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.

Download Full-text

Integrin Alpha-2 as a Potential Prognostic and Predictive Biomarker for Patients With Lower-Grade Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.738651 ◽

2021 ◽

Vol 11 ◽

Author(s):

Li Lin ◽

Kai Huang ◽

Zewei Tu ◽

Xingen Zhu ◽

Jingying Li ◽

...

Keyword(s):

Enrichment Analysis ◽

Clinical Information ◽

Predictive Biomarker ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Western Blot Assay ◽

Clinical Model ◽

Cell Functions ◽

Novel Target ◽

Genome Atlas

Diffuse gliomas are the most common malignant brain tumors with the highest mortality and recurrence rate in adults. Integrin alpha-2 (ITGA2) is involved in a series of biological processes, including cell adhesion, stemness regulation, angiogenesis, and immune/blood cell functions. The role of ITGA2 in lower-grade gliomas (LGGs) is not well defined. Firstly, we downloaded RNA sequencing and relevant clinical information from The Cancer Genome Atlas cohort, the Chinese Glioma Genome Atlas cohort, and related immune cohorts. Next, prognosis analysis, difference analysis, clinical model construction, enrichment analysis, and immune infiltration analysis are performed for this study. These analyses indicated that ITGA2 may have clinical application value and research value in LGG immunotherapy. We also detected the mRNA and protein expression of ITGA2 in three LGG cell lines and normal glial cells using quantitative real-time polymerase chain reaction assay and western blot assay. Our study not only offers a novel target for LGG immunotherapy but also can better comprehend the mechanism of the development and progression of patients with LGG. This study revealed that ITGA2 may be a potential prognostic and predictive biomarker for LGG, which can bring new insights into targeted immunotherapy.

Download Full-text

The Prognostic Value of Risk Score Based on Immunogenenomic Landscape Analysis in Glioma

10.21203/rs.2.20513/v1 ◽

2020 ◽

Author(s):

Haitao Luo ◽

Kai Huang ◽

Chuming Tao ◽

Mioaojing Wu ◽

Minhua Ye ◽

...

Keyword(s):

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Low Grade ◽

Clinical Prognosis ◽

Cox Analysis ◽

Genome Atlas

Abstract Background: Glioma is a lethal intracranial tumor, and inflammation plays an important role in the initiation and development of glioma. Hence, there is an urgent need to conduct a bioinformatics analysis of immune-related genes (IRGs) for glioma. The present study aims to explore the association of the risk score with clinical outcomes and predict the prognosis with glioma. Methods: In The Cancer Genome Atlas (TCGA) database, 462 low grade glioma (LGG) samples and 166 glioblastoma (GBM) samples were reviewed, and IRGs correlated with the prognosis were selected by performing a survival analysis and establishing a Cox regression model. The potential molecular mechanism of these IRGs were also explored with assistance of computational biology. The risk score based on seven survival-associated IRGs was determined with the help of the multivariable Cox analysis, the patients were divided into two subgroups according to their risk score. Results: It was found that these differentially expressed IRGs were involved with the cytokine-cytokine receptor through functional enrichment analysis. The risk score based on the seven IRGs (SSTR5、CXCL10、CCL13、SAA1、CCL21、CCL27 and HTR1A) performed well in predicting patient’s the overall survival (OS), and correlated with age, 1p/19q codeletion status, IDH status, and WHO grades, both in the training (TCGA) datasets and the validation ((Chinese Glioma Genome Atlas) CGGA) datasets. The risk score also could reflect infiltration through several types of immune cells. Conclusions: This present study screened some IRGs associated with the patient’s clinical characteristic and prognosis, connect to the immune repertoire, demonstrated the importance of the risk score as a promising biomarker for estimating the clinical prognosis of glioma.

