Research Article

Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis

Published: December 14, 2011
Genet. Mol. Res. 10 (4) : 3856-3887 DOI: 10.4238/2011.December.14.10

Abstract

Prostate cancer is one of the most common male malignant neoplasms; however, its causes are not completely understood. A few recent studies have used gene expression profiling of prostate cancer to identify differentially expressed genes and possible relevant pathways. However, few studies have examined the genetic mechanics of prostate cancer at the pathway level to search for such pathways. We used gene set enrichment analysis and a meta-analysis of six independent studies after standardized microarray preprocessing, which increased concordance between these gene datasets. Based on gene set enrichment analysis, there were 12 down- and 25 up-regulated mixing pathways in more than two tissue datasets, while there were two down- and two up-regulated mixing pathways in three cell datasets. Based on the meta-analysis, there were 46 and nine common pathways in the tissue and cell datasets, respectively. Three up- and 10 down-regulated crossing pathways were detected with combined gene set enrichment analysis and meta-analysis. We found that genes with small changes are difficult to detect by classic univariate statistics; they can more easily be identified by pathway analysis. After standardized microarray preprocessing, we applied gene set enrichment analysis and a meta-analysis to increase the concordance in identifying biological mechanisms involved in prostate cancer. The gene pathways that we identified could provide insight concerning the development of prostate cancer.

Prostate cancer is one of the most common male malignant neoplasms; however, its causes are not completely understood. A few recent studies have used gene expression profiling of prostate cancer to identify differentially expressed genes and possible relevant pathways. However, few studies have examined the genetic mechanics of prostate cancer at the pathway level to search for such pathways. We used gene set enrichment analysis and a meta-analysis of six independent studies after standardized microarray preprocessing, which increased concordance between these gene datasets. Based on gene set enrichment analysis, there were 12 down- and 25 up-regulated mixing pathways in more than two tissue datasets, while there were two down- and two up-regulated mixing pathways in three cell datasets. Based on the meta-analysis, there were 46 and nine common pathways in the tissue and cell datasets, respectively. Three up- and 10 down-regulated crossing pathways were detected with combined gene set enrichment analysis and meta-analysis. We found that genes with small changes are difficult to detect by classic univariate statistics; they can more easily be identified by pathway analysis. After standardized microarray preprocessing, we applied gene set enrichment analysis and a meta-analysis to increase the concordance in identifying biological mechanisms involved in prostate cancer. The gene pathways that we identified could provide insight concerning the development of prostate cancer.