An integrative multi-omics network-based approach identifies key regulators for breast cancer

Abstract

Although genome-wide association studies (GWASs) have successfully identified thousands of risk variants for human complex diseases, understanding the biological function and molecular mechanisms of the associated SNPs involved in complex diseases is challenging. Here we developed a framework named integrative multi-omics network-based approach (IMNA), aiming to identify potential key genes in regulatory networks by integrating molecular interactions across multiple biological scales, including GWAS signals, gene expression-based signatures, chromatin interactions and protein interactions from the network topology. We applied this approach to breast cancer, and prioritized key genes involved in regulatory networks. We also developed an abnormal gene expression score (AGES) signature based on the gene expression deviation of the top 20 rank-ordered genes in breast cancer. The AGES values are associated with genetic variants, tumor properties and patient survival outcomes. Among the top 20 genes, RNASEH2A was identified as a new candidate gene for breast cancer. Thus, our integrative network-based approach provides a genetic-driven framework to unveil tissue-specific interactions from multiple biological scales and reveal potential key regulatory genes for breast cancer. This approach can also be applied in other complex diseases such as ovarian cancer to unravel underlying mechanisms and help for developing therapeutic targets.

Keywords: Breast cancer; GWASs; Integrative network-based approach; Multi-omics; Regulatory network.

Graphical abstract

Fig. 1

An overview of the integrative genomics network-based approach. a) Extraction of SNPs from GWASs and annotation SNPs by multiple tissue-specific functional omics datasets. b) Construction of SNP-gene bipartite with SNP-gene mapping pairs and calculation of degree centrality for each gene node based on the network topology. c) Construction of functional interaction network. Gene nodes in the networks are extracted from the bipartite. Key genes in weighted molecular networks are captured by using enrichment analysis. d) Identification of key genes in the network. Scores of each gene from multiple biological networks are combined to get composite scores. The importance of genes is evaluated by the order of their composite scores.

Fig. 2

The top 20 rank-ordered genes are correlated with breast cancer, disease pathogenesis and genetic variants. The top 20 genes are differentially expressed between breast cancer samples and normal samples. The top 20 gene expression levels in a) various datasets. b) breast ductal carcinoma in situ (DCIS) and invasive ductal carcinomas (IDCs) (GSE21422). Samples were clustered by gene expression levels of the 20 genes by centroid. c) Genetic alternation frequencies of the top 20 genes in TCGA-BRCA patient samples.

Fig. 3

The relevance of the AGES values to tumorigenesis, clinical characteristics and clinical survival. a) AGES values are significantly positively correlated with linear copy-number alterations (CNA). b,c) The AGES values are associated with tumor clinical characteristics. Significant P-values are determined by Chi-squared test (P < 0.01). b) ER-negative. c) Triple-negative. d,e,f) Kaplan-Meier survival curves showed that the AGES are predictive of survival outcomes for breast cancer. d) disease-free survival in TCGA-BRCA dataset. e) distant metastasis-free survival in GSE11121. f) distant metastasis-free survival in GSE20685.

Fig. 4

The expression level of RNASEH2A related to patient outcome in breast cancer. a) Box plots show increased expression of RNASEH2A in breast tumor samples in 7 independent studies. b, c) Kaplan-Meier survival analyses show the differences in overall survival (OS) and relapse-free survival (RFS) between breast cancer patients with high or low RNASEH2A mRNA levels. P-values are calculated by using the log-rank analysis.

Supplementary Fig. 1

KEGG pathway enrichment analysis for breast cancer GWAS signal-driven genes.

Supplementary Fig. 2

The similarity between the pairwise comparisons of the robustness tests. a) Top 20 genes. c) Top 100 genes.

Supplementary Fig. 3

Co-expression between RNASEH2A and 8 cell proliferation markers.

Supplementary Fig. 4

KEGG pathway enrichment analysis for ovarian cancer identified genes. a) GWAS signal-driven genes in the bipartite network. b) Top 100 genes.