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. 2023 Jan 5:13:1078609.
doi: 10.3389/fgene.2022.1078609. eCollection 2022.

Functional impact of multi-omic interactions in breast cancer subtypes

Soledad Ochoa  1   2 , Enrique Hernández-Lemus  1   3
Affiliations

Functional impact of multi-omic interactions in breast cancer subtypes

Soledad Ochoa et al. Front Genet. .

Abstract

Multi-omic approaches are expected to deliver a broader molecular view of cancer. However, the promised mechanistic explanations have not quite settled yet. Here, we propose a theoretical and computational analysis framework to semi-automatically produce network models of the regulatory constraints influencing a biological function. This way, we identified functions significantly enriched on the analyzed omics and described associated features, for each of the four breast cancer molecular subtypes. For instance, we identified functions sustaining over-representation of invasion-related processes in the basal subtype and DNA modification processes in the normal tissue. We found limited overlap on the omics-associated functions between subtypes; however, a startling feature intersection within subtype functions also emerged. The examples presented highlight new, potentially regulatory features, with sound biological reasons to expect a connection with the functions. Multi-omic regulatory networks thus constitute reliable models of the way omics are connected, demonstrating a capability for systematic generation of mechanistic hypothesis.

Keywords: DNA methylation; HIF; RAS; SOX9; WNT; breast cancer; multi-omics; network biology.

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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
Overview of the steps followed.
FIGURE 2
FIGURE 2
UpSet plot for (A) biological processes and (B) KEGG pathways enrichment.
FIGURE 3
FIGURE 3
Enriched functions. (A) Feature similarity between functions shared by the pair of datasets indicated. Functions with similarity over 0.5 are displayed. (B) Bias of exclusive functions. An asterisk marks categories with significant over-representation (Fisher’s test, Bonferroni adjusted p-value < 0.05).
FIGURE 4
FIGURE 4
Functions enriched in Her2 SGCCA components. Both KEGG pathways and GO biological processes are represented together. Same-color clustered nodes are enriched in the same components. Nodes in gray do not belong to any cluster. The size of nodes and labels reflect the number of features behind the enrichment. Functions exclusively found in this subtype are highlighted with a red border.
FIGURE 5
FIGURE 5
Features connected with HIF-1 signaling in the basal subtype. Circles represent CpGs, and squares are transcripts. When possible, CpGs are identified with the symbol of the gene they affect; otherwise, the ID of the probe is used. The shades of red indicate the level of overexpression/methylation against the normal tissue, while blue tones represent values under what is expected. The node size reflects its degree. A purple border identifies nodes whose protein plays a transcription factor role. The weight of the link is the extent of mutual information between connected nodes. Dashed edges link MI components with prior information.
FIGURE 6
FIGURE 6
Features connected with the regulation of stem cell differentiation in the Her2-enriched subtype. Node size reflects the betweenness centrality.
FIGURE 7
FIGURE 7
Example networks for the luminal subtypes. (A) Features connected with the Ras signaling pathway in luminal A data. Node size indicates degree. (B) Features connected with Wnt signaling in the luminal B subtype. Node size reflects betweenness centrality.
FIGURE 8
FIGURE 8
Features connected with DNA methylation in the normal adjacent tissue. Color corresponds to log fold changes in the basal subtype.
See this image and copyright information in PMC

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