Download Full-text

Development of a Prognostic Five-Gene Signature with Radiotherapy Guidance Significance for Diffuse Lower-Grade Glioma Patients Based on Large-Scale Sequencing Data

10.21203/rs.3.rs-32321/v1 ◽

2020 ◽

Author(s):

Qiang Zhang ◽

Shun-Bin Luo ◽

Fu-Chen Xie ◽

Xiao-Jun Liu ◽

Ren-ai Xu

Keyword(s):

Large Scale ◽

Expression Profiles ◽

Predictive Performance ◽

Gene Signature ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Good Prospect ◽

Sequencing Data ◽

Genome Atlas

Abstract Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein coding genes (PCGs) is widely used as tumor markers. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with significantly different overall survival (Log Rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.Conclusions: The new five-PCG signature is a reliable prognostic marker with radiotherapy guidance significance for LGG patients and has a good prospect in clinical application.

Download Full-text

Validation of the Functions and Prognostic Values of Synapse Associated Proteins in Lower-Grade Glioma

10.21203/rs.3.rs-80139/v1 ◽

2020 ◽

Author(s):

Han Lin ◽

Yong Yang ◽

Chongxian Hou ◽

Jiantao Zheng ◽

Guangzhao Lv ◽

...

Keyword(s):

Survival Analysis ◽

Prognostic Significance ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Models ◽

Normal Brain ◽

Lower Grade ◽

Associated Proteins ◽

Genome Atlas

Abstract BackgroundSynapse and synapse associated proteins (SAPs) play critical roles in various neurodegeneration diseases and brain tumors. However, in lower-grade gliomas (LGG), SAPs have not been explored systematically. Herein, we are going to explore SAPs expression profile and its clinicopathological significance in LGG which can offer new insights to glioma therapy. MethodIn this study, we used five sources including, Venkatesh, Shen, Colón, Jüttner R, Gene Ontology (GO) project to integrate a list of SAPs that covered 231 proteins with synaptogenesis activity and post synapse formation. The LGG RNA-seq data were downloaded from gene expression omnibus (GEO) database, The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA). The differentially expressed SAPs were filtered out and constructed PPI to search for key modules and SAPs. Then, using Kaplan–Meier survival analysis, least absolute shrinkage and selection operator (LASSO), and multicox regression analysis, the prognostic significance of these key SAPs was evaluated. CGGA database, Human Protein Altas (HPA) and quantitative real-time PCR were used to verify our findings. ResultData from function enrichment analysis revealed functions of differentially expressed SAPs in synapse organization and glutamatergic receptor pathway in LGGs. Survival analysis revealed four SAPs, GRIK2, GABRD, GRID2, ARC that were correlate with the prognosis of LGG patients and used to construct the prognostic models. Among them, the expression of GABRD was lower in glioma tissue than normal brain tissue, but higher in seizure LGG patients than non-seizure patients. The four-SAPs signature was revealed as an independent prognostic factor in gliomas. ConclusionOur study presented a novel strategy to assess the prognostic risks of LGGs, based on the expression of SAPs. Also, we revealed that several SAPs upregulated in patients with seizures, indicating that they are linked to the pathogenesis of seizures in glioma patients.

Download Full-text

Identification of an Autophagy-Related 10-lncRNA-mRNA Signature for Distinguishing Glioblastoma Multiforme from Lower‑Grade Glioma and Prognosis Prediction

10.21203/rs.3.rs-51234/v1 ◽

2020 ◽

Author(s):

Bo Wei ◽

Le Wang ◽

Jingwei Zhao

Keyword(s):

Prognostic Factors ◽

Risk Score ◽

Clinical Decision Making ◽

Survival Curve ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Lower Grade ◽

