Identification of Iron Metabolism-Related Genes as Prognostic Indicators for Lower-Grade Glioma.

Lower-grade glioma (LGG) is characterized by genetic and transcriptional heterogeneity, and a dismal prognosis. Iron metabolism is considered central for glioma tumorigenesis, tumor progression and tumor microenvironment, although key iron metabolism-related genes are unclear. Here we developed and validated an iron metabolism-related gene signature LGG prognosis. RNA-sequence and clinicopathological data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) were downloaded. Prognostic iron metabolism-related genes were screened and used to construct a risk-score model via differential gene expression analysis, univariate Cox analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO)-regression algorithm. All LGG patients were stratified into high- and low-risk groups, based on the risk score. The prognostic significance of the risk-score model in the TCGA and CGGA cohorts was evaluated with Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analysis. Risk- score distributions in subgroups were stratified by age, gender, the World Health Organization (WHO) grade, isocitrate dehydrogenase 1 (IDH1) mutation status, the O6-methylguanine-DNA methyl-transferase (MGMT) promoter-methylation status, and the 1p/19q co-deletion status. Furthermore, a nomogram model with a risk score was developed, and its predictive performance was validated with the TCGA and CGGA cohorts. Additionally, the gene set enrichment analysis (GSEA) identified signaling pathways and pathological processes enriched in the high-risk group. Finally, immune infiltration and immune checkpoint analysis were utilized to investigate the tumor microenvironment characteristics related to the risk score. We identified a prognostic 15-gene iron metabolism-related signature and constructed a risk-score model. High risk scores were associated with an age of > 40, wild-type IDH1, a WHO grade of III, an unmethylated MGMT promoter, and 1p/19q non-codeletion. ROC analysis indicated that the risk-score model accurately predicted 1-, 3-, and 5-year overall survival rates of LGG patients in the both TCGA and CGGA cohorts. KM analysis showed that the high-risk group had a much lower overall survival than the low-risk group (P < 0.0001). The nomogram model showed a strong ability to predict the overall survival of LGG patients in the TCGA and CGGA cohorts. GSEA analysis indicated that inflammatory responses, tumor-associated pathways, and pathological processes were enriched in high-risk group. Moreover, a high risk score correlated with the infiltration immune cells (dendritic cells, macrophages, CD4+ T cells, and B cells) and expression of immune checkpoint (PD1, PDL1, TIM3, and CD48). Our prognostic model was based on iron metabolism-related genes in LGG, can potentially aid in LGG prognosis, and provides potential targets against gliomas.

Xu S ，Wang Z ，Ye J ，Mei S ，Zhang J ... -  《Frontiers in Oncology》

Interferon gamma-related gene signature based on anti-tumor immunity predicts glioma patient prognosis.

Background: Glioma is the most common primary tumor of the central nervous system. The conventional glioma treatment strategies include surgical excision and chemo- and radiation-therapy. Interferon Gamma (IFN-γ) is a soluble dimer cytokine involved in immune escape of gliomas. In this study, we sought to identify IFN-γ-related genes to construct a glioma prognostic model to guide its clinical treatment. Methods: RNA sequences and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the China Glioma Genome Atlas (CGGA). Using univariate Cox analysis and the Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm, IFN-γ-related prognostic genes were selected to construct a risk scoring model, and analyze its correlation with the clinical features. A high-precision nomogram was drawn to predict prognosis, and its performance was evaluated using calibration curve. Finally, immune cell infiltration and immune checkpoint molecule expression were analyzed to explore the tumor microenvironment characteristics associated with the risk scoring model. Results: Four out of 198 IFN-γ-related genes were selected to construct a risk score model with good predictive performance. The expression of four IFN-γ-related genes in glioma tissues was significantly increased compared to normal brain tissue (p < 0.001). Based on ROC analysis, the risk score model accurately predicted the overall survival rate of glioma patients at 1 year (AUC: The Cancer Genome Atlas 0.89, CGGA 0.59), 3 years (AUC: TCGA 0.89, CGGA 0.68), and 5 years (AUC: TCGA 0.88, CGGA 0.70). Kaplan-Meier analysis showed that the overall survival rate of the high-risk group was significantly lower than that of the low-risk group (p < 0.0001). Moreover, high-risk scores were associated with wild-type IDH1, wild-type ATRX, and 1P/19Q non-co-deletion. The nomogram predicted the survival rate of glioma patients based on the risk score and multiple clinicopathological factors such as age, sex, pathological grade, and IDH Status, among others. Risk score and infiltrating immune cells including CD8 T-cell, resting CD4 memory T-cell, regulatory T-cell (Tregs), M2 macrophages, resting NK cells, activated mast cells, and neutrophils were positively correlated (p < 0.05). In addition, risk scores closely associated with expression of immune checkpoint molecules such as PD-1, PD-L1, CTLA-4, LAG-3, TIM-3, TIGIT, CD48, CD226, and CD96. Conclusion: Our risk score model reveals that IFN-γ -associated genes are an independent prognostic factor for predicting overall survival in glioma, which is closely associated with immune cell infiltration and immune checkpoint molecule expression. This model will be helpful in predicting the effectiveness of immunotherapy and survival rate in patients with glioma.

Zhang Z ，Shen X ，Tan Z ，Mei Y ，Lu T ，Ji Y ，Cheng S ，Xu Y ，Wang Z ，Liu X ，He W ，Chen Z ，Chen S ，Lv Q ... -  《Frontiers in Genetics》

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma.

In the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma. Stromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model. We obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells. The PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

Qiu H ，Li Y ，Cheng S ，Li J ，He C ，Li J ... -  《Frontiers in Oncology》

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma.

Despite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG. RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER. Noted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan-Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4+ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8+T cells were significant higher in high-risk group in both TCGA and CGGA cohorts. The present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.

Zhang M ，Wang X ，Chen X ，Zhang Q ，Hong J ... -  《Frontiers in Genetics》

A 1p/19q Codeletion-Associated Immune Signature for Predicting Lower Grade Glioma Prognosis.

Lower grade gliomas (LGGs) with codeletion of chromosomal arms 1p and 19q (1p/19 codeletion) have a favorable outcome. However, its overall survival (OS) varies. Here, we established an immune signature associated with 1p/19q codeletion for accurate prediction of prognosis of LGGs. The Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases with RNA sequencing and corresponding clinical data were dichotomized into training group and testing group. The immune-related differentially expressed genes (DEGs) associated with 1p/19q codeletion were screened using Cox proportional hazards regression analyses. A prognostic signature was established using dataset from CGGA and tested in TCGA database. Subsequently, we explored the correlation between the prognostic signature and immune response. Thirteen immune genes associated with 1p/19q codeletion were used to construct a prognostic signature. The 1-, 3-, 5-year survival rates of the low-risk group were approximately 97%, 89%, and 79%, while those of the high-risk group were 81%, 50% and 34%, respectively, in the training group. The nomogram which comprised age, WHO grade, primary or recurrent types, 1p/19q codeletion status and risk score provided accurate prediction for the survival rate of glioma. DEGs that were highly expressed in the high-risk group clustered with many immune-related pathways. Immune checkpoints including TIM3, PD1, PDL1, CTLA4, TIGIT, MIR155HG, and CD48 were correlated with the risk score. VAV3 and TNFRFSF11B were found to be candidate immune checkpoints associated with prognosis. The 1p/19q codeletion-associated immune signature provides accurate prediction of OS. VAV3 and TNFRFSF11B are novel immune checkpoints.

Xu J ，Liu F ，Li Y ，Shen L ... -  《-》