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. 2021 Mar;10(3):924-942.
doi: 10.21037/gs-20-767.

Identification of markers associated with brain metastasis from breast cancer through bioinformatics analysis and verification in clinical samples

Yongchang Gao  1 , Jianjing Liu  2 , Xiaolong Qian  3 , Xianghui He  1
Affiliations

Identification of markers associated with brain metastasis from breast cancer through bioinformatics analysis and verification in clinical samples

Yongchang Gao et al. Gland Surg. 2021 Mar.

Abstract

Background: Brain metastasis from breast cancer (BC) is an important cause of BC-related death. The present study aimed to identify markers of brain metastasis from BC.

Methods: Datasets were downloaded from the public databases Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) was performed to identify metastasis-associated genes (MAGs). Least absolute shrinkage and selection operator (LASSO) Cox proportional hazards regression models were constructed for screening key MAGs. Survival analysis and receiver operating characteristic (ROC) curves were used for evaluating the prognostic value. The factors associated with tumor metastasis were integrated to create a nomogram of TCGA data using R software. Gene Set Enrichment Analyses (GSEA) was performed for detecting the potential mechanisms of identified MAGs. Immunohistochemistry (IHC) was used to verify the expression of the key genes in clinical samples.

Results: The genes in 2 modules were identified to be significantly associated with metastasis through WGCNA. LASSO Cox proportional hazards regression models were constructed successfully. Subsequently, a clinical prediction model was constructed, and a nomogram was mapped, which had better sensitivity and specificity for BC metastasis. Two key genes, discs large homolog 3 (DLG3) and growth factor independence 1 (GFI1), were highly expressed in clinical samples, and the expression of these 2 genes was associated with patients' survival time.

Conclusions: We successfully constructed a clinical prediction model for brain metastasis from BC, and identified that the expression of DLG3 and GFI1 were strongly associated with brain metastasis from BC.

Keywords: Brain metastasis from breast cancer (BMBC); discs large homolog 3 (DLG3); growth factor independence 1 (GFI1); least absolute shrinkage and selection operator (LASSO); nomogram.

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/gs-20-767). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The gene expression profiling and clinical features were integrated as the input dataset. (A) Sample tree of the samples in the GSE14690 dataset. (B) Determination of soft-thresholding power in the WGCNA. WGCNA, weighted gene co-expression network analysis.
Figure 2
Figure 2
The modules of genes were merged in the gene cluster dendrogram and then analysis of relevance between key genes and clinical features was constructed. (A) The merged modules with the high similarity of feature genes in the gene cluster dendrogram. (B) A heat map created by analyzing the correlation between clinical information and key genes.
Figure 3
Figure 3
Distribution of LASSO coefficients and the MAGs were screened. (A) Distribution of LASSO coefficients for the selected genes in WGCNA. (B)The selection process of 10-fold cross-validation penalty parameter λ. LASSO, least absolute shrinkage and selection operator; MAGs, metastasis-associated genes; WGCNA, weighted gene co-expression network analysis.
Figure 4
Figure 4
The risk scores and the association between clinical characteristics and risk scores using the Kruskal-Wallis test in the TCGA-BRCA dataset. (A) The risk score and survival time of the samples in the TCGA-BRCA (breast invasive carcinoma in The Cancer Genome Atlas) dataset. (B) The correlation between risk score and clinical information.
Figure 5
Figure 5
Survival analysis and ROC curve was performed to evaluate the prognostic prediction effect of the identified MAGs when the test set, validation set, and total set were divided into high-risk groups and low-risk groups. (A) Survival analysis and ROC curve for the samples in the train set. (B) Survival analysis and ROC curve for the samples in the test set. (C) Survival analysis and ROC curve for the samples in total set. ROC, receiver operating characteristic; MAGs, metastasis-associated genes.
Figure 6
Figure 6
Univariate and multivariate Cox regression was performed based on MAGs and clinical information. (A) Multivariate Cox’s regression analysis of TCGA-BRCA data. (B) Univariate Cox regression analysis of TCGA-BRCA data. MAGs, metastasis-associated genes; TCGA, The Cancer Genome Atlas.
Figure 7
Figure 7
Establishment and evaluation of nomogram based on Cox regression model. (A) Nomogram of metastasis of breast cancer. (B) Survival analysis for the nomogram model and ROC curve for the nomogram model. (C) Calibration curve for 3-year and 5-year metastasis rate of breast cancer. ***, significant difference, P<0.001. ROC, receiver operating characteristic.
Figure 8
Figure 8
GSEA assay was performed in order to investigate the biological characteristics of the MAGs. (A) GSEA analysis for MAGs in the KEGG pathway. (B) GSEA analysis for MAGs in a biological processes. (C) GSEA analysis for MAGs in cell components. (D) GSEA analysis for MAGs in molecular function. MAGs, metastasis-associated genes; GSEA, Gene Set Enrichment Analyses; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 9
Figure 9
Increased DLG3 expression in primary breast cancer tissues predicts poor survival durations and highly RFS rate. (A) Expression analysis of DLG3 protein in adjacent normal breast tissues by immunohistochemistry (magnification, 400×). (B) Expression analysis of DLG3 protein in primary breast cancer tissues by immunohistochemistry (magnification, 400×). (C) Analysis of the DLG3 expression in the primary breast cancer tissues. (D) Association of DLG3 expression with OS rate in patients with breast cancer of brain metastases. The patients (n=103) were stratified into 2 groups in line with DLG3 immunohistochemical staining intensity. (E) Association of DLG3 expression with RFS rate with primary breast cancer to brain metastases patients. RFS, recurrence-free survival; OS, overall survival.
Figure 10
Figure 10
The correlation between GFI1 and DLG3 expression. (A) Immunohistochemical analysis results of correlative expression with DLG3 and GFI1in breast cancer with brain metastases surgical samples (magnification, 200×). (B) The real distribution of immunohistochemical staining scores between DLG3 and GFI1 expression in human primary breast cancer. (C) Statistical analysis of immunohistochemical results of DLG3 and GFI1 expression in human primary breast cancer surgical samples. P values were analyzed by the Chi-square test.
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