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.

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Identification and Validation of an 11-Ferroptosis Related Gene Signature and Its Correlation With Immune Checkpoint Molecules in Glioma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.652599 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zhuohui Chen ◽

Tong Wu ◽

Zhouyi Yan ◽

Mengqi Zhang

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Gene Signature ◽

Curve Analysis ◽

Regression Analyses ◽

Related Gene ◽

Roc Curve Analysis ◽

Small Molecule Drugs ◽

The Relationship ◽

Genome Atlas

BackgroundGlioma is the most common primary malignant brain tumor with significant mortality and morbidity. Ferroptosis, a novel form of programmed cell death (PCD), is critically involved in tumorigenesis, progression and metastatic processes.MethodsWe revealed the relationship between ferroptosis-related genes and glioma by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE16011, and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) datasets. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a ferroptosis-associated gene signature in the TCGA cohort. Glioma patients from the CGGA, GSE16011, and REMBRANDT cohorts were used to validate the efficacy of the signature. Receiver operating characteristic (ROC) curve analysis was applied to measure the predictive performance of the risk score for overall survival (OS). Univariate and multivariate Cox regression analyses of the 11-gene signature were performed to determine whether the ability of the prognostic signature in predicting OS was independent. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify the potential biological functions and pathways of the signature. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status. Finally, seven putative small molecule drugs were predicted by Connectivity Map.ResultsThe 11-gene signature was identified to divide patients into two risk groups. ROC curve analysis indicated the 11-gene signature as a potential diagnostic factor in glioma patients. Multivariate Cox regression analyses showed that the risk score was an independent predictive factor for overall survival. Functional analysis revealed that genes were enriched in iron-related molecular functions and immune-related biological processes. The results of ssGSEA indicated that the 11-gene signature was correlated with the initiation and progression of glioma. The small molecule drugs we selected showed significant potential to be used as putative drugs.Conclusionwe identified a novel ferroptosis-related gene signature for prognostic prediction in glioma patients and revealed the relationship between ferroptosis-related genes and immune checkpoint molecules.

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Clinicopathological signature and prognostic value of RNA:m5C methyltransferases in gliomas

10.21203/rs.2.22601/v1 ◽

2020 ◽

Author(s):

Peng Wang ◽

Kai Huang ◽

Miaojing Wu ◽

Qing Hu ◽

Chuming Tao ◽

...

Keyword(s):

Overall Survival ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Roc Curves ◽

Clinicopathological Features ◽

Gene Set Enrichment Analysis ◽

Consensus Clustering ◽

Score Model ◽

Genome Atlas

Abstract Background: Glioma is the most common primary intracranial tumor, accounting for the vast majority of intracranial malignant tumors. Aberrant expression of RNA:5-methylcytosine(m5C) methyltransferases has recently been the focus of research relating to the occurrence and progression of tumors. However, the prognostic value of RNA:m5C methyltransferases in glioma remains unclear. This study investigated RNA: m5C methyltransferase expression and defined its clinicopathological signature and prognostic value in gliomas. Methods: We systematically studied the RNA-sequence data of RNA:m5C methyltransferases underlying gliomas in the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets and identified different subtypes using Consensus clustering analysis. Gene Ontology (GO) and Gene Set Enrichment analysis (GSEA) was used to annotate the function of these genes. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm analyses were performed to construct the risk score model. Kaplan-Meier method and Receiver operating characteristic (ROC) curves were used to assess the overall survival of glioma patients. Additionally, Cox proportional regression model analysis was developed to address the connections between the risk scores and clinical factors. Results: Consensus clustering of RNA:m5C methyltransferases identified three clusters of gliomas with different prognostic and clinicopathological features. Meanwhile, Functional annotations demonstrated that RNA:m5C methyltransferases were significantly associated with the malignant progression of gliomas. Thereafter, five RNA:m5C methyltransferase genes were screened to construct a risk score model which can be used to predict not only overall survival but also clinicopathological features in gliomas. ROC curves revealed the significant prognostic ability of this signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for glioma outcome. Conclusion: We demonstrated the role of RNA:m5C methyltransferases in the initiation and progression of glioma. We have expanded on the understanding of the molecular mechanism involved, and provided a unique approach to predictive biomarkers and targeted therapy.

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Weighted Gene Coexpression Network Analysis Reveals Cancer Stem Cell-Associated Metabolic Gene Signature in Glioma

10.21203/rs.3.rs-205111/v1 ◽

2021 ◽

Author(s):

Yan Tang ◽

Yao Jiang ◽

Dan Zhang ◽

Jia Fan ◽

Juan Yao ◽

...

