Dataset Information

CancerGeneNet: linking driver genes to cancer hallmarks.

ABSTRACT: CancerGeneNet (https://signor.uniroma2.it/CancerGeneNet/) is a resource that links genes that are frequently mutated in cancers to cancer phenotypes. The resource takes advantage of a curation effort aimed at embedding a large fraction of the gene products that are found altered in cancer cells into a network of causal protein relationships. Graph algorithms, in turn, allow to infer likely paths of causal interactions linking cancer associated genes to cancer phenotypes thus offering a rational framework for the design of strategies to revert disease phenotypes. CancerGeneNet bridges two interaction layers by connecting proteins whose activities are affected by cancer drivers to proteins that impact on the 'hallmarks of cancer'. In addition, CancerGeneNet annotates curated pathways that are relevant to rationalize the pathological consequences of cancer driver mutations in selected common cancers and 'MiniPathways' illustrating regulatory circuits that are frequently altered in different cancers.

SUBMITTER: Iannuccelli M

PROVIDER: S-EPMC6943052 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CancerGeneNet: linking driver genes to cancer hallmarks.

Iannuccelli Marta M Micarelli Elisa E Surdo Prisca Lo PL Palma Alessandro A Perfetto Livia L Rozzo Ilaria I Castagnoli Luisa L Licata Luana L Cesareni Gianni G

Nucleic acids research 20200101 D1

CancerGeneNet (https://signor.uniroma2.it/CancerGeneNet/) is a resource that links genes that are frequently mutated in cancers to cancer phenotypes. The resource takes advantage of a curation effort aimed at embedding a large fraction of the gene products that are found altered in cancer cells into a network of causal protein relationships. Graph algorithms, in turn, allow to infer likely paths of causal interactions linking cancer associated genes to cancer phenotypes thus offering a rational ...[more]

PMID: 31598703

Similar Datasets

Project description:Reciprocal chromosomal translocations (RCTs) leading to the formation of fusion genes are important drivers of hematological cancers. Although the general requirements for breakage and fusion are fairly well understood, quantitative support for a general mechanism of RCT formation is still lacking. The aim of this paper is to analyze available high-throughput datasets with computational and robust statistical methods, in order to identify genomic hallmarks of translocation partner genes (TPGs). Our results show that fusion genes are generally overexpressed due to increased promoter activity of 5' TPGs and to more stable 3'-UTR regions of 3' TPGs. Furthermore, expression profiling of 5' TPGs and of interaction partners of 3' TPGs indicates that these features can help to explain tissue specificity of hematological translocations. Analysis of protein domains retained in fusion proteins shows that the co-occurrence of specific domain combinations is non-random and that distinct functional classes of fusion proteins tend to be associated with different components of the gene fusion network. This indicates that the configuration of fusion proteins plays an important role in determining which 5' and 3' TPGs will combine in specific fusion genes. It is generally accepted that chromosomal proximity in the nucleus can explain the specific pairing of 5' and 3' TPGS and the recurrence of hematological translocations. Using recently available data for chromosomal contact probabilities (Hi-C) we show that TPGs are preferentially located in early replicated regions and occupy distinct clusters in the nucleus. However, our data suggest that, in general, nuclear position of TPGs in hematological cancers explains neither TPG pairing nor clinical frequency. Taken together, our results support a model in which genomic features related to regulation of expression and replication timing determine the set of candidate genes more likely to be translocated in hematological tissues, with functional constraints being responsible for specific gene combinations.

Dataset Information

CancerGeneNet: linking driver genes to cancer hallmarks.

Publications

CancerGeneNet: linking driver genes to cancer hallmarks.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets