Constructing a signature based on the SIRT family to help the prognosis and treatment sensitivity in glioma patients - Xuan, Feiyue; Zhang, Zhiwei; Liu, Kuili; Gong, Haidong; Liang, Shaodong; Zhao, Youzhi; Li, Hongzhe (Frontiers in Genetics, 2022)
The silent information regulator (SIRT) family of enzymes has an essential role in basic cellular physiological events including apoptosis, metabolism, ageing and cell cycle progression. They critically contribute to promoting or inhibiting disease in cancers like glioma. In the present study, a new gene signature of this family has been identified to be applicable for risk assessment and stratification in glioma patients. For this purpose, the transcriptome and relevant clinical records of patients recognized with glioma were obtained from the Cancer Genomic Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LASSO regression analysis and multivariate Cox analysis were used to construct the signature. Using Kaplan-Meier analysis, the overall survival (OS) values were measured and compared between the train dataset and external test datasets which displayed a lower value in patients with a high risk of glioma compared to those with low risk. Also, ROC curve analysis displayed that the SIRT-based signature had desired accuracy and universality in evaluating the prognosis in glioma patients. Using univariate and multivariate Cox regression analyses, SIRT-based signature was demonstrated as an independent prognostic factor applicable for subjects recorded in TCGA and CGGA databases. We also developed an OS nomogram including gender, age, risk scores, pathological grades, and IDH status for clinical decision-making purposes. ssGSEA analysis displayed a higher score for various immune subgroups (e.g. CD8+ T cells, DC, and TIL) in the samples from HR patients compared to the low-risk ones. Finally, qPCR and Western blot confirmed the dysregulated expression of SIRTs in gliomas. In conclusion, we have developed a new signature on the basis of five SIRT family genes, which can accurately predict the glioma patients' OS. In addition, the present findings attribute this feature to the difference in immune status and the infiltration levels of various immune cells in the tumour microenvironment.