A meta-analysis of multiple matched aCGH/expression cancer datasets reveals regulatory relationships and pathway enrichment of potential oncogenes

Abstract

The copy numbers of genes in cancer samples are often highly disrupted and form a natural amplification/deletion experiment encompassing multiple genes. Matched array comparative genomics and transcriptomics datasets from such samples can be used to predict inter-chromosomal gene regulatory relationships. Previously we published the database METAMATCHED, comprising the results from such an analysis of a large number of publically available cancer datasets. Here we investigate genes in the database which are unusual in that their copy number exhibits consistent heterogeneous disruption in a high proportion of the cancer datasets. We assess the potential relevance of these genes to the pathology of the cancer samples, in light of their predicted regulatory relationships and enriched biological pathways. A network-based method was used to identify enriched pathways from the genes’ inferred targets. The analysis predicts both known and new regulator-target interactions and pathway memberships. We examine examples in detail, in particular the gene POGZ , which is disrupted in many of the cancer datasets and has an unusually large number of predicted targets, from which the network analysis predicts membership of cancer related pathways. The results suggest close involvement in known cancer pathways of genes exhibiting consistent heterogeneous copy number disruption. Further experimental work would clarify their relevance to tumor biology. The results of the analysis presented in the database METAMATCHED, and included here as an R archive file, constitute a large number of predicted regulatory relationships and pathway memberships which we anticipate will be useful in informing such experiments.

Deciphering the Polygenic Basis of Racial Disparities in Prostate Cancer By an Integrative Analysis of Genomic and Transcriptomic Data

“…A recent publication reports that the downregulated long noncoding RNA DHRS4-AS1 is pro-tumoral and associated with the prognosis of clear cell renal cell carcinoma (53). A bioinformatics analysis shows that the coding gene POGZ is disrupted in many of the cancer datasets and has an unusually large number of predicted targets involved in the cancer pathways (54). The associations between PRS and gene expression level in tumor samples suggest that the germline mutations exert impact on cancer cells, implicating the active roles in tumor progression.…”