prognosis, however, remains difficult, as recent studies are quite limited. The identification of molecular biomarkers is needed to better stratify patients. The significantly mutated genes may be potentially used as biomarkers to identify the subtype and to predict outcomes. To identify the biomarkers of receptor-negative breast cancer among the significantly mutated genes, we developed a workflow to screen significantly mutated genes associated with the estrogen receptor in breast cancer by a gene coexpression module. The similarity matrix was calculated with distance correlation to obtain gene modules through a weighted gene coexpression network analysis. The modules highly associated with the estrogen receptor, called important modules, were enriched for breast cancer-related pathways or disease. To screen significantly mutated genes, a new gene list was obtained through the overlap of the important module genes and the significantly mutated genes. The genes on this list can be used as biomarkers to predict survival of estrogen receptor-negative breast cancer patients. Furthermore, we selected six hub significantly mutated genes in the gene list which were also able to separate these patients. Our method provides a new and alternative method for integrating somatic gene mutations and expression data for patient stratification of estrogen receptor-negative breast cancers.

Keywords: breast cancer patient stratification; distance correlation; estrogen receptor-negative; gene coexpression network; significantly mutated gene.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Workflow of identifying new biomarkers using transcriptomic and variants data.
Figure 2
Figure 2
Kaplan-Meier survival curves of ER− and ER+. The ER− breast cancer patient have a poor prognosis in the short term and a relatively good prognosis in the longer term.
Figure 3
Figure 3
Identification of modules associated with the ER_Status of breast cancer. (A) The scale-free fit index for various soft-thresholding power. Scale-free topology (SFT) was achieved when the recommended soft-thresholding power was 3. (B) The mean connectivity for various soft-thresholding powers. (C) The cluster dendrogram of module eigengenes. (D) The cluster dendrogram of all genes with corresponding color assignments. Nine colors present nine modules. (E) Module-ER_Status relationship heatmap. The values above the brackets represent the correlation coefficients between modules and ER_Status. The values in brackets are the P-values for the association test. The red and yellow modules were significantly related to the ER_Status and selected as the important modules.
Figure 4
Figure 4
Kaplan-Meier survival curves. The 227 important-SMGs were able to separate the patients into two groups more significantly. The P-values were smaller in METABRIC dataset. (A) Group 1 in TCGA, (B) important-SMGs in TCGA, (C) group 1 METABRIC, (D) important-SMGs in METABRIC.
Figure 5
Figure 5
Kaplan-Meier survival curves using the six hub SMGs. A few hub SMGs can represent the 227 important-SMGs and were able to separate the patients into two groups more significantly. (A) Six hub SMGs in TCGA. (B) Six hub SMGs in METABRIC.
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