Systematic identification, development, and validation of prognostic biomarkers involving the tumor-immune microenvironment for glioblastoma.

Gliomas are infiltrative neoplasms with a highly invasive nature. Due to its distinct genomic, genetic and epigenetic features, the immune prognostic signature (IPS) and immune microenvironment of glioblastoma (GBM) merit further research. We aimed to explore prognosis-related immune genes and develop an IPS model for predicting prognosis in GBM. RNA-sequencing data, as well as clinical information, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) public cohorts were analyzed. To develop the IPS, least absolute shrinkage and selection operator (LASSO) Cox analysis was performed for immune-related genes that were differentially expressed between GBM and normal tissues. Then, interaction effects of the IPS on the immune microenvironment were systematically analyzed; the precise prognostic model was developed based on the IPS and clinical data and was then further validated. A total of 21 immune prognostic genes were identified based on GBM microenvironment status. An 8-gene IPS was established, and the GBM patients were effectively stratified into low- and high-risk groups in the TCGA cohort as a training set. Univariate and multivariate Cox analyses revealed that IPS was an independent prognostic factor, and the prognostic performance of individual IPS genes was systematically illustrated. In addition, a comprehensive and novel nomogram model was initially established to estimate overall survival in TCGA-GBM patients, and high-risk patients had higher levels of dendritic cell and neutrophil infiltration. Furthermore, the nomogram model was developed and validated in the CGGA validation set. The low-risk IPS was linked to a stronger response to anti-PD-L1 immunotherapy and clinical advantages in the IMvigor210 cohort. This novel IPS with promising biomarkers classifies GBM patients into subgroups with distinct clinical outcomes and immunophenotypes. Our findings and this resource may help to characterize the immune microenvironment, inform cancer immunotherapy and facilitate the development of precision immuno-oncology.

Zhao B ，Wang Y ，Wang Y ，Chen W ，Liu PH ，Kong Z ，Dai C ，Wang Y ，Ma W ... -  《-》

A novel Foxp3-related immune prognostic signature for glioblastoma multiforme based on immunogenomic profiling.

Guo XY ，Zhang GH ，Wang ZN ，Duan H ，Xie T ，Liang L ，Cui R ，Hu HR ，Wu Y ，Dong JJ ，He ZQ ，Mou YG ... -  《Aging-US》

Establishment and validation of an immune-based prognostic score model in glioblastoma.

Immune escape is one of the landmark features of glioblastoma (GBM). Immunotherapy is undoubtedly a revolution in the field of tumor treatment, especially the application of immune checkpoint inhibitors and CAR-T cells, which have achieved amazing results in fighting against cancer. This study aimed to establish a TP53-related immune-based score model to improve the prognostic of GBM by investigating the gene mutations and the immune landscape of GBM. Data were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed genes (DEGs) analysis between the TP53 mutated (TP53MUT) and wild-type (TP53WT) GBM patients was conducted. The CIBERSORT algorithm was applied to evaluate the proportion of immune cell types and RNA sequencing (RNA-seq) data from the TCGA and CGGA were used as discovery and validation cohorts, respectively, to build and validate an immune-related prognostic model (IPM). Genes in the IPM model were first screened by univariate Cox analysis, then filtered by the least absolute shrinkage and selection operator (LASSO) Cox regression method to eliminate collinearity among DEGs. A nomogram was finally established and evaluated by combining both the IPM and other clinical factors. PTEN was the top most mutated gene in GBM patients (118/393), followed by TP53 (116/393). 332 immune-related genes were identified and the immune response in the TP53WT group was remarkably greater than in the TP53MUT group. The final IPM model composed three immune-related genes: IPM risk score = (0.392 × S100A8 expression) + (0.174 × CXCL1 expression) + (0.368 × IGLL5 expression), significantly correlated with the overall survival (OS) of GBM in the stratified TP53 status subgroups and was an independent prognostic variate for GBM. By integrating the IPM and clinical characteristics, a nomogram was generated to facilitate clinical utilization, with the results suggesting that it has better predictive performance for GBM prognosis than the IPM. The IPM model can identify patients at high-risk and can be combined with other clinical factors to estimate the OS of GBM patients, demonstrating that it is a promising biomarker to optimize the prognosis of GBM.

