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 ... -  《-》

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》

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》

Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma.

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR = 3.192, 95%CI = 2.182-4.670; CGGA: HR = 1.922, 95%CI = 1.431-2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

Zhao J ，Wang L ，Wei B 《-》

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 ... -  《-》