Original Research
Analysis of genomics and immune infiltration patterns of epithelial-mesenchymal transition related to metastatic breast cancer to bone

https://doi.org/10.1016/j.tranon.2020.100993Get rights and content
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Highlights

  • Differential expression analysis showed a total of 304 differentially expressed genes, which were mainly related to proteoglycans in cancer, the PI3K/Akt signaling pathway, and the TGF-beta signaling pathway.

  • The survival-related linear risk assessment model consisting of eight genes (FERMT2, ITGA5, ITGB1, MCAM, CEMIP, HGF, TGFBR1, F2RL2) was constructed. The survival rates of high-risk patients were significantly lower than that of the low-risk group, and the 3-, 5-, and 10-year AUCs were satisfactory.

  • BMP2, BMPR2, and GREM1 were differentially expressed in both data sets of breast cancer bone metastasis.

  • In GSE20685 and GSE45255, significant differences in immune infiltration patterns were found between high- and low-risk groups.

  • BMP-2 may regulate the immune infiltration process in breast cancer tissues through the PI3K/Akt signaling pathway.

Abstract

Objective

This study aimed to design a weighted co-expression network and a breast cancer (BC) prognosis evaluation system using a specific whole-genome expression profile combined with epithelial-mesenchymal transition (EMT)-related genes; thus, providing the basis and reference for assessing the prognosis risk of spreading of metastatic breast cancer (MBC) to the bone.

Methods

Four gene expression datasets of a large number of samples from GEO were downloaded and combined with the dbEMT database to screen out EMT differentially expressed genes (DEGs). Using the GSE20685 dataset as a training set, we designed a weighted co-expression network for EMT DEGs, and the hub genes most relevant to metastasis were selected. We chose eight hub genes to build prognostic assessment models to estimate the 3-, 5-, and 10-year survival rates. We evaluated the models’ independent predictive abilities using univariable and multivariable Cox regression analyses. Two GEO datasets related to bone metastases from BC were downloaded and used to perform differential genetic analysis. We used CIBERSORT to distinguish 22 immune cell types based on tumor transcripts.

Results

Differential expression analysis showed a total of 304 DEGs, which were mainly related to proteoglycans in cancer, and the PI3K/Akt and the TGF-β signaling pathways, as well as mesenchyme development, focal adhesion, and cytokine binding functionally. The 50 hub genes were selected, and a survival-related linear risk assessment model consisting of eight genes (FERMT2, ITGA5, ITGB1, MCAM, CEMIP, HGF, TGFBR1, F2RL2) was constructed. The survival rate of patients in the high-risk group (HRG) was substantially lower than that of the low-risk group (LRG), and the 3-, 5-, and 10-year AUCs were 0.68, 0.687, and 0.672, respectively. In addition, we explored the DEGs of BC bone metastasis, and BMP2, BMPR2, and GREM1 were differentially expressed in both data sets. In GSE20685, memory B cells, resting memory T cell CD4 cells, T regulatory cells (Tregs), γδ T cells, monocytes, M0 macrophages, M2 macrophages, resting dendritic cells (DCs), resting mast cells, and neutrophils exhibited substantially different distribution between HRG and LRG. In GSE45255, there was a considerable difference in abundance of activated NK cells, monocytes, M0 macrophages, M2 macrophages, resting DCs, and neutrophils in HRG and LRG.

Conclusions

Based on the weighted co-expression network for breast-cancer-metastasis-related DEGs, we screened hub genes to explore a prognostic model and the immune infiltration patterns of MBC. The results of this study provided a factual basis to bioinformatically explore the molecular mechanisms of the spread of MBC to the bone and the possibility of predicting the survival of patients.

Keywords

Breast cancer metastases
Bone metastases
Differential gene expression
WGCNA
Prognostic model
Immune infiltration pattern

Abbreviations

GEO
gene expression omnibus
EMT
epithelial-mesenchymal transition
WGCNA
weighted co-expression network analysis
ME
module eigengene
Mrna
messenger rna
TOM
topological overlap measure
DE
differentially expressed
GO
gene ontology
KEGG
kyoto encyclopedia of genes and genomes
PCC
Pearson correlation coefficient
ROC curve
receiver operating characteristic curve
AUC
Area under curve
IPA
Ingenuity Pathway Analysis
BMP
Bone morphogenetic protein
Runx2
runt related transcription factor 2
TNM
Tumor, Node, Metastases

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Shuzhong Liu and An Song contributed equally to this work.