Increased Risk ◽

Tcga Dataset ◽

Genome Atlas

Abstract Background: Autophagy provides the nutrients and energy for tumor growth, invasion and metastasis. Theoretically, autophagy-related mRNAs and regulatory long non-coding RNAs (lncRNAs) may represent promising biomarkers to predict tumor progression and poor prognosis. Our study aims to develop an autophagy-related signature to distinguish glioblastoma (GBM) from lower-grade gliomas (LGG) and predict overall survival (OS).Methods: The expression profile of GBM and LGG was collected from the Chinese Glioma Genome Atlas (CGGA) database that was used to identify differentially expressed genes (DEGs) and lncRNAs (DELs). The autophagy-related genes were obtained from the Human Autophagy Database. The autophagy-related DELs were identified by a co-expression network with DEGs. These DEGs and DELs underwent univariate and multivariate analyses to screen prognostic genes. They were entered into the Logit regression model to identify the GBM feature genes. The prognostic signature was evaluated by survival curve analyses and validated using The Cancer Genome Atlas (TCGA) dataset. The prognostic model and clinicopathological parameters were integrated to construct the nomogram.Results: A total of 131 autophagy-related DEGs and 54 autophagy-related DELs were identified. Ten of them were demonstrated as independent prognostic factors and could distinguish GBM from LGG, with the accuracy of 0.891 using CGGA dataset and 0.790 using TCGA dataset. The risk score was established based on these 10 genes. Patients with higher risk score were at an increased risk of developing GBM (49.7% vs 21.3%) and worse OS prognosis than those in low risk group. The predictive accuracy was 0.840 and 0.744 for CGGA and TCGA dataset, respectively. Multivariate analysis showed age, recurrence, IDH mutation and risk score status were independent prognostic factors and thus they were used to build a nomogram which showed the highest predictive power than other factors.Conclusion: The established nomogram may aid the clinical decision making of personalized treatment.

Download Full-text

CTNI-39. IDENTIFICATION OF AN AUTOPHAGY-RELATED GENE SIGNATURE AND ESTABLISHMENT OF A NOMOGRAM PREDICTING SURVIVAL OF LOWER-GRADE GLIOMA

Neuro-Oncology ◽

10.1093/neuonc/noab196.264 ◽

2021 ◽

Vol 23 (Supplement_6) ◽

pp. vi68-vi68

Author(s):

Lei Wen ◽

hui Wang ◽

Mingyao Lai ◽

Changguo Shan ◽

Linbo Cai

Keyword(s):

Risk Stratification ◽

Cox Regression ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Survival Prediction ◽

Lower Grade ◽

Related Gene ◽

Prognostic Signature ◽

Genome Atlas

Abstract OBJECTIVE The aim of our study was to establish an autophagy-related signature for individualized risk stratification and prognosis prediction in LGG. METHODS RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. The 232 ARGs were obtained from the Human Autophagy Database (HADb). Univariate and Lasso regression were employed to identify differentially expressed autophagy-related genes (ARGs) and establish a prognostic signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index) and calibration curve. RESULTS Fifty-three autophagy-related DEGs were identified. Four autophagy-related genes (DIRAS3, GNAI3, PTK6, and BIRC5) were selected to establish the prognostic signature and verified in the CGGA validation cohorts. Univariate and multivariate Cox regression indicated that the autophagy signature (HR, 95%CI, P) was an independent predictor of prognosis in LGG. Finally, a prognostic nomogram incorporating age, grade, targeted therapy, new event, tumor status and autophagy signature achieved excellent predicative performance (AUC 0.907, 0.865 and 0.858 for 1-year, 3-year and 5-year survival, respectively) verified by Time-dependent ROC, C-index (0.844, 95% CI, 0.799 to 0.889; P = 1.01e-12) and calibration plots. CONCLUSION The present study constructed a robust four autophagy-related gene signature. A prognostic nomogram in risk stratification and prediction of overall survival in LGG was established. The findings may be beneficial to individualized survival prediction and medical decision-making for LGG.

Download Full-text

Development of a Prognostic Five-Gene Signature for Diffuse Lower-Grade Glioma Patients

Frontiers in Neurology ◽

10.3389/fneur.2021.633390 ◽

2021 ◽

Vol 12 ◽

Author(s):

Qiang Zhang ◽

Wenhao Liu ◽

Shun-Bin Luo ◽

Fu-Chen Xie ◽

Xiao-Jun Liu ◽

...

