Comprehensive meta-analysis and differential network analysis of breast cancer

Backgrounds: Breast cancer is the most common cancer known among women and is theleading cause of cancer death in women worldwide. Various molecular markers were suggestedin numerous studies, but they were limited by one study and its experimental design. The aim ofthis project was to validate suggested prognostic and therapeutic markers using an integrativeanalytical approach in breast cancer.Materials and Methods: We performed meta-analysis of ۲۱ gene expression microarray studiesand differentially expressed genes (DEGs( were identified using LIMMA Package of R.Weighted gene co-expression network analysis (WGCNA) was used to construct free-scale geneco-expression networks to explore the associated modules and identify candidate biomarkers.Quantitative real-time PCR (qPCR) was performed to evaluate the expression of hub genes intumor and healthy breast tissues in Iranian women.Results: The results demonstrated that the blue and tan modules have the lowest modulepreservation between tumor and healthy networks. The hub genes of above mentioned moduleswere identified based on intramodular connectivity. A total of ۱۹۱۱ differentially expressedgenes (DEGs) were screened out which among these DEGs, the ۳۳ genes were common with hubgenes. The FOXA۱ and ERBB۲ genes with the most intramodular connectivity were selected forexperimental validation. Two above mentioned genes were found to be significantly higherexpressed in tumor samples compared to paired normal tissues.Conclusion: The FOXA۱ and ERBB۲ genes can be used as biomarkers for early detection ofbreast cancer and the appropriate treatment.

کلیدواژه ها:

Meta-analysis ، WGCNA ، DEGs ، Breast cancer ، qPCR