Meta-analysis of Gene Expression in Breast Cancer
Author Information
Author(s): Gur-Dedeoglu Bala, Konu Ozlen, Kir Serkan, Ozturk Ahmet Rasit, Bozkurt Betul, Ergul Gulusan, Yulug Isik G
Primary Institution: Bilkent University
Hypothesis
Can a resampling-based meta-analysis identify differentially expressed genes in breast cancer?
Conclusion
The meta-analysis approach successfully identified a stable set of differentially expressed genes that can help classify breast cancer subtypes.
Supporting Evidence
- The meta-analysis identified a highly stable set of genes for classification of normal breast samples and breast tumors.
- Expression results from real-time qRT-PCR supported the meta-analysis findings.
- Statistical power and stringent filtering criteria enhanced the identification of novel candidate genes.
Takeaway
Researchers looked at breast cancer samples to find genes that can tell the difference between healthy and cancerous tissue. They found some important genes that help identify different types of breast cancer.
Methodology
The study used a resampling-based meta-analysis strategy on two independent microarray datasets to identify differentially expressed genes.
Potential Biases
Potential biases may arise from the selection of datasets and the methodologies used in the original studies.
Limitations
The study's findings may not be generalizable beyond the specific datasets used.
Participant Demographics
The study included samples from patients with invasive ductal carcinoma and invasive lobular carcinoma.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website