Related Experiment Video
Updated: Feb 18, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identification of key gene modules and pathways of human breast cancer by co-expression analysis
Qingnan Zhao1, Wenqing Song2, Dai Yu He2
1Department of Breast Surgeon, China-Japan Union Hospital of JILIN University, Chang Chun, 130033, Jilin Province, China.
Background:
Breast cancer is the most common and aggressive tumor causing injury to women world wide. Although gene expression analysis had been performed previously, systemic co-expression analysis for this cancer is still lacking to date. We attempted to identify the critical modules of breast cancer.
Methods:
Co-expression modules were established with the help of WGCNA and the interactions among them were performed by R language. Biological process and pathways analysis of co-expression genes were figured out by GO and KEGG functional enrichment analysis using DAVID dataset.
Results:
In this study, expression data of 4,000 genes from 136 samples with breast cancer was used for the establishment of co-expression modules. And nine modules were identified. There was much higher scale independence among different modules by interactions analysis. Moreover, there was an obvious difference in adjacency degree among different modules. The most enriched pathways as immune response and ubiquitin-mediated proteolysis were identified as the most critical modules of breast cancer by GO and KEGG enrichment analysis.
Conclusion:
Our result demonstrated that immune response and ubiquitin-mediated proteolysis could serve as prognostic and predictive markers for the occurrence of breast cancer, providing evidence for further analysis in the prognosis and treatment of breast cancer.
