Self-consistent signal transduction analysis for modeling context-specific signaling cascades and perturbations.
Journal
NPJ systems biology and applications
ISSN: 2056-7189
Titre abrégé: NPJ Syst Biol Appl
Pays: England
ID NLM: 101677786
Informations de publication
Date de publication:
19 Jul 2024
19 Jul 2024
Historique:
received:
19
09
2023
accepted:
12
07
2024
medline:
20
7
2024
pubmed:
20
7
2024
entrez:
19
7
2024
Statut:
epublish
Résumé
Biological signal transduction networks are central to information processing and regulation of gene expression across all domains of life. Dysregulation is known to cause a wide array of diseases, including cancers. Here I introduce self-consistent signal transduction analysis, which utilizes genome-scale -omics data (specifically transcriptomics and/or proteomics) in order to predict the flow of information through these networks in an individualized manner. I apply the method to the study of endocrine therapy in breast cancer patients, and show that drugs that inhibit estrogen receptor α elicit a wide array of antitumoral effects, and that their most clinically-impactful ones are through the modulation of proliferative signals that control the genes GREB1, HK1, AKT1, MAPK1, AKT2, and NQO1. This method offers researchers a valuable tool in understanding how and why dysregulation occurs, and how perturbations to the network (such as targeted therapies) effect the network itself, and ultimately patient outcomes.
Identifiants
pubmed: 39030258
doi: 10.1038/s41540-024-00404-x
pii: 10.1038/s41540-024-00404-x
doi:
Substances chimiques
Estrogen Receptor alpha
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
78Informations de copyright
© 2024. The Author(s).
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