{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Larsson I"],"funding":["Barncancerfonden (Swedish Childhood Cancer Foundation)","Cancerfonden (Swedish Cancer Society)","Vetenskapsrådet (Swedish Research Council)","Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation)"],"pagination":["9699"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11549355"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["15(1)"],"pubmed_abstract":["Nervous system cancers exhibit diverse transcriptional cell states influenced by normal development, injury response, and growth. However, the understanding of these states' regulation and pharmacological relevance remains limited. Here we present \"single-cell regulatory-driven clustering\" (scregclust), a method that reconstructs cellular regulatory programs from extensive collections of single-cell RNA sequencing (scRNA-seq) data from both tumors and developing tissues. The algorithm efficiently divides target genes into modules, predicting key transcription factors and kinases with minimal computational time. Applying this method to adult and childhood brain cancers, we identify critical regulators and suggest interventions that could improve temozolomide treatment in glioblastoma. Addit"],"journal":["Nature communications"],"pubmed_title":["Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers."],"pmcid":["PMC11549355"],"funding_grant_id":["20 0839 PjF","2023-0089","2022.0057","2021-0322, 2019-03686"],"pubmed_authors":["Popova G","Nelander S","Koc A","Jornsten R","Held F","Larsson I","Kundu S"],"additional_accession":[]},"is_claimable":false,"name":"Reconstructing the regulatory programs underlying the phenotypic plasticity of neural cancers.","description":"Nervous system cancers exhibit diverse transcriptional cell states influenced by normal development, injury response, and growth. However, the understanding of these states' regulation and pharmacological relevance remains limited. Here we present \"single-cell regulatory-driven clustering\" (scregclust), a method that reconstructs cellular regulatory programs from extensive collections of single-cell RNA sequencing (scRNA-seq) data from both tumors and developing tissues. The algorithm efficiently divides target genes into modules, predicting key transcription factors and kinases with minimal computational time. Applying this method to adult and childhood brain cancers, we identify critical regulators and suggest interventions that could improve temozolomide treatment in glioblastoma. Addit","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Nov","modification":"2026-06-03T00:51:35.67Z","creation":"2025-04-19T17:27:38.28Z"},"accession":"S-EPMC11549355","cross_references":{"pubmed":["39516198"],"doi":["10.1038/s41467-024-53954-3"]}}