Keyword(s):

Expression Profiles ◽

Clinical Information ◽

Predictive Performance ◽

Risk Groups ◽

Gene Signature ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Lower Grade ◽

Good Prospect ◽

Genome Atlas

Background: Diffuse lower-grade gliomas (LGGs) are infiltrative and heterogeneous neoplasms. Gene signature including multiple protein-coding genes (PCGs) is widely used as a tumor marker. This study aimed to construct a multi-PCG signature to predict survival for LGG patients.Methods: LGG data including PCG expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Survival analysis, receiver operating characteristic (ROC) analysis, and random survival forest algorithm (RSFVH) were used to identify the prognostic PCG signature.Results: From the training (n = 524) and test (n = 431) datasets, a five-PCG signature which can classify LGG patients into low- or high-risk group with a significantly different overall survival (log rank P < 0.001) was screened out and validated. In terms of prognosis predictive performance, the five-PCG signature is stronger than other clinical variables and IDH mutation status. Moreover, the five-PCG signature could further divide radiotherapy patients into two different risk groups. GO and KEGG analysis found that PCGs in the prognostic five-PCG signature were mainly enriched in cell cycle, apoptosis, DNA replication pathways.Conclusions: The new five-PCG signature is a reliable prognostic marker for LGG patients and has a good prospect in clinical application.

Download Full-text

Tumor Mutational Burden Predicts Survival In Patients With Low Grade Gliomas Expressing Mutated IDH1

10.1101/2020.01.20.20016766 ◽

2020 ◽

Author(s):

Mahmoud S Alghamri ◽

Rohit Thalla ◽

Ruthvik Avvari ◽

Ali Dabaja ◽

Ayman Taher ◽

...

Keyword(s):

Median Survival ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Low Grade Gliomas ◽

Mutational Burden ◽

Tumor Mutational Burden ◽

Genome Atlas

ABSTRACTGliomas are the most common primary brain tumors. High Grade Gliomas have a median survival of 18 months, while Low Grade Gliomas (LGG) have a median survival of ∼7.3 years. Seventy-six percent of patients with LGG express mutated isocitrate dehydrogenase (mIDH1) enzyme (IDH1R132H). Survival of these patients ranges from 1-15 years, and tumor mutational burden ranges from 8 to 447 total somatic mutations per tumor. We tested the hypothesis that the tumor mutational burden would predict survival of patients with tumors bearing mIDH1R132H. We analyzed the effect of tumor mutational burden on patients’ survival using clinical and genomic data of 1199 glioma patients from The Cancer Genome Atlas and validated our results using the Chinese Glioma Genome Atlas. High tumor mutational burden negatively correlates with survival of patients with LGG harboring IDH1R132H (p<0.0001). This effect was significant for both Oligodendroglioma and Astrocytoma LGG-mIDH1 patients. In the TCGA data, median survival of the high mutational burden group was 76 months, while in the low mutational burden group it was 136 months; p<0.0001. There was no differential representation of frequently mutated genes (e.g., TP53, ATRX, CIC, FUBP) in either group. Gene set enrichment analysis revealed an enrichment in Gene Ontologies related to Cell cycle, DNA damage response in high vs low tumor mutational burden. Finally, we identified a 19 gene signature that predicts survival for patients from both databases. In summary, we demonstrate that tumor mutational burden is a powerful, robust, and clinically relevant prognostic factor of median survival in mIDH1 patients.

Download Full-text

Validation of the Functions and Prognostic Values of Synapse Associated Proteins in Lower-Grade Glioma

10.21203/rs.3.rs-80139/v2 ◽

2020 ◽

Author(s):

Han Lin ◽

Yong Yang ◽

Chongxian Hou ◽

Jiantao Zheng ◽

Guangzhao Lv ◽

...

