Background:
Breast cancer is the most common cancer in women across the world, with high incidence and
mortality rates. Being a heterogeneous disease, gene expression profiling based analysis plays a significant role in
understanding breast cancer. Since expression patterns of patients belonging to the same stage of breast cancer vary
considerably, an integrated stage-wise analysis involving multiple samples is expected to give more comprehensive results
and understanding of breast cancer.
Objective:
The objective of this study is to detect functionally significant modules
from gene co-expression network of cancerous tissues and to extract prognostic genes related to multiple stages of breast
cancer.
Methods:
To achieve this, a multiplex framework is modelled to map the multiple stages of breast cancer, which
is followed by a modularity optimization method to identify functional modules from it. These functional modules are
found to enrich many Gene Ontology terms significantly that are associated with cancer.
Result and Discussion:
predictive biomarkers are identified based on differential expression analysis of multiple stages of breast cancer.
Conclusion:
Our analysis identified 13 stage-I specific genes, 12 stage-II specific genes, and 42 stage-III specific genes
that are significantly regulated and could be promising targets of breast cancer therapy. That apart, we could identify 29,
18 and 26 lncRNAs specific to stage I, stage II and stage III respectively.
Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data