Open AccessConstruction of a SUMOylation regulator‐based prognostic model in low‐grade glioma
Author(s) -
Li Xiaozhi,
Meng Yutong
Publication year - 2021
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.16553
Subject(s) - sumo protein , glioma , biology , regulator , kegg , proportional hazards model , cancer research , computational biology , transcriptome , bioinformatics , genetics , medicine , gene expression , gene , ubiquitin
Abstract Low‐grade glioma (LGG) is an intracranial malignant tumour that mainly originates from astrocytes and oligodendrocytes. SUMOylation is one of the post‐translational modifications but studies of SUMOylation in LGG is quite limited. Transcriptome data, single nucleotide variant (SNV) data and clinical data of LGG were derived from public databases. The differences between the expression of SUMOylation regulators in LGG and normal brain tissue were analysed. Cox regression was used to construct a prognostic model in the training cohort. Kaplan‐Meier survival curves and ROC curves were plotted in the training and the validation cohort to evaluate the effectiveness of the prognostic model. GO and KEGG analyses were applied to preliminarily analyse the biological functions. Compared with normal brain tissue, SENP1 and SENP7 were up‐regulated and SENP5 was down‐regulated in LGG. SUMOylation regulators may be involved in functions such as mRNA splicing, DNA replication, ATPase activity and spliceosome. One prognostic model was established based on the 4 SUMOylation regulator‐related signatures (RFWD3, MPHOSPH9, WRN and NUP155), which had a good predictive ability for overall survival. This study is expected to provide targets for the diagnosis and treatment of low‐grade glioma.