Journal of Inflammation Research (Jan 2022)
A Pyroptosis-Related Gene Prognostic Index Correlated with Survival and Immune Microenvironment in Glioma
Abstract
Jianglin Zheng,1,* Zijie Zhou,1,* Yue Qiu,2,* Minjie Wang,1 Hao Yu,1 Zhipeng Wu,1 Xuan Wang,1 Xiaobing Jiang1 1Department of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2Department of Otolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xuan Wang; Xiaobing JiangDepartment of Neurosurgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of ChinaEmail [email protected]; [email protected]: As an inflammatory form of programmed cell death, pyroptosis has been well established to be associated with tumorigenesis and tumor immune microenvironment. In this paper, we aimed at the construction of a pyroptosis-related gene prognostic index (PRGPI) for predicting prognosis and guiding individualized immunotherapy in glioma patients.Patients and Methods: Pyroptosis-related genes (PRGs) were identified based on a detailed review of published literatures. The transcriptome data and clinical information of glioma patients were obtained from CGGA and TCGA databases. PRGPI was constructed by using the multivariate Cox regression method. The immune cell infiltration level was analyzed via CIBERSORT algorithm. The tumor immune dysfunction and exclusion (TIDE) algorithm was applied to evaluate the potential response to immune checkpoint inhibitor (ICI) therapy. The expression patterns of PRGs in PRGPI were validated in cell lines and pathological specimens.Results: We identified a total of 31 PRGs. Among them, PRGs (CASP3, DPP9, MAPK8, PELP1 and TOMM20) were selected for the construction of PRGPI. In both training (CGGA693) and validation (CGGA325 and TCGA) cohorts, PRGPI-high patients showed an inferior survival outcome compared with PRGPI-low patients. ROC curves illustrated that the prognostic prediction power of PRGPI was robust. A nomogram was developed based on independent prognostic indicators (PRGPI, age and WHO grade), and also exhibited a strong forecasting ability for overall survival (OS). Additionally, PRGPI-high patients exhibited higher immune, stroma and ESTIMATE scores, lower tumor purity, higher infiltration of M2-type macrophages, lower infiltration of CD8+ T cells and activated NK cells, higher tumor mutation burden (TMB), and higher expression of immune checkpoints. TIDE showed that PRGPI-high group had more responders of ICI therapy than PRGPI-low group. Finally, the expression patterns of five selected PRGs in PRGPI were significantly different between normal and glioma.Conclusion: The constructed PRGPI can be used for predicting prognosis and guiding individualized immunotherapy in glioma patients.Keywords: pyroptosis, glioma, prognosis, immune microenvironment, ICI therapy
