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》

A Risk Score Signature Consisting of Six Immune Genes Predicts Overall Survival in Patients with Lower-Grade Gliomas.

Lower-grade gliomas (LGGs) are less aggressive with a long overall survival (OS) time span. Because of individualized genomic features, a prognostic system incorporating molecular signatures can more accurately predict OS. Differential expression analysis between LGGs and normal tissues was performed using the Gene Expression Omnibus (GEO) datasets (GSE4290 and GSE12657). Immune-related differentially expressed genes (ImmPort-DEGs) were analyzed for functional enrichment. The least absolute shrinkage and selection operator (LASSO) analysis was performed to develop an immune risk score signature (IRSS). We extracted information from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) to establish and validate the model. The relationship of model gene sets with immune infiltration was analyzed based on gene set variation analysis (GSVA) scores. Patients were divided into low- and high-risk groups based on the median score. The time-dependent receiver-operating characteristic (ROC) curve and the Kaplan-Meier curve were used to evaluate the model. Then, a precise prognostic nomogram was established, and its efficacy was verified. A total of 18 related immune genes were identified, building a 6-gene IRSS (BMP2, F2R, FGF13, PCSK1, PRKCB, and PTGER3). DEGs were enriched in T cell and NK cell regulatory pathways. Immune infiltration analysis confirmed that the gene signature correlated with a decrease in innate immune cells. In terms of model evaluation, ROC curves at 1, 3, and 5 years showed moderate predictive ability of IRSS (AUC = 0.930, 0.797, and 0.728). The Cox regression analysis revealed that IRSS was an independent prognostic factor, and the nomogram model had good predictive ability (C - index = 0.828). Meanwhile, the predictive power of IRSS was also confirmed in the training cohort. The Kaplan-Meier results showed that the prognosis of the high-risk group was significantly worse in all cohorts. IRSS may serve as a novel survival prediction tool in the classification of LGG patients.

Wu Y ，Peng Z ，Gu S ，Wang H ，Xiang W ... -  《-》

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 autophagic gene-based signature to predict the survival of patients with low-grade gliomas.

Since autophagy remains an important topic of investigation, the RNA-sequence profiles of autophagy-related genes (ARGs) can provide insights into predicting low-grade gliomas (LGG) prognosis. The RNA-seq profiles of autophagic genes and prognosis data of LGG were integrated from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) regression model were carried out to identify the differentially expressed prognostic autophagy-related genes. Then, the autophagic-gene signature was formed and verified in TCGA test set and external CGGA cohorts. Time-dependent receiver operating characteristic (ROC) was examined to test the accuracy of this signature feature. A nomogram was conducted to meet the needs of clinicians. Sankey diagrams were performed to visualize the relationship between the multigene signatures and clinic-pathological features. Twenty-four ARGs were finally identified most relevant to LGG prognosis. According to the specific prediction index formula, the patients were classified into low-risk or high-risk groups. Prognostic accuracy was proved by time-dependent ROC analysis, with AUC 0.9, 0.93, and 0.876 at the survival time of 2-, 3-, and 5-year, respectively, which was superior to the AUC of the isocitrate dehydrogenase (IDH) mutation. The result was confirmed while validated in the TCGA test set and external validation CGGA cohort. A nomogram was constructed to meet individual needs. With a visualization approach, Sankey diagrams show the relationship of the histological type, IDH status, and predict index. In TCGA and CGGA cohorts, both low-risk groups displayed better survival rate in LGG while histological type and IDH status did not show consistency results. 24-ARGs may play crucial roles in the progression of LGG, and LGG patients were effectively divided into low-risk and high-risk groups according to prognostic prediction. Overall, our study will provide novel strategies for clinical applications.

Chen J ，Li Y ，Han X ，Pan Y ，Qian X ... -  《Cancer Medicine》

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 ... -  《-》