Keyword(s):

Stem Cell ◽

Network Analysis ◽

Risk Stratification ◽

Cancer Stem Cell ◽

Prognostic Model ◽

Cox Regression ◽

Roc Curves ◽

Gene Signature ◽

Metabolic Gene ◽

Genome Atlas

Abstract Background: Isocitrate dehydrogenase (IDH) mutant glioma patients have a favorable prognosis, accompanying with metabolic alterations and glioma cell dedifferentiation. Recently, mRNA expression-based stemness index (mRNAsi) characteristic relation to IDH status of gliomas has yet illuminated. Thus, we aimed to establish a cancer stem cell-associated metabolic gene signature for risk stratification of gliomas. Methods: The glioma samples came from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Next, we performed the differential expression analysis between IDH mutant and IDH wild-type gliomas and also conducted weighted gene correlation network analysis (WGCNA) for determining the modules associated with cancer stem cell trait. Subsequently, multivariate Cox regression analysis with the Akaike information criterion (AIC) algorithm was employed to establish a stemness-related metabolic gene signature, which was validated using time-dependent receiver operating characteristic (ROC) curves and concordance index (C-index). Also, we developed a nomogram based on clinical traits and prognostic model. Additionally, according to the results of immunohistochemistry (IHC) staining, the protein levels of gene signature were consistent with the genes expression’s direction.Results: Low expression of mRNAsi was capable of predicting the unfavourable OS of gliomas with a 5-year survival rate of 14.08%. The blue module and its 1466 genes were pertinent to mRNAsi characteristic. Next, Kaplan-Meier (KM) survival curves revealed that cancer stem cell-associated metabolic genes exerted impact on gliomas’ prognosis. Subsequently, univariate and multivariate Cox regression analyses were implemented, and gene signature (LCAT, UST, GALNT13, and SMPD3) was constructed, with C-index of 0.798 (95%CI: 0.769-0.827). Notably, the prognostic model presented a superior predictive value for gliomas’ survival, with the area under the curve (AUC) of ROC curves at 1-year, 3-year as well as 5-year time-point of 0.845, 0.85 and 0.811, respectively. And forest plot uncovered its role as a potential independent predictor for gliomas (HR=2.840, 95%CI: 1.961-4.113, P <0.001). Nomogram also presented superior predictive performance for gliomas’ OS. Conclusion: The gene signature (LCAT, UST, GALNT13, and SMPD3) can be used for risk stratification and also can serve as an independent prognostic factor of glioma patients.

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Identification of the Role and Clinical Prognostic Value of Target Genes of m6A RNA Methylation Regulators in Glioma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.709022 ◽

2021 ◽

Vol 9 ◽

Author(s):

Peilin Cong ◽

Tingmei Wu ◽

Xinwei Huang ◽

Huazheng Liang ◽

Xiaofei Gao ◽

...

Keyword(s):

Prognostic Value ◽

Target Genes ◽

Cox Regression ◽

Notch Pathway ◽

Gene Signature ◽

Rna Methylation ◽

Prognostic Gene ◽

Cell Experiment ◽

M6a Rna Methylation ◽

Genome Atlas

m6A RNA methylation regulators can regulate the growth, progression, and invasion of glioma cells by regulating their target genes, which provides a reliable support for the m6A regulator–target axes as the novel therapeutic targets and clinical prognostic signature in glioma. This study aimed to explore the role and prognostic value of m6A RNA methylation regulators and their targets. Expression profiles and clinicopathological data were obtained from the Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Clinical Proteome Tumor Analysis Consortium (CPTAC) datasets. Differential expression and correlation analyses were performed between normal and glioma tissues at mRNA and protein levels. Univariate Cox regression, survival, and Lasso Cox regression analyses were conducted to identify and establish the prognostic gene signature. Kaplan–Meier curve, multivariate Cox regression analysis, and ROC were utilized to evaluate the prognostic capacity of the prognostic gene signature. The correlation analysis, systematic bioinformatics analysis, and cell experiment were performed to further understand the potential underlying molecular mechanisms and drug sensitivity. Our results suggested that IGF2BP2, KIAA1429, METTL16, and METTL3, as well as 208 targets are involved in the occurrence of glioma, GBM, and LGG. YTHDF1 and 78 targets involved the occurrence of glioma and GBM, not LGG, among which 181 genes were associated with overall survival. From other findings and our cell experiment results, we demonstrated that METTL3 can activate Notch pathway and facilitate glioma occurrence through regulating its direct targets NOTCH3, DLL3, and HES1, and Notch pathway genes may serve as the potential treatment targets for glioma. Our study established and validated a seven-gene signature comprising METTL3, COL18A1, NASP, PHLPP2, TIMP1, U2AF2, and VEGFA, with a good capability for predicting glioma survival, which may guide therapeutic customization and clinical decision-making. These genes were identified to influence 81 anticancer drug responses, which further contributes to the early phase clinical trials of drug development.

