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》

Prognostic Value of a Stemness Index-Associated Signature in Primary Lower-Grade Glioma.

As a prevalent and infiltrative cancer type of the central nervous system, the prognosis of lower-grade glioma (LGG) in adults is highly heterogeneous. Recent evidence has demonstrated the prognostic value of the mRNA expression-based stemness index (mRNAsi) in LGG. Our aim was to develop a stemness index-based signature (SI-signature) for risk stratification and survival prediction. Differentially expressed genes (DEGs) between LGG in the Cancer Genome Atlas (TCGA) and normal brain tissue samples from the Genotype-Tissue Expression (GTEx) project were screened out, and the weighted gene correlation network analysis (WGCNA) was employed to identify the mRNAsi-related gene sets. Meanwhile, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed for the functional annotation of the key genes. ESTIMATE was used to calculate tumor purity for acquiring the correct mRNAsi. Differences in overall survival (OS) between the high and low mRNAsi (corrected mRNAsi) groups were compared using the Kaplan Meier analysis. By combining the Lasso regression with univariate and multivariate Cox regression, the SI-signature was constructed and validated using the Chinese Glioma Genome Atlas (CGGA). There was a significant difference in OS between the high and low mRNAsi groups, which was also observed in the two corrected mRNAsi groups. Based on threshold limits, 86 DEGs were most significantly associated with mRNAsi via WGCNA. Seven genes (ADAP2, ALOX5AP, APOBEC3C, FCGRT, GNG5, LRRC25, and SP100) were selected to establish a risk signature for primary LGG. The ROC curves showed a fair performance in survival prediction in both the TCGA and the CGGA validation cohorts. Univariate and multivariate Cox regression revealed that the risk group was an independent prognostic factor in primary LGG. The nomogram was developed based on clinical parameters integrated with the risk signature, and its accuracy for predicting 3- and 5-years survival was assessed by the concordance index, the area under the curve of the time-dependent receiver operating characteristics curve, and calibration curves. The SI-signature with seven genes could serve as an independent predictor, and suggests the importance of stemness features in risk stratification and survival prediction in primary LGG.

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

Prognostic Model and Nomogram Construction Based on a Novel Ferroptosis-Related Gene Signature in Lower-Grade Glioma.

Background: Low-grade glioma (LGG) is considered a fatal disease for young adults, with overall survival widely ranging from 1 to 15 years depending on histopathologic and molecular subtypes. As a novel type of programmed cell death, ferroptosis was reported to be involved in tumorigenesis and development, which has been intensively studied in recent years. Methods: For the discovery cohort, data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to identify the differentially expressed and prognostic ferroptosis-related genes (FRGs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox were used to establish a prognostic signature with the above-selected FRGs. Then, the signature was developed and validated in TCGA and Chinese Glioma Genome Atlas (CGGA) databases. By combining clinicopathological features and the FRG signature, a nomogram was established to predict individuals' one-, three-, and five-year survival probability, and its predictive performance was evaluated by Harrell's concordance index (C-index) and calibration curves. Enrichment analysis was performed to explore the signaling pathways regulated by the signature. Results: A novel risk signature contains seven FRGs that were constructed and were used to divide patients into two groups. Kaplan-Meier (K-M) survival curve and receiver-operating characteristic (ROC) curve analyses confirmed the prognostic performance of the risk model, followed by external validation based on data from the CGGA. The nomogram based on the risk signature and clinical traits was validated to perform well for predicting the survival rate of LGG. Finally, functional analysis revealed that the immune statuses were different between the two risk groups, which might help explain the underlying mechanisms of ferroptosis in LGG. Conclusion: In conclusion, this study constructed a novel and robust seven-FRG signature and established a prognostic nomogram for LGG survival prediction.

Zhao J ，Liu Z ，Zheng X ，Gao H ，Li L ... -  《Frontiers in Genetics》

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》

The Systematic Landscape of Nectin Family and Nectin-Like Molecules: Functions and Prognostic Value in Low Grade Glioma.

Objective: Nectin and nectin-like molecules (Necls) are molecules that are involved in cell-cell adhesion and other vital cellular processes. This study aimed to determine the expression and prognostic value of nectin and Necls in low grade glioma (LGG). Materials and Methods: Differentially expressed nectin and Necls in LGG samples and the relationship of nectin family and Necls expression with prognosis, clinicopathological parameters, and survival were explored using The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Repository of Molecular Brain Neoplasia Data (REMBRANDT) databases. Univariate and multivariate Cox analysis models were performed to construct the prognosis-related gene signature. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves and multivariate Cox regression analysis, were utilized to evaluate the prognostic capacity of the four-gene signature. Gene ontology (GO)enrichment analysis and Gene Set Enrichment Analyses (GSEA) were performed to further understand the underlying molecular mechanisms. The Tumor Immune Estimation Resource (TIMER) was used to explore the relationship between the four-gene signature and tumor immune infiltration. Results: Several nectin and Necls were differentially expressed in LGG. Kaplan-Meier survival analyses and Univariate Cox regression showed patients with high expression of NECTIN2 and PVR and low expression of CADM2 and NECTIN1 had worse prognosis among TCGA, CGGA, and REMBRANDT database. Then, a novel four-gene signature was built for LGG prognosis prediction. ROC curves, KM survival analyses, and multivariate COX regression indicated the new signature was an independent prognostic indicator for overall survival. Finally, GSEA and GO enrichment analyses revealed that immune-related pathways participate in the molecular mechanisms. The risk score had a strong negative correlation with tumor purity and data of TIMER showed different immune cell proportions (macrophage and myeloid dendritic cell) between high- and low-risk groups. Additionally, signature scores were positively related to multiple immune-related biomarkers (IL 2, IL8 and IFNγ). Conclusion: Our results offer an extensive analysis of nectin and Necls levels and a four-gene model for prognostic prediction in LGG, providing insights for further investigation of CADM2, NECTIN1/2, and PVR as potential clinical and immune targets in LGG.

Han Y ，Zou C ，Zhu C ，Liu T ，Shen S ，Cheng P ，Cheng W ，Wu A ... -  《Frontiers in Genetics》