{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Xuan F"],"pubmed_abstract":["Enzymes of the silent information regulator (SIRT) family exert crucial roles in basic cellular physiological processes including apoptosis, metabolism, ageing, and cell cycle progression. They critically contribute to promoting or inhibiting cancers such as glioma. In the present study, a new gene signature of this family was identified for use in risk assessment and stratification of glioma patients. To this end, the transcriptome and relevant clinical records of patients diagnosed with glioma were obtained from the Cancer Genomic Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LASSO regression and multivariate Cox analyses were used to establish the signature. Using Kaplan-Meier analyses, overall survival (OS) was assessed and compared between a training and an external test da"],"journal":["Frontiers in genetics"],"pagination":["1035368"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9780371"],"repository":["biostudies-literature"],"pubmed_title":["Constructing a signature based on the SIRT family to help the prognosis and treatment sensitivity in glioma patients."],"pmcid":["PMC9780371"],"pubmed_authors":["Liang S","Li H","Liu K","Zhang Z","Xuan F","Gong H","Zhao Y"],"additional_accession":[]},"is_claimable":false,"name":"Constructing a signature based on the SIRT family to help the prognosis and treatment sensitivity in glioma patients.","description":"Enzymes of the silent information regulator (SIRT) family exert crucial roles in basic cellular physiological processes including apoptosis, metabolism, ageing, and cell cycle progression. They critically contribute to promoting or inhibiting cancers such as glioma. In the present study, a new gene signature of this family was identified for use in risk assessment and stratification of glioma patients. To this end, the transcriptome and relevant clinical records of patients diagnosed with glioma were obtained from the Cancer Genomic Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LASSO regression and multivariate Cox analyses were used to establish the signature. Using Kaplan-Meier analyses, overall survival (OS) was assessed and compared between a training and an external test da","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T21:55:06.013Z","creation":"2024-11-20T22:39:03.789Z"},"accession":"S-EPMC9780371","cross_references":{"pubmed":["36568393"],"doi":["10.3389/fgene.2022.1035368"]}}