Clinical and Biological Significances of a Methyltransferase-Related Signature in Diffuse Glioma

Zhang, Ying; Liu, Yuqing; Liu, Hanjie; Zhao, Zheng; Wu, Fan; Zeng, Fan

doi:10.3389/fonc.2020.00508

Cited by 3 publications

(3 citation statements)

References 54 publications

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“…Nine medium-quality models show high accuracy [ 60 – 68 ], whereby three models for diffuse glioma estimated accuracy level in training and validation sets despite the large size of predictors [ 64 , 66 , 67 ]. Notably, Sun's model has high accuracy when compared with all the signatures randomly derived from the screen method and outperformed the other three signatures in predicting drug sensitivity [ 66 ].…”

Section: Rna Modelsmentioning

confidence: 99%

Promoting Prognostic Model Application: A Review Based on Gliomas

Liang

Wang

Dai

et al. 2021

Journal of Oncology

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Malignant neoplasms are characterized by poor therapeutic efficacy, high recurrence rate, and extensive metastasis, leading to short survival. Previous methods for grouping prognostic risks are based on anatomic, clinical, and pathological features that exhibit lower distinguishing capability compared with genetic signatures. The update of sequencing techniques and machine learning promotes the genetic panels-based prognostic model development, especially the RNA-panel models. Gliomas harbor the most malignant features and the poorest survival among all tumors. Currently, numerous glioma prognostic models have been reported. We systematically reviewed all 138 machine-learning-based genetic models and proposed novel criteria in assessing their quality. Besides, the biological and clinical significance of some highly overlapped glioma markers in these models were discussed. This study screened out markers with strong prognostic potential and 27 models presenting high quality. Conclusively, we comprehensively reviewed 138 prognostic models combined with glioma genetic panels and presented novel criteria for the development and assessment of clinically important prognostic models. This will guide the genetic models in cancers from laboratory-based research studies to clinical applications and improve glioma patient prognostic management.

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Section: Rna Modelsmentioning

confidence: 99%

Promoting Prognostic Model Application: A Review Based on Gliomas

Liang

Wang

Dai

et al. 2021

Journal of Oncology

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“…Rank-sum test was used to analyze different methylation of GDF10 between UCEC and normal samples. Univariate Cox regression analysis was applied to detect methylation sites with P value < 0.05; Kaplan-Meier (K-M) survival analysis was further applied to evaluate the prognostic value of methylation sites in GDF10 using the survival software package in the R platform [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Comprehensive Analysis of GDF10 Methylation Site-Associated Genes as Prognostic Markers for Endometrial Cancer

Fan

Zhou

2022

Journal of Oncology

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Growth differentiation factor-10 (GDF10) with its methylation trait has recently been found to play a crucial regulatory and communication role in cancers. This investigation aims to identify GDF10 methylation site-associated genes that are closely associated with endometrial cancer (EC) patients’ survival based on normal and UCEC samples from the UCSC Xena database. Our study revealed for the first time that EC exhibited significantly higher levels of GDF10 promoter methylation in comparison with normal tissues. Multiple differentiated methylation sites, which have prognostic value due to their apparent survival differences, were found in the GDF10 promoter region. We performed weighted gene coexpression network analysis (WGCNA) on EC tissues and paraneoplastic tissues while using these differentially methylated sites as phenotypes for selecting the most correlated key modules and their internal genes. To obtain a gene set, the key module genes and differentially expressed genes (DEGs) of EC were intersected. The least absolute shrinkage and selection operator (LASSO) regression along with multivariate Cox regression were performed from the gene set and we screened out the key genes B4GALNT3, DNAJC22, and GREB1. Finally, a prognostic model was validated for effectiveness based on these genes. Additionally, Kaplan-Meier analysis and time-dependent receiver operating characteristics (ROC) were applied to assess and verify the model, and they showed good prognosis prediction. Moreover, the differences in risk scores were statistically significant with age, tumor stage, and grade. They may be related to the immune infiltration of tumors as well. In conclusion, based on the methylation-related genes associated with GDF10, we developed a prognosis model for EC patients. It might provide a fresh view for further research and treatment of EC.

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“…With the development of molecular biology and bioinformatics, recent studies indicated there were obvious differences in gene expression profile between GBM and LGG (Wang and Ma 2019;Wang et al 2019aWang et al , 2019bWu et al 2019;Zhang et al 2019Zhang et al , 2020Biterge-Sut 2020). Thus, identification of molecular biomarkers may be underlying approaches to distinguish GBM from LGG and predict the prognosis.…”

Section: Introductionmentioning

confidence: 99%

Identification of an autophagy-related 10-lncRNA-mRNA signature for distinguishing glioblastoma multiforme from lower‑grade glioma and prognosis prediction

Wei¹,

Wang²,

Zhao³

2021

gpb

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Autophagy may provide the source of nutrients for tumor cells. We aim to develop an autophagy-related signature to predict the progression from lower-grade gliomas (LGG) to glioblastoma multiforme (GBM) and prognosis. Totally, 686 differentially expressed genes (DEGs) and 73 long non-coding RNAs (DELs) were identified between GBM and LGG samples from the Chinese Glioma Genome Atlas (CGGA). Of them, 131 DEGs were intersected with autophagy genes from the Human Autophagy Database; while 54 DELs co-expressed with autophagy-related DEGs. Ten autophagy-related genes were associated with overall survival and could distinguish GBM from LGG, with the accuracy of 0.891 using CGGA dataset and 0.790 using The Cancer Genome Atlas (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%; p < 0.001) and worse prognosis than those in low risk group. The prognostic accuracy was 0.840 and 0.744 for CGGA and TCGA dataset, respectively. Age, recurrence, isocitrate dehydrogenase mutation and risk score were independent prognostic factors and thus they were used to build a nomogram which showed the highest prognostic power. This established nomogram may aid the clinical decision making of personalized treatment.

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Clinical and Biological Significances of a Methyltransferase-Related Signature in Diffuse Glioma

Cited by 3 publications

References 54 publications

Promoting Prognostic Model Application: A Review Based on Gliomas

Promoting Prognostic Model Application: A Review Based on Gliomas

Comprehensive Analysis of GDF10 Methylation Site-Associated Genes as Prognostic Markers for Endometrial Cancer

Identification of an autophagy-related 10-lncRNA-mRNA signature for distinguishing glioblastoma multiforme from lower‑grade glioma and prognosis prediction

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