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 ... -  《-》

An Immune-Related Signature for Predicting the Prognosis of Lower-Grade Gliomas.

Lower-grade gliomas (LGGs) have more favorable outcomes than glioblastomas; however, LGGs often progress to process glioblastomas within a few years. Numerous studies have proven that the tumor microenvironment (TME) is correlated with the prognosis of glioma. LGG RNA-Sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were extracted and then divided into training and testing cohorts, respectively. Immune-related differentially expressed genes (DEGs) were screened to establish a prognostic signature by a multivariate Cox proportional hazards regression model. The immune-related risk score and clinical information, such as age, sex, World Health Organization (WHO) grade, and isocitrate dehydrogenase 1 (IDH1) mutation, were used to independently validate and develop a prognostic nomogram. GO and KEGG pathway analyses to DEGs between immune-related high-risk and low-risk groups were performed. Sixteen immune-related genes were screened for establishing a prognostic signature. The risk score had a negative correlation with prognosis, with an area under the receiver operating characteristic (ROC) curve of 0.941. The risk score, age, grade, and IDH1 mutation were identified as independent prognostic factors in patients with LGGs. The hazard ratios (HRs) of the high-risk score were 5.247 [95% confidence interval (CI) = 3.060-8.996] in the multivariate analysis. A prognostic nomogram of 1-, 3-, and 5-year survival was established and validated internally and externally. Go and KEGG pathway analyses implied that immune-related biological function and pathways were involved in the TME. The immune-related prognostic signature and the prognostic nomogram could accurately predict survival.

Zhang H ，Li X ，Li Y ，Chen B ，Zong Z ，Shen L ... -  《Frontiers in Immunology》

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 ... -  《-》

Comprehensive exploration of tumor mutational burden and immune infiltration in diffuse glioma.

Immune checkpoint inhibitors (ICIs) have been used as a novel treatment for diffuse gliomas, but the efficacy varies with patients, which may be associated with the tumor mutational burden (TMB) and immune infiltration. We aimed to explore the relationship between the two and their impacts on the prognosis. The data of the training set were downloaded from The Cancer Genome Atlas (TCGA). "DESeq2" R package was used for differential analysis and identification of differentially expressed genes (DEGs). A gene risk score model was constructed based on DEGs, and a nomogram was developed combined with clinical features. With the CIBERSORT algorithm, the relationship between TMB and immune infiltration was analyzed, and an immune risk score model was constructed. Two models were verification in the validation set downloaded from the Chinese Glioma Genome Atlas (CGGA). Higher TMB was related to worse prognosis, older age, higher grade, and higher immune checkpoint expression. The gene risk score model was constructed based on BIRC5, SAA1, and TNFRSF11B, and their expressions were all negatively correlated with prognosis. The nomogram was developed combined with age and grade. The immune risk score model was constructed based on M0 macrophages, neutrophils, naïve CD4+ T cells, and activated mast cells. The proportions of the first two were higher in the high-TMB group and correlated with worse prognosis, while the latter two were precisely opposite. In diffuse gliomas, TMB was negatively correlated with prognosis. The association of immune infiltration with TMB and prognosis varied with the type of immune cells. The nomogram and risk score models can accurately predict prognosis. The results can help identify patients suitable for ICIs and potential therapeutic targets, thus improve the treatment of diffuse gliomas.

Kang K ，Xie F ，Wu Y ，Wang Z ，Wang L ，Long J ，Lian X ，Zhang F ... -  《-》

Single cell RNA sequencing reveals differentiation related genes with drawing implications in predicting prognosis and immunotherapy response in gliomas.

Differentiation states of glioma cells correlated with prognosis and tumor-immune microenvironment (TIME) in patients with gliomas. We aimed to identify differentiation related genes (DRGs) for predicting the prognosis and immunotherapy response in patients with gliomas. We identified three differentiation states and the corresponding DRGs in glioma cells through single-cell transcriptomics analysis. Based on the DRGs, we separated glioma patients into three clusters with distinct clinicopathological features in combination with bulk RNA-seq data. Weighted correlation network analysis, univariate cox regression analysis and least absolute shrinkage and selection operator analysis were involved in the construction of the prognostic model based on DRGs. Distinct clinicopathological characteristics, TIME, immunogenomic patterns and immunotherapy responses were identified across three clusters. A DRG signature composing of 12 genes were identified for predicting the survival of glioma patients and nomogram model integrating the risk score and multi-clinicopathological factors were constructed for clinical practice. Patients in high-risk group tended to get shorter overall survival and better response to immune checkpoint blockage therapy. We obtained 9 candidate drugs through comprehensive analysis of the differentially expressed genes between the low and high-risk groups in the model. Our findings indicated that the risk score may not only contribute to the determination of prognosis but also facilitate in the prediction of immunotherapy response in glioma patients.

Zhou Z ，Wei J ，Yang Z ，Bao Y ，Jiang W ，Lu B ，Wang W ，Li L ... -  《Scientific Reports》