Dataset Information

Oncogenic Hijacking of the PIN1 Signaling Network.

ABSTRACT: Cellular choices are determined by developmental and environmental stimuli through integrated signal transduction pathways. These critically depend on attainment of proper activation levels that in turn rely on post-translational modifications (PTMs) of single pathway members. Among these PTMs, post-phosphorylation prolyl-isomerization mediated by PIN1 represents a unique mechanism of spatial, temporal and quantitative control of signal transduction. Indeed PIN1 was shown to be crucial for determining activation levels of several pathways and biological outcomes downstream to a plethora of stimuli. Of note, studies performed in different model organisms and humans have shown that hormonal, nutrient, and oncogenic stimuli simultaneously affect both PIN1 activity and the pathways that depend on PIN1-mediated prolyl-isomerization, suggesting the existence of evolutionarily conserved molecular circuitries centered on this isomerase. This review focuses on molecular mechanisms and cellular processes like proliferation, metabolism, and stem cell fate, that are regulated by PIN1 in physiological conditions, discussing how these are subverted in and hijacked by cancer cells. Current status and open questions regarding the use of PIN1 as biomarker and target for cancer therapy as well as clinical development of PIN1 inhibitors are also addressed.

SUBMITTER: Zannini A

PROVIDER: S-EPMC6401644 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Oncogenic Hijacking of the PIN1 Signaling Network.

Zannini Alessandro A Rustighi Alessandra A Campaner Elena E Del Sal Giannino G

Frontiers in oncology 20190225

Cellular choices are determined by developmental and environmental stimuli through integrated signal transduction pathways. These critically depend on attainment of proper activation levels that in turn rely on post-translational modifications (PTMs) of single pathway members. Among these PTMs, post-phosphorylation prolyl-isomerization mediated by PIN1 represents a unique mechanism of spatial, temporal and quantitative control of signal transduction. Indeed PIN1 was shown to be crucial for deter ...[more]

PMID: 30873382

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Project description:Cancer has been increasingly recognized as a systems biology disease since many investigators have demonstrated that this malignant phenotype emerges from abnormal protein-protein, regulatory and metabolic interactions induced by simultaneous structural and regulatory changes in multiple genes and pathways. Therefore, the identification of oncogenic interactions and cancer-related signaling networks is crucial for better understanding cancer. As experimental techniques for determining such interactions and signaling networks are labor-intensive and time-consuming, the development of a computational approach capable to accomplish this task would be of great value. For this purpose, we present here a novel computational approach based on network topology and machine learning capable to predict oncogenic interactions and extract relevant cancer-related signaling subnetworks from an integrated network of human genes interactions (INHGI). This approach, called graph2sig, is twofold: first, it assigns oncogenic scores to all interactions in the INHGI and then these oncogenic scores are used as edge weights to extract oncogenic signaling subnetworks from INHGI. Regarding the prediction of oncogenic interactions, we showed that graph2sig is able to recover 89% of known oncogenic interactions with a precision of 77%. Moreover, the interactions that received high oncogenic scores are enriched in genes for which mutations have been causally implicated in cancer. We also demonstrated that graph2sig is potentially useful in extracting oncogenic signaling subnetworks: more than 80% of constructed subnetworks contain more than 50% of original interactions in their corresponding oncogenic linear pathways present in the KEGG PATHWAY database. In addition, the potential oncogenic signaling subnetworks discovered by graph2sig are supported by experimental evidence. Taken together, these results suggest that graph2sig can be a useful tool for investigators involved in cancer research interested in detecting signaling networks most prone to contribute with the emergence of malignant phenotype.

Dataset Information

Oncogenic Hijacking of the PIN1 Signaling Network.

Publications

Oncogenic Hijacking of the PIN1 Signaling Network.

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