A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism
Some inherited or somatically-acquired gene variants are observed significantly more frequently in the genome of cancer cells. Although many of these cannot be confidently classified as driver mutations, they may contribute to shaping a cell environment that favours cancer onset and development. Und...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-05-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2022.893256/full |
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| author | Marta Iannuccelli Prisca Lo Surdo Prisca Lo Surdo Luana Licata Luana Licata Luisa Castagnoli Gianni Cesareni Livia Perfetto Livia Perfetto |
| author_facet | Marta Iannuccelli Prisca Lo Surdo Prisca Lo Surdo Luana Licata Luana Licata Luisa Castagnoli Gianni Cesareni Livia Perfetto Livia Perfetto |
| author_sort | Marta Iannuccelli |
| collection | DOAJ |
| description | Some inherited or somatically-acquired gene variants are observed significantly more frequently in the genome of cancer cells. Although many of these cannot be confidently classified as driver mutations, they may contribute to shaping a cell environment that favours cancer onset and development. Understanding how these gene variants causally affect cancer phenotypes may help developing strategies for reverting the disease phenotype. Here we focus on variants of genes whose products have the potential to modulate metabolism to support uncontrolled cell growth. Over recent months our team of expert curators has undertaken an effort to annotate in the database SIGNOR 1) metabolic pathways that are deregulated in cancer and 2) interactions connecting oncogenes and tumour suppressors to metabolic enzymes. In addition, we refined a recently developed graph analysis tool that permits users to infer causal paths leading from any human gene to modulation of metabolic pathways. The tool grounds on a human signed and directed network that connects ∼8400 biological entities such as proteins and protein complexes via causal relationships. The network, which is based on more than 30,000 published causal links, can be downloaded from the SIGNOR website. In addition, as SIGNOR stores information on drugs or other chemicals targeting the activity of many of the genes in the network, the identification of likely functional paths offers a rational framework for exploring new therapeutic strategies that revert the disease phenotype. |
| first_indexed | 2024-12-12T10:51:28Z |
| format | Article |
| id | doaj.art-1b37fde12a9a40fbbc2bc7322c8559a7 |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-12-12T10:51:28Z |
| publishDate | 2022-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-1b37fde12a9a40fbbc2bc7322c8559a72022-12-22T00:26:45ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-05-01910.3389/fmolb.2022.893256893256A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell MetabolismMarta Iannuccelli0Prisca Lo Surdo1Prisca Lo Surdo2Luana Licata3Luana Licata4Luisa Castagnoli5Gianni Cesareni6Livia Perfetto7Livia Perfetto8Department of Biology, University of Rome Tor Vergata, Rome, ItalyDepartment of Biology, University of Rome Tor Vergata, Rome, ItalyFondazione Human Technopole, Milan, ItalyDepartment of Biology, University of Rome Tor Vergata, Rome, ItalyFondazione Human Technopole, Milan, ItalyDepartment of Biology, University of Rome Tor Vergata, Rome, ItalyDepartment of Biology, University of Rome Tor Vergata, Rome, ItalyDepartment of Biology, University of Rome Tor Vergata, Rome, ItalyFondazione Human Technopole, Milan, ItalySome inherited or somatically-acquired gene variants are observed significantly more frequently in the genome of cancer cells. Although many of these cannot be confidently classified as driver mutations, they may contribute to shaping a cell environment that favours cancer onset and development. Understanding how these gene variants causally affect cancer phenotypes may help developing strategies for reverting the disease phenotype. Here we focus on variants of genes whose products have the potential to modulate metabolism to support uncontrolled cell growth. Over recent months our team of expert curators has undertaken an effort to annotate in the database SIGNOR 1) metabolic pathways that are deregulated in cancer and 2) interactions connecting oncogenes and tumour suppressors to metabolic enzymes. In addition, we refined a recently developed graph analysis tool that permits users to infer causal paths leading from any human gene to modulation of metabolic pathways. The tool grounds on a human signed and directed network that connects ∼8400 biological entities such as proteins and protein complexes via causal relationships. The network, which is based on more than 30,000 published causal links, can be downloaded from the SIGNOR website. In addition, as SIGNOR stores information on drugs or other chemicals targeting the activity of many of the genes in the network, the identification of likely functional paths offers a rational framework for exploring new therapeutic strategies that revert the disease phenotype.https://www.frontiersin.org/articles/10.3389/fmolb.2022.893256/fullmetabolic pathwayrate limiting enzymecancercausal interactionnetworkSIGNOR |
| spellingShingle | Marta Iannuccelli Prisca Lo Surdo Prisca Lo Surdo Luana Licata Luana Licata Luisa Castagnoli Gianni Cesareni Livia Perfetto Livia Perfetto A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism Frontiers in Molecular Biosciences metabolic pathway rate limiting enzyme cancer causal interaction network SIGNOR |
| title | A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism |
| title_full | A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism |
| title_fullStr | A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism |
| title_full_unstemmed | A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism |
| title_short | A Resource to Infer Molecular Paths Linking Cancer Mutations to Perturbation of Cell Metabolism |
| title_sort | resource to infer molecular paths linking cancer mutations to perturbation of cell metabolism |
| topic | metabolic pathway rate limiting enzyme cancer causal interaction network SIGNOR |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2022.893256/full |
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