Background: Breast cancer is a complex disease with high prevalence in women, the
molecular mechanisms of which are still unclear at present. Most transcriptomic studies on breast
cancer focus on differential expression of each gene between tumor and the adjacent normal tissues,
while the other perturbations induced by breast cancer including the gene regulation variations, the
changes of gene modules and the pathways, which might be critical to the diagnosis, treatment and
prognosis of breast cancer are more or less ignored.
Objective: We presented a complete process to study breast cancer from multiple perspectives,
including differential expression analysis, constructing gene co-expression networks, modular
differential connectivity analysis, differential gene connectivity analysis, gene function enrichment
analysis key driver analysis. In addition, we prioritized the related anti-cancer drugs based on
enrichment analysis between differential expression genes and drug perturbation signatures.
Methods: The RNA expression profiles of 1109 breast cancer tissue and 113 non-tumor tissues were
downloaded from The Cancer Genome Atlas (TCGA) database. Differential expression of RNAs
was identified using the “DESeq2” bioconductor package in R, and gene co-expression networks
were constructed using the weighted gene co-expression network analysis (WGCNA). To compare
the module changes and gene co-expression variations between tumor and the adjacent normal
tissues, modular differential connectivity (MDC) analysis and differential gene connectivity analysis
(DGCA) were performed.
Results: Top differential genes like MMP11 and COL10A1 were known to be associated with breast
cancer. And we found 23 modules in the tumor network had significantly different co-expression
patterns. The top differential modules were enriched in Goterms related to breast cancer like MHC
protein complex, leukocyte activation, regulation of defense response and so on. In addition, key
genes like UBE2T driving the top differential modules were significantly correlated with the
patients’ survival. Finally, we predicted some potential breast cancer drugs, such as Eribulin,
Taxane, Cisplatin and Oxaliplatin.
Conclusion: As an indication, this framework might be useful in understanding the molecular
pathogenesis of diseases like breast cancer and inferring useful drugs for personalized medication.