The Breast Cancer Protein Co-Expression Landscape

Abstract

Breast cancer is a complex phenotype (or better yet, several complex phenotypes) characterized by the interplay of a large number of cellular and biomolecular entities. Biological networks have been successfully used to capture some of the heterogeneity of intricate pathophenotypes, including cancer. Gene coexpression networks, in particular, have been used to study large-scale regulatory patterns. Ultimately, biological processes are carried out by proteins and their complexes. However, to date, most of the tumor profiling research has focused on the genomic and transcriptomic information. Here, we tried to expand this profiling through the analysis of open proteomic data via mutual information co-expression networks’ analysis. We could observe that there are distinctive biological processes associated with communities of these networks and how some transcriptional co-expression phenomena are lost at the protein level. These kinds of data and network analyses are a broad resource to explore cellular behavior and cancer research.

The network structure of hematopoietic cancers

“…Complementary to the last, a recent study on the protein co-expression landscape of breast cancer, showed that the loss of long distance co-expression is not observed at the proteome level. The most relevant protein co-expression interactions occur between members of the same protein family or between proteins with similar functions (42). So far, the loss of long-range co-expression has only been seen at the transcriptomic level of regulation.…”

Passive Microwave Radiometry and microRNA Detection for Breast Cancer Diagnostics

“…Ultimately, biological processes are carried out by proteins and their complexes. Therefore, as demonstrated in a recent study [28], the profiling of breast cancers can be extended by analyzing open proteomic data along with gene expression.…”

DataSheet_3.pdf

“…Our aim is to generate plausible findings that highlight semi-mechanistic processes, facilitating experimental validation to advance our comprehension of the phenomena and assess the reliability of our computational methods. In the past, through systems biologyoriented analyses, some of these findings were found by our group and others (84,91,(94)(95)(96)(97)(98). However, the ultimate benchmark in natural sciences remains experimental validation, reproducibility, and, to some extent, generalizability.…”

“…The integration of multi-omics approaches, including DNA methylation assays (84), copy number variants (CNVs) (85,86), miRNA expression profiling (87-89), transcription factor binding site analysis (90), and proteomics (91), can further improve our understanding of gene regulation in breast cancer tumors (84,92). These comprehensive techniques offer unprecedented insights into the intricate molecular mechanisms underlying tumorigenesis and progression.…”