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

Abstract

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.

Genetic association analysis of the RTK/ERK pathway with aggressive prostate cancer highlights the potential role of CCND2 in disease progression

“…Although the potential association of the RTK/ERK pathway with prostate cancer risk has been suggested by our previous study 10–12 , the genetic and functional link of the pathway to aggressive prostate cancer have not been elucidated. In this work, on the basis of the Chinese Consortium for Prostate Cancer Genetics (ChinaPCa) project, we performed SNP- and gene-based prostate cancer case-only study, as well as expression quantitative trait loci (eQTL), clinical correlation, and prognosis analyses.…”

“…In addition, considerable evidence indicated that the signaling pathways including AR, PI3K/AKT, and RTK/ERK 7–9 have been identified as the key factors for prostate cancer risk, yet the underlying genetic basis remains elusive. The association analyses in our previous work suggested that the RTK/ERK pathway might be a significant factor conferring risk of prostate cancer 10–12 . Moreover, recent studies demonstrated the important role of this pathway in metastasis and progression of prostate cancer 13, 14 .…”

HNF1b is involved in prostate cancer risk via modulating androgenic hormone effects and coordination with other genes

“…Finally, 3 groups of data, including HNF1b co-expression genes from GEO, prostate cancer genes from DDPC, and meta-analysis of 6 independent gene expression profiling studies about prostate cancer (Ning et al, 2011), were checked manually to identify genes that were themselves not only related to HNF1b but also associated with prostate cancer susceptibility.…”

“…To address the hypothesis, we applied the microarray datasets from Gene Expression Omnibus (GEO) to generate genes whose expression is significantly correlated with HNF1b. The HNF1b-correlated genes were then compared with genes reported to be implicated in prostate cancer by Dragon Database (DDPC; http://apps.sanbi.ac.za/ddpc/) and our previous meta-analysis of 6 independent gene expression profiling studies about prostate cancer from GEO (Ning et al, 2011). Thus, HNF1b-associated prostate cancer genes were obtained.…”

A novel microRNA signature predicts survival in stomach adenocarcinoma

“…Specifically, Ago2, the co-target of miRNA-145, miR-184 and miR-802, played an important role in GC differentiation, lymph node invasion and clinical stage [33]. Myc, a multiplayer in carcinogenesis, progression and metabolism, was also the predicted target of miR-145 and miR-184 [34]. Moreover, many target genes related to VEGF receptor signaling pathway have been identified, such as VEGFR-3.…”