Qin Z ，Zhang X ，Chen Z ，Liu N ... -  《-》

Ferroptosis-related gene signature correlates with the tumor immune features and predicts the prognosis of glioma patients.

Glioma is a malignant intracranial tumor and the most fatal cancer. The role of ferroptosis in the clinical progression of gliomas is unclear. Univariate and least absolute shrinkage and selection operator (Lasso) Cox regression methods were used to develop a ferroptosis-related signature (FRSig) using a cohort of glioma patients from the Chinese Glioma Genome Atlas (CGGA), and was validated using an independent cohort of glioma patients from The Cancer Genome Atlas (TCGA). A single-sample gene set enrichment analysis (ssGSEA) was used to calculate levels of the immune infiltration. Multivariate Cox regression was used to determine the independent prognostic role of clinicopathological factors and to establish a nomogram model for clinical application. We analyzed the correlations between the clinicopathological features and ferroptosis-related gene (FRG) expression and established an FRSig to calculate the risk score for individual glioma patients. Patients were stratified into two subgroups with distinct clinical outcomes. Immune cell infiltration in the glioma microenvironment and immune-related indexes were identified that significantly correlated with the FRSig, the tumor mutation burden (TMB), copy number alteration (CNA), and immune checkpoint expression was also significantly positively correlated with the FRSig score. Ultimately, an FRSig-based nomogram model was constructed using the independent prognostic factors age, World Health Organization (WHO) grade, and FRSig score. We established the FRSig to assess the prognosis of glioma patients. The FRSig also represented the glioma microenvironment status. Our FRSig will contribute to improve patient management and individualized therapy by offering a molecular biomarker signature for precise treatment.

Hu Y ，Tu Z ，Lei K ，Huang K ，Zhu X ... -  《-》

Prognostic value and immune relevancy of a combined autophagy-, apoptosis- and necrosis-related gene signature in glioblastoma.

Glioblastoma (GBM) is considered the most malignant and devastating intracranial tumor without effective treatment. Autophagy, apoptosis, and necrosis, three classically known cell death pathways, can provide novel clinical and immunological insights, which may assist in designing personalized therapeutics. In this study, we developed and validated an effective signature based on autophagy-, apoptosis- and necrosis-related genes for prognostic implications in GBM patients. Variations in the expression of genes involved in autophagy, apoptosis and necrosis were explored in 518 GBM patients from The Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis, and multivariate Cox analysis were performed to construct a combined prognostic signature. Kaplan-Meier survival, receiver-operating characteristic (ROC) curves and Cox regression analyses based on overall survival (OS) and progression-free survival (PFS) were conducted to estimate the independent prognostic performance of the gene signature. The Chinese Glioma Genome Atlas (CGGA) dataset was used for external validation. Finally, we investigated the differences in the immune microenvironment between different prognostic groups and predicted potential compounds targeting each group. A 16-gene cell death index (CDI) was established. Patients were clustered into either the high risk or the low risk groups according to the CDI score, and those in the low risk group presented significantly longer OS and PFS than the high CDI group. ROC curves demonstrated outstanding performance of the gene signature in both the training and validation groups. Furthermore, immune cell analysis identified higher infiltration of neutrophils, macrophages, Treg, T helper cells, and aDCs, and lower infiltration of B cells in the high CDI group. Interestingly, this group also showed lower expression levels of immune checkpoint molecules PDCD1 and CD200, and higher expression levels of PDCD1LG2, CD86, CD48 and IDO1. Our study proposes that the CDI signature can be utilized as a prognostic predictor and may guide patients' selection for preferential use of immunotherapy in GBM.

Bi Y ，Wu ZH ，Cao F 《BMC CANCER》