Development and Validation of an IDH1-Associated Immune Prognostic Signature for Diffuse 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.

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A four-protein expression prognostic signature predicts clinical outcome of lower-grade glioma

Gene ◽

10.1016/j.gene.2018.08.001 ◽

2018 ◽

Vol 679 ◽

pp. 57-64 ◽

Cited By ~ 4

Author(s):

Vikas Patil ◽

Kulandaivelu Mahalingam

Keyword(s):

Clinical Outcome ◽

Protein Expression ◽

Lower Grade ◽

Prognostic Signature ◽

Lower Grade Glioma

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An Integrative Multi-omics Analysis based on Liquid-liquid Phase Separation Delineates Distinct Subtypes of Lower-grade Glioma and Identifies a Prognostic Signature

10.21203/rs.3.rs-870729/v1 ◽

2021 ◽

Author(s):

Jianglin Zheng ◽

Zhipeng Wu ◽

Yue Qiu ◽

Xuan Wang ◽

Xiaobing Jiang

Keyword(s):

Phase Separation ◽

Liquid Phase ◽

Clinicopathological Features ◽

Lower Grade ◽

Prognostic Signature ◽

Cancer Hallmarks ◽

Lower Grade Glioma ◽

Time Patterns ◽

Integrative Assessment ◽

Liquid Liquid Phase Separation

Abstract Background: Emerging evidences have indicated that the aberrant liquid-liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG).Methods: We integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment. Results: Based on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4. We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent.Conclusions: This study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.

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Integrative Analysis of a Novel Eleven-Small Nucleolar RNA Prognostic Signature in Patients With Lower Grade Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.650828 ◽

2021 ◽

Vol 11 ◽

Author(s):

Teng Deng ◽

Yizhen Gong ◽

Xiwen Liao ◽

Xiangkun Wang ◽

Xin Zhou ◽

...

Keyword(s):

Enrichment Analysis ◽

Comprehensive Analysis ◽

Integrative Analysis ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Rna Seq ◽

Prognostic Signature ◽

Immune Microenvironment ◽

Immune Infiltration ◽

Lower Grade Glioma

ObjectiveThe present study used the RNA sequencing (RNA-seq) dataset to identify prognostic snoRNAs and construct a prognostic signature of The Cancer Genome Atla (TCGA) lower grade glioma (LGG) cohort, and comprehensive analysis of this signature.MethodsRNA-seq dataset of 488 patients from TCGA LGG cohort were included in this study. Comprehensive analysis including function enrichment, gene set enrichment analysis (GSEA), immune infiltration, cancer immune microenvironment, and connectivity map (CMap) were used to evaluate the snoRNAs prognostic signature.ResultsWe identified 21 LGG prognostic snoRNAs and constructed a novel eleven-snoRNA prognostic signature for LGG patients. Survival analysis suggests that this signature is an independent prognostic risk factor for LGG, and the prognosis of LGG patients with a high-risk phenotype is poor (adjusted P = 0.003, adjusted hazard ratio = 2.076, 95% confidence interval = 1.290–3.340). GSEA and functional enrichment analysis suggest that this signature may be involved in the following biological processes and signaling pathways: such as cell cycle, Wnt, mitogen-activated protein kinase, janus kinase/signal transducer and activator of tran-ions, T cell receptor, nuclear factor-kappa B signaling pathway. CMap analysis screened out ten targeted therapy drugs for this signature: 15-delta prostaglandin J2, MG-262, vorinostat, 5155877, puromycin, anisomycin, withaferin A, ciclopirox, chloropyrazine and megestrol. We also found that high- and low-risk score phenotypes of LGG patients have significant differences in immune infiltration and cancer immune microenvironment.ConclusionsThe present study identified a novel eleven-snoRNA prognostic signature of LGG and performed a integrative analysis of its molecular mechanisms and relationship with tumor immunity.

