Identify Breast Cancer Subtypes by Gene Expression Profiles

AbstractFor breast cancer, clinically important subtypes are well characterised at the molecular level in terms of gene expression profiles. In addition, signaling pathways in breast cancer have been extensively studied as therapeutic targets due to their roles in tumor growth and metastasis. However, it is challenging to put signaling pathways and gene expression profiles together to characterise biological mechanisms of breast cancer subtypes since many signaling events result from post-translational modifications, rather than gene expression differences.We present a logic-based approach to explain the differences in gene expression profiles among breast cancer subtypes using Pathway Logic and transcriptional network information. Pathway Logic is a rewriting-logic-based formal system for modeling biological pathways including post-translational modifications. Proposed method demonstrated its utility by constructing subtype-specific path from key receptors (TNFR, TGFBR1 and EGFR) to key transcription factor (TF) regulators (RELA, ATF2, SMAD3 and ELK1) and identifying potential pathway crosstalk via TFs in basal-specific paths, which could provide a novel insight on aggressive breast cancer subtypes.AvailabilityAnalysis result is available at http://epigenomics.snu.ac.kr/PL/

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Sample-specific perturbation of gene interactions identifies breast cancer subtypes

Briefings in Bioinformatics ◽

10.1093/bib/bbaa268 ◽

2020 ◽

Author(s):

Yuanyuan Chen ◽

Yu Gu ◽

Zixi Hu ◽

Xiao Sun

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Biological Networks ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Gene Interaction ◽

Breast Cancer Subtypes ◽

Gene Interaction Network ◽

Cancer Subtypes

Abstract Breast cancer is a highly heterogeneous disease, and there are many forms of categorization for breast cancer based on gene expression profiles. Gene expression profiles are variables and may show differences if measured at different time points or under different conditions. In contrast, biological networks are relatively stable over time and under different conditions. In this study, we used a gene interaction network from a new point of view to explore the subtypes of breast cancer based on individual-specific edge perturbations measured by relative gene expression value. Our study reveals that there are four breast cancer subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of breast cancer show strong heterogeneity in prognosis, somatic mutations, phenotypic changes and enriched pathways. The network-based subtypes are closely related to the PAM50 subtypes and immunohistochemistry index. This work helps us to better understand the heterogeneity and mechanisms of breast cancer from a network perspective.

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Discovering lncRNA Mediated Sponge Interactions in Breast Cancer Molecular Subtypes

10.1101/209015 ◽

2017 ◽

Author(s):

Gulden Olgun ◽

Ozgur Sahin ◽

Oznur Tastan

Keyword(s):

Breast Cancer ◽

Hox Genes ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Functional Enrichment ◽

Breast Cancer Subtypes ◽

Integrative Approach ◽

Cancer Subtypes ◽

Significant Difference

AbstractMotivationLong non-coding RNAs(lncRNAs) can indirectly regulate mRNAs expression levels by sequestering microRNAs (miRNAs), and act as competing endogenous RNAs (ceRNAs) or as sponges. Previous studies identified lncRNA-mediated sponge interactions in various cancers including the breast cancer. However, breast cancer subtypes are quite distinct in terms of their molecular profiles; therefore, ceRNAs are expected to be subtype-specific as well.ResultsTo find lncRNA-mediated ceRNA interactions in breast cancer subtypes, we develop an integrative approach. We conduct partial correlation analysis and kernel independence tests on patient gene expression profiles and further refine the candidate interactions with miRNA target information. We find that although there are sponges common to multiple subtypes, there are also distinct subtype-specific interactions. Functional enrichment of mRNAs that participate in these interactions highlights distinct biological processes for different subtypes. Interestingly, some of the ceRNAs also reside in close proximity in the genome; for example, those involving HOX genes, HOTAIR, miR-196a-1 and miR-196a-2. We also discover subtype-specific sponge interactions with high prognostic potential. For instance, when grouping is based on the expression patterns of specific sponge interactions, patients differ significantly in their survival distributions. If on the other hand, patients are grouped based on the individual RNA expression profiles of the sponge participants, they do not exhibit a significant difference in survival. These results can help shed light on subtype-specific mechanisms of breast cancer, and the methodology developed herein can help uncover sponges in other diseases.

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