Virus proteins similar to human proteins as possible disturbance on human pathways

Zhao, Jinyan; Kotera, Masaaki; Goto, Susumu

doi:10.1007/s11693-014-9141-y

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…Analysis of the significantly enriched KEGG pathways affected by the proteins involved in multi-cancer edgetic perturbations revealed known pathways 49,50 deregulated across cancer types - Fig. 6 and Dataset 3.…”

Section: Resultsmentioning

confidence: 99%

Edgetic perturbation signatures represent known and novel cancer biomarkers

Kataka

Zaucha

Frishman

et al. 2020

Sci Rep

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isoform switching is a recently characterized hallmark of cancer, and often translates to the loss or gain of domains mediating protein interactions and thus, the re-wiring of the interactome. Recent computational tools leverage domain-domain interaction data to resolve the condition-specific interaction networks from RnA-Seq data accounting for the domain content of the primary transcripts expressed. Here, we used The Cancer Genome Atlas RNA-Seq datasets to generate 642 patient-specific pairs of interactomes corresponding to both the tumor and the healthy tissues across 13 cancer types. The comparison of these interactomes provided a list of patient-specific edgetic perturbations of the interactomes associated with the cancerous state. We found that among the identified perturbations, select sets are robustly shared between patients at the multi-cancer, cancer-specific and cancer subtype specific levels. Interestingly, the majority of the alterations do not directly involve significantly mutated genes, nevertheless, they strongly correlate with patient survival. The findings (available at edgeexplorer: "http://webclu.bio.wzw.tum.de/edgeexplorer") are a new source of potential biomarkers for classifying cancer types and the proteins we identified are potential anti-cancer therapy targets.Cancer involves the accumulation of somatic mutations 1 and epigenetic modifications 2 , which drive the cells into the malignant state. Recurrent mutations implicated in tumorigenesis affect highly connected proteins within the protein interaction network 3,4 and are enriched at the interaction interfaces 5,6 and phosphorylation sites 7 signifying their role in rewiring protein interactions 8 . For this reason, cancer has been described as the disease of the interactome 9 . Indeed, the network of protein-protein interactions (PPI) has repeatedly allowed for the extraction of molecular features predictive of various phenotypic traits relevant to cancer -the so-called disease biomarkers 10 . For example, Cui et al. have identified putative interaction-disrupting mutations occurring at the interfaces of protein complexes and demonstrated that their presence is prognostic of poor survival 11 . In another study, Li et al. 12 developed the "OncoPPI" network of protein-protein interactions (PPIN) relevant to lung cancer, identifying biomarkers that can inform therapeutic decisions according to the drug sensitivity in certain conditions 12 . Nevertheless, the physical disruption of interaction sites by somatic mutations is only one mode of perturbing the interactome. Another relevant cellular process is regulating the expression (and thereby the local molecular concentration) of the interacting proteins 13 ; this has been utilized in mining the network of protein-protein interactions to identify modules of differentially expressed genes serving as robust biomarkers indicative of breast cancer metastasis 14 or stratifying patients from several breast cancer subtypes 15 . Furthermore, the phenomenon of "isoform switching", i.e. altering t...

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Section: Resultsmentioning

confidence: 99%

Edgetic perturbation signatures represent known and novel cancer biomarkers

Kataka

Zaucha

Frishman

et al. 2020

Sci Rep

View full text Add to dashboard Cite

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Interactome of the hepatitis C virus: Literature mining with ANDSystem

et al. 2016

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A study of the molecular genetics mechanisms of host-pathogen interactions is of paramount importance in developing drugs against viral diseases. Currently, the literature contains a huge amount of information that describes interactions between HCV and human proteins. In addition, there are many factual databases that contain experimentally verified data on HCV-host interactions. The sources of such data are the original data along with the data manually extracted from the literature. However, the manual analysis of scientific publications is time consuming and, because of this, databases created with such an approach often do not have complete information. One of the most promising methods to provide actualisation and completeness of information is text mining. Here, with the use of a previously developed method by the authors using ANDSystem, an automated extraction of information on the interactions between HCV and human proteins was conducted. As a data source for the text mining approach, PubMed abstracts and full text articles were used. Additionally, external factual databases were analyzed. On the basis of this analysis, a special version of ANDSystem, extended with the HCV interactome, was created. The HCV interactome contains information about the interactions between 969 human and 11 HCV proteins. Among the 969 proteins, 153 'new' proteins were found not previously referred to in any external databases of protein-protein interactions for HCV-host interactions. Thus, the extended ANDSystem possesses a more comprehensive detailing of HCV-host interactions versus other existing databases. It was interesting that HCV proteins more preferably interact with human proteins that were already involved in a large number of protein-protein interactions as well as those associated with many diseases. Among human proteins of the HCV interactome, there were a large number of proteins regulated by microRNAs. It turned out that the results obtained for protein-protein interactions and microRNA-regulation did not depend on how well the proteins were studied, while protein-disease interactions appeared to be dependent on the level of study. In particular, the mean number of diseases linked to well-studied proteins (proteins were considered well-studied if they were mentioned in 50 or more PubMed publications) from the HCV interactome was 20.8, significantly exceeding the mean number of associations with diseases (10.1) for the total set of well-studied human proteins present in ANDSystem. For proteins not highly poorly-studied investigated, proteins from the HCV interactome (each protein was referred to in less than 50 publications) distribution of the number of diseases associated with them had no statistically significant differences from the distribution of the number of diseases associated with poorly-studied proteins based on the total set of human proteins stored in ANDSystem. With this, the average number of associations with diseases for the HCV interactome and the total set of human proteins were 0.3 and 0.2, ...

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Virus proteins similar to human proteins as possible disturbance on human pathways

Cited by 2 publications

References 35 publications

Edgetic perturbation signatures represent known and novel cancer biomarkers

Edgetic perturbation signatures represent known and novel cancer biomarkers

Interactome of the hepatitis C virus: Literature mining with ANDSystem

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