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An Integrative Multi-Omics Analysis Based On Liquid-liquid Phase Separation Delineates Distinct Subtypes of Lower-Grade Glioma and Identifies a Prognostic Signature

10.21203/rs.3.rs-870729/v3 ◽

2021 ◽

Author(s):

Jianglin Zheng ◽

Zhipeng Wu ◽

Yue Qiu ◽

Xuan Wang ◽

Xiaobing Jiang

Keyword(s):

Phase Separation ◽

Liquid Phase ◽

Clinicopathological Features ◽

Lower Grade ◽

Prognostic Signature ◽

Cancer Hallmarks ◽

Lower Grade Glioma ◽

Time Patterns ◽

Integrative Assessment ◽

Liquid Liquid Phase Separation

Abstract Background: Emerging evidences have indicated that the aberrant liquid-liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG).Methods: We integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment. Results: Based on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4. We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent.Conclusions: This study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.

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Prediction of Survival Outcome in Lower-Grade Glioma Using a Prognostic Signature with 33 Immune-Related Gene Pairs

International Journal of General Medicine ◽

10.2147/ijgm.s338135 ◽

2021 ◽

Vol Volume 14 ◽

pp. 8149-8160

Author(s):

Shaohua Chen ◽

Yongchu Sun ◽

Xiaodong Zhu ◽

Zengnan Mo

Keyword(s):

Survival Outcome ◽

Lower Grade ◽

Related Gene ◽

Prognostic Signature ◽

Gene Pairs ◽

Immune Related Gene ◽

Lower Grade Glioma ◽

Prediction Of Survival

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An Integrative Multi-Omics Analysis Based on Liquid-Liquid Phase Separation Delineates Distinct Subtypes of Lower-Grade Glioma and Identifies a Prognostic Signature

10.21203/rs.3.rs-870729/v2 ◽

2021 ◽

Author(s):

Jianglin Zheng ◽

Zhipeng Wu ◽

Yue Qiu ◽

Xuan Wang ◽

Xiaobing Jiang

Keyword(s):

Phase Separation ◽

Liquid Phase ◽

Clinicopathological Features ◽

Lower Grade ◽

Prognostic Signature ◽

Cancer Hallmarks ◽

Lower Grade Glioma ◽

Time Patterns ◽

Integrative Assessment ◽

Liquid Liquid Phase Separation

Abstract Background: Emerging evidences have indicated that the aberrant liquid-liquid phase separation (LLPS) leads to the dysfunction of biomolecular condensates, thereby contributing to the tumorigenesis and progression. Nevertheless, it remains unclear whether or how the LLPS of specific molecules affects the prognosis and tumor immune microenvironment (TIME) of patients with lower-grade glioma (LGG).Methods: We integrated the transcriptome information of 3585 LLPS-related genes to comprehensively evaluate the LLPS patterns of 423 patients with LGG in The Cancer Genome Atlas (TCGA) cohort. Then, we systematically demonstrated the differences among four LLPS subtypes based on multi-omics analyses. In addition, we constructed the LLPS-related prognostic risk score (LPRS) for individualized integrative assessment. Results: Based on the expression profiles of 85 scaffolds, 355 regulators, and 3145 clients in LGG, we identified four LLPS subtypes, namely LS1, LS2, LS3 and LS4. We confirmed that there were significant differences in prognosis, clinicopathological features, cancer hallmarks, genomic alterations, TIME patterns and immunotherapeutic responses among four LLPS subtypes. In addition, a prognostic signature called LPRS was constructed for individualized integrative assessment. LPRS exhibited a robust predictive capacity for prognosis of LGG patients in multiple cohorts. Moreover, LPRS was found to be correlated with clinicopathological features, cancer hallmarks, genomic alterations and TIME patterns of LGG patients. The predictive power of LPRS in response to immune checkpoint inhibitor (ICI) therapy was also prominent.Conclusions: This study provided a novel classification of LGG patients based on LLPS. The constructed LPRS might facilitate individualized prognosis prediction and better immunotherapy options for LGG patients.

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