Integration of multiple networks and pathways identifies cancer driver genes in pan-cancer analysis.
BMC Genomics
; 19(1): 25, 2018 01 06.
Article
in En
| MEDLINE
| ID: mdl-29304754
ABSTRACT
BACKGROUND:
Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumourigenesis has yet to be completely understood. Pathways and networks are important tools to explain the role of genes in functional genomic studies. However, few methods consider the functional non-equal roles of genes in pathways and the complex gene-gene interactions in a network.RESULTS:
We present a novel method in pan-cancer analysis that identifies de-regulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumour/normal samples from 16 cancer types identified 895 genes with a central role in pathways and de-regulated in cancer. Comparing our approach with 15 current tools that identify cancer driver genes, we found that 35.6% of the 895 genes identified by our method have been found as cancer driver genes with at least 2/15 tools. Finally, we applied a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. We obtained a list of the top-ten cancer driver genes for each cancer considered in this study.CONCLUSIONS:
Our analysis 1) confirmed that there are several known cancer driver genes in common among different types of cancer, 2) highlighted that cancer driver genes are able to regulate crucial pathways.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Signal Transduction
/
Biomarkers, Tumor
/
Genomics
/
Gene Regulatory Networks
/
Neoplasms
Type of study:
Observational_studies
Limits:
Humans
Language:
En
Journal:
BMC Genomics
Journal subject:
GENETICA
Year:
2018
Type:
Article
Affiliation country:
Italy