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Finding common genes in multiple cancer types through meta-analysis of microarray experiments: a rank aggregation
V Pihur1, Somnath Datta, Susmita Datta
1Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40292, USA.
Abstract:
Discovering genes involved in multiple types of cancers is of significant therapeutic importance. We show that collective evidence for such genes can be obtained via a form of meta-analysis that aggregates the results (rankings and p values) from various cancer-specific microarray experiments. This method is illustrated by a combined analysis of 20 microarray experiments. In the aggregated list of top-50 genes, 36 of them have been implicated in cancer (often multiple cancers) genesis in past studies, which also suggests that this list may contain some novel cancer genes that may deserve further scrutiny in the future.
Insights
Identifying genes linked to multiple cancers is crucial for therapy. A meta-analysis of cancer microarray data reveals a list of top genes, potentially including novel cancer-driving genes for future research.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Discovering genes implicated in multiple cancer types is vital for developing effective cancer therapies.
- Gene expression profiling using microarrays generates large datasets for cancer research.
Purpose of the Study:
- To develop and illustrate a meta-analysis method for aggregating results from cancer-specific microarray experiments.
- To identify genes consistently associated with multiple cancer types.
Main Methods:
- A meta-analysis approach was employed, combining rankings and p-values from diverse cancer-specific microarray studies.
- The method was applied to analyze a dataset comprising 20 microarray experiments.
Main Results:
- An aggregated list of the top 50 genes was generated.
- Out of the top 50 genes, 36 were previously implicated in the genesis of one or more cancer types.
- The findings suggest the potential discovery of novel cancer genes within the top-ranked list.
Conclusions:
- Meta-analysis of microarray data is an effective strategy for identifying genes involved in multiple cancers.
- The identified gene list provides valuable candidates for further investigation into cancer mechanisms and therapeutic targets.