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Prognostic value of hypoxia gene expression in bladder cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.6_suppl.548 ◽

2020 ◽

Vol 38 (6_suppl) ◽

pp. 548-548

Author(s):

Hyun Chang ◽

Seung-Hyun Lee ◽

Taeryool Koo ◽

Moon Ho Kim ◽

Soo-Yoon Sung

Keyword(s):

Bladder Cancer ◽

Urothelial Carcinoma ◽

Cancer Patients ◽

Prognostic Value ◽

Cox Regression ◽

Gene Signature ◽

Survival Curves ◽

Kaplan Meier ◽

Bladder Urothelial Carcinoma

548 Background: The prognostic value of hypoxia in bladder cancer remains unknown. We aimed to evaluate the potential role of hypoxia gene signature as prognostic factors in bladder cancer patients. Methods: We investigated the hypoxia gene signature and clinicopathologic features of The Cancer Genome Atlas (TCGA) bladder urothelial carcinoma (n = 408) using the Kaplan-Meier survival curves and multivariate Cox regression analyses. The clinicopathologic data and the processed data of hypoxia gene signature were obtained from TCGA Bladder urothelial carcinoma database. Results: Hypoxia gene signature score was significantly associated with overall survival (OS) and progression-free survival (PFS). Higher score resulted in shorter OS and PFS in Kaplan-Meier survival curves with Log-rank test ( P < 0.01 and P <0.05, respectively). In multivariate analysis containing clinical prognostic variables, higher hypoxia gene signature score predicted poor OS (adjusted HR= 1.58, 95% CI 1.15 - 2.19; P <0.01). Conclusions: Hypoxia gene signature was an independent prognostic factor in bladder cancer. Prospective studies are needed to evaluate the prognostic role of hypoxia in bladder cancer patients.

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A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

Technology in Cancer Research & Treatment ◽

10.1177/1533033821992084 ◽

2021 ◽

Vol 20 ◽

pp. 153303382199208

Author(s):

Wentao Liu ◽

Jiaxuan Zou ◽

Rijun Ren ◽

Jingping Liu ◽

Gentang Zhang ◽

...

Keyword(s):

Risk Score ◽

Poor Prognosis ◽

Cox Regression ◽

Gene Signature ◽

Low Grade Glioma ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Normal Brain ◽

Cox Regression Analysis ◽

Genome Atlas

Aim: Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions. Methods: survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score. Results: We identified a total of 1489 genes significantly correlated with patients’ prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls. Conclusions: The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

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Rab25 Acts as a Promising Novel Bio-Marker in the Prognosis of Patients with Hepatocellular Carcinoma

10.21203/rs.3.rs-43510/v1 ◽

2020 ◽

Author(s):

Zhong Dai ◽

Ke-Qing Yao ◽

Xing-Sheng Hu ◽

Yi-Qun Li ◽

Yu-Tao Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Value ◽

Cox Regression ◽

Rank Test ◽

Chi Square ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Kaplan Meier ◽

Chi Square Test ◽

The Relationship

Abstract Background: Rab25 was indicated to be involved in several human tumors. However, the clinical significance of Rab25 in hepatocellular carcinoma (HCC) was still unclear. The purpose of this study was to investigate the expression and prognostic value of Rab25 in HCC.Methods: The relative mRNA expression levels of Rab25 in HCC tissues and adjacent normal tissues were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Chi-square test was used to analyze the relationship between Rab25 expression and clinical characteristics of patients. The prognostic value of Rab25 in HCC was estimated through Kaplan-Meier method and cox regression analysis.Results: Rab25 gene expression level was significantly higher in HCC tissues than that in normal tissues (P<0.001). Importantly, the increased Rab25 expression was closely associated with TNM stage (P=0.024), metastasis (P=0.022) and invasion classification (P=0.039). Moreover, patients with high Rab25 expression tended to have obviously shorter overall survival than those with low expression of Rab25 (log rank test, P<0.001) via Kaplan-Meier analysis. Univariate and multivariate cox regression analyses revealed that Rab25 was an independent prognostic factor of HCC.Conclusions: Rab25 is up-regulated in HCC and contributes to the progression of this tumor. What’s more, Rab25 may be a potential bio-marker for the prognosis of HCC.

