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Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
The Role of Transcription Factors in the Loss of Inter-Chromosomal Co-Expression for Breast Cancer Subtypes
Rodrigo Trujillo-Ortíz1, Jesús Espinal-Enríquez1,2, Enrique Hernández-Lemus1,2
1Computational Genomics Division, Instituto Nacional de Medicina Genómica, Mexico City 14610, Mexico.
Abstract:
Breast cancer encompasses a diverse array of subtypes, each exhibiting distinct clinical characteristics and treatment responses. Unraveling the underlying regulatory mechanisms that govern gene expression patterns in these subtypes is essential for advancing our understanding of breast cancer biology. Gene co-expression networks (GCNs) help us identify groups of genes that work in coordination. Previous research has revealed a marked reduction in the interaction of genes located on different chromosomes within GCNs for breast cancer, as well as for lung, kidney, and hematopoietic cancers. However, the reasons behind why genes on the same chromosome often co-express remain unclear. In this study, we investigate the role of transcription factors in shaping gene co-expression networks within the four main breast cancer subtypes: Luminal A, Luminal B, HER2+, and Basal, along with normal breast tissue. We identify communities within each GCN and calculate the transcription factors that may regulate these communities, comparing the results across different phenotypes. Our findings indicate that, in general, regulatory behavior is to a large extent similar among breast cancer molecular subtypes and even in healthy networks. This suggests that transcription factor motif usage does not fully determine long-range co-expression patterns. Specific transcription factor motifs, such as CCGGAAG, appear frequently across all phenotypes, even involving multiple highly connected transcription factors. Additionally, certain transcription factors exhibit unique actions in specific subtypes but with limited influence. Our research demonstrates that the loss of inter-chromosomal co-expression is not solely attributable to transcription factor regulation. Although the exact mechanism responsible for this phenomenon remains elusive, this work contributes to a better understanding of gene expression regulatory programs in breast cancer.
Insights
Investigating gene co-expression networks in breast cancer subtypes reveals transcription factor regulation is similar across phenotypes. This suggests transcription factors do not fully explain reduced inter-chromosomal gene interactions in cancer.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Breast cancer comprises diverse subtypes with varying clinical behaviors.
- Gene co-expression networks (GCNs) reveal coordinated gene activity.
- Reduced inter-chromosomal gene interactions are observed in breast cancer GCNs.
Purpose of the Study:
- To investigate the role of transcription factors in shaping GCNs across breast cancer subtypes and normal tissue.
- To compare transcription factor regulation patterns among Luminal A, Luminal B, HER2+, and Basal subtypes.
Main Methods:
- Construction of GCNs for each breast cancer subtype and normal breast tissue.
- Identification of communities within GCNs.
- Calculation and comparison of transcription factor motifs regulating these communities.
Main Results:
- Transcription factor regulation patterns are largely similar across breast cancer subtypes and normal tissue.
- Specific transcription factor motifs (e.g., CCGGAAG) are prevalent across all phenotypes.
- Transcription factors have limited unique influence on specific subtypes, and do not fully explain reduced inter-chromosomal co-expression.
Conclusions:
- Transcription factor motif usage does not solely determine long-range co-expression patterns in breast cancer.
- The loss of inter-chromosomal co-expression in breast cancer is not fully explained by transcription factor regulation.
- Further research is needed to elucidate the mechanisms behind altered inter-chromosomal interactions.
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