Constructing a signature based on the SIRT family to help the prognosis and treatment sensitivity in glioma patients

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 datasets which showed lower OS in patients with high risk of glioma compared to those with low risk. Further, ROC curve analyses indicated that the SIRT-based signature had the desired accuracy and universality for evaluating the prognosis of glioma patients. Using univariate and multivariate Cox regression analyses, the SIRT-based signature was confirmed as an independent prognostic factor applicable to subjects in the TCGA and CGGA databases. We also developed an OS nomogram including gender, age, risk score, pathological grade, and IDH status for clinical decision-making purposes. ssGSEA analysis showed a higher score for various immune subgroups (e.g., CD8+ T cells, DC, and TIL) in samples from high-risk patients, compared to those of low-risk ones. qPCR and western blotting confirmed the dysregulated expression of SIRTs in gliomas. Taken together, we developed a new signature on the basis of five SIRT family genes, which can help accurately predict OS of glioma patients. In addition, the findings of the present study suggest that this characteristic is associated with differences in immune status and infiltration levels of various immune cells in the tumor microenvironment.