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The Relationship Between microRNA-195 and the Prognosis of Cervical Cancer

10.21203/rs.3.rs-58914/v1 ◽

2020 ◽

Author(s):

Shuangqing Cao ◽

Lei Zheng

Keyword(s):

Cervical Cancer ◽

Overall Survival ◽

Prognostic Value ◽

Cox Regression ◽

Rank Test ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Kaplan Meier ◽

Cancer Tissues ◽

The Relationship

Abstract Background: MicroRNA-195 (miR-195), a tumor suppressor, had reported to be involved in carcinogenesis and the progression of some cancers. However, the prognostic value of miR-195 in cervical cancer remained unclear. The purpose of this study was to detect the expression of miR-195 in cervical cancer tissues and to investigate its correlation with tumor progression and prognosis.Methods: Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the relative mRNA expression of miR-195 in cervical cancer tissues and corresponding adjacent normal tissues. The relationship between miR-195 expression and clinical characteristics of patients was analyzed by chi-square test. Kaplan-Meier method was applied to compare the overall survival, and the prognostic value of miR-195 was estimated via cox regression analysis.Results: Compared with normal tissues, miR-195 expression was significantly down-regulated in cervical cancer tissues (P < 0.001). Importantly, decreased expression of miR-195 was closely associated with FIGO stage, lymph node metastasis and vascular invasion (P < 0.05). Additionally, Kaplan-Meier analysis indicated that patients with high miR-195 expression had obviously longer overall survival than those with low miR-195 expression (log rank test, P = 0.001). And miR-195 was an independent prognostic factor of cervical cancer patients via univariate and multivariate cox regression analyses.Conclusions: Decreased expression of miR-195 is associated with the progression of cervical cancer. And miR-195 may have potency to predict the prognosis of cervical cancer.

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Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.799223 ◽

2022 ◽

Vol 11 ◽

Author(s):

Xiya Jia ◽

Bing Chen ◽

Ziteng Li ◽

Shenglin Huang ◽

Siyuan Chen ◽

...

Keyword(s):

Poor Prognosis ◽

Prognostic Value ◽

Drug Sensitivity ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

Regression Analyses ◽

Prognostic Signature ◽

Kaplan Meier ◽

Drug Sensitivity Prediction

BackgroundGastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC.MethodsThe gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort.ResultsA prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson’s correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients’ OS, DFS, and susceptibility to adjuvant chemotherapy.ConclusionsOur SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.

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A Novel Signature Constructed by RNA-Binding Protein Coding Genes to Improve Overall Survival Prediction of Glioma Patients

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2020.588368 ◽

2021 ◽

Vol 8 ◽

Author(s):

Zewei Tu ◽

Lei Shu ◽

Jingying Li ◽

Lei Wu ◽

Chuming Tao ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Signaling Pathway ◽

Prognostic Value ◽

Rna Binding ◽

Cox Regression ◽

Survival Prediction ◽

Low Grade ◽

Cox Regression Analysis ◽

Genome Atlas

RNA binding proteins (RBPs) have been reported to be involved in cancer malignancy but related functions in glioma have been less studied. Herein, we screened 14 prognostic RBP genes and constructed a risk signature to predict the prognosis of glioma patients. Univariate Cox regression was used to identify overall survival (OS)-related RBP genes. Prognostic RBP genes were screened and used to establish the RBP-signature using the least absolute shrinkage and selection operator (Lasso) method in The Cancer Genome Atlas (TCGA) cohort. The 14 RBP genes signature showed robust and stable prognostic value in the TCGA training (n = 562) cohort and in three independent validation cohorts (Chinese Glioma Genome Atlas [CGGA]seq1, CGGAseq2, and GSE16011 datasets comprising 303, 619, and 250 glioma patients, respectively). Risk scores were calculated for each patient and high-risk gliomas were defined by the median risk score in each cohort. Survival analysis in subgroups of glioma patients showed that the RBP-signature retained its prognostic value in low-grade gliomas (LGGs) and glioblastomas (GBM)s. Univariate and multivariate Cox regression analysis in each dataset and the meta cohort revealed that the RBP-signature stratification could efficiently recognize high-risk gliomas [Hazard Ratio (HR):3.662, 95% confidence interval (CI): 3.187–4.208, p < 0.001] and was an independent prognostic factor for OS (HR:1.594, 95% CI: 1.244–2.043, p < 0.001). Biological process and KEGG pathway analysis revealed the RBP gene signature was associated with immune cell activation, the p53 signaling pathway, and the PI3K-Akt signaling pathway and so on. Moreover, a nomogram model was constructed for clinical application of the RBP-signature, which showed stable predictive ability. In summary, the RBP-signature could be a robust indicator for prognostic evaluation and identifying high-risk glioma patients.

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