Keyword(s):

Survival Analysis ◽

Prognostic Significance ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Models ◽

Normal Brain ◽

Lower Grade ◽

Associated Proteins ◽

Genome Atlas

Abstract Background: Synapse and synapse associated proteins (SAPs) play critical roles in various neurodegeneration diseases and brain tumors. However, in lower-grade gliomas (LGG), SAPs have not been explored systematically. Herein, we are going to explore SAPs expression profile and its clinicopathological significance in LGG which can offer new insights to glioma therapy. Method: In this study, we used five sources including, Venkatesh, Shen, Colón, Jüttner R, Gene Ontology (GO) project to integrate a list of SAPs that covered 231 proteins with synaptogenesis activity and post synapse formation. The LGG RNA-seq data were downloaded from gene expression omnibus (GEO) database, The Cancer Genome Atlas (TCGA), and Chinese Glioma Genome Atlas (CGGA). The differentially expressed SAPs were filtered out and constructed PPI to search for key modules and SAPs. Then, using Kaplan–Meier survival analysis, least absolute shrinkage and selection operator (LASSO), and multicox regression analysis, the prognostic significance of these key SAPs was evaluated. CGGA database, Human Protein Altas (HPA) and quantitative real-time PCR were used to verify our findings. Result: Data from function enrichment analysis revealed functions of differentially expressed SAPs in synapse organization and glutamatergic receptor pathway in LGGs. Survival analysis revealed four SAPs, GRIK2, GABRD, GRID2, ARC that were correlate with the prognosis of LGG patients and used to construct the prognostic models. Among them, the expression of GABRD was lower in glioma tissue than normal brain tissue, but higher in seizure LGG patients than non-seizure patients. The four-SAPs signature was revealed as an independent prognostic factor in gliomas.Conclusion: Our study presented a novel strategy to assess the prognostic risks of LGGs, based on the expression of SAPs. Also, we revealed that several SAPs upregulated in patients with seizures, indicating that they are linked to the pathogenesis of seizures in glioma patients.

Download Full-text

Clinical Significance and Systematic Expression Analysis of the Thyroid Receptor Interacting Protein 13 (TRIP13) as Human Gliomas Biomarker

Cancers ◽

10.3390/cancers13102338 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2338

Author(s):

Ssu-Han Chen ◽

Hong-Han Lin ◽

Yao-Feng Li ◽

Wen-Chiuan Tsai ◽

Dueng-Yuan Hueng

Keyword(s):

Clinical Significance ◽

Expression Analysis ◽

Expression Profiles ◽

Malignant Gliomas ◽

The Cancer Genome Atlas ◽

Low Grade ◽

Lower Grade ◽

Interacting Protein ◽

Thyroid Receptor ◽

Genome Atlas

The prognosis of malignant gliomas such as glioblastoma multiforme (GBM) has remained poor due to limited therapeutic strategies. Thus, it is pivotal to determine prognostic factors for gliomas. Thyroid Receptor Interacting Protein 13 (TRIP13) was found to be overexpressed in several solid tumors, but its role and clinical significance in gliomas is still unclear. Here, we conducted a comprehensive expression analysis of TRIP13 to determine the prognostic values. Gene expression profiles of the Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and GSE16011 dataset showed increased TRIP13 expression in advanced stage and worse prognosis in IDH-wild type lower-grade glioma. We performed RT-PCR and Western blot to validate TRIP13 mRNA expression and protein levels in GBM cell lines. TRIP13 co-expressed genes via database screening were regulated by essential cancer-related upstream regulators (such as TP53 and FOXM1). Then, TCGA analysis revealed that more TRIP13 promoter hypomethylation was observed in GBM than in low-grade glioma. We also inferred that the upregulated TRIP13 levels in gliomas could be regulated by dysfunction of miR-29 in gliomas patient cohorts. Moreover, TRIP13-expressing tumors not only had higher aneuploidy but also tended to reduce the ratio of CD8+/Treg, which led to a worse survival outcome. Overall, these findings demonstrate that TRIP13 has with multiple functions in gliomas, and they may be crucial for therapeutic potential.

Download Full-text