Integrative Analysis of DNA Methylation and Gene Expression Identify a Three-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Zeng, Wenjing; Yi, Yang; Liu, Zheng-Zheng; Wen, Zhipeng; Chen, Yanhong; Hu, Xiaolei; Cheng, Quan; Xiao, Jian; Zhao, Jie; Chen, Xiaoping

doi:10.1159/000489954

Cited by 49 publications

(48 citation statements)

References 43 publications

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“…Lower-grade gliomas (LGG) constitute the prevalent primary malignances of the central nervous system, demonstrating great intrinsic heterogeneity in terms of their biological behavior (Ostrom et al, 2013;Zeng et al, 2018). So far, maximum surgical resection combined with postoperative radiotherapy and chemotherapy is the standard treatment for LGG.…”

Section: Introductionmentioning

confidence: 99%

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

et al. 2020

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Objective: 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. Methods: 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. Results: 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

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Section: Introductionmentioning

confidence: 99%

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

et al. 2020

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“…Recently，Chen X.P et al reported a model containing 3 genes(EMP3、GSX2、EMILIN3) based on integrative analysis of DNA methylation and gene expression in TCGA dataset (Zeng et al 2018).Chuang Zhang et al also reported a 4-gene(EMP3、GNG12、KIF2C、IFI44) prognostic signature based on genes encodes by chr1p/19q (Zhang et al 2019a). To compare the prognostic values of our prognostic signature and their model，we performed time-dependent ROC curve analysis in our model and other models based on the risk score calculated by the regression coefficients which obtained by themselves and the expression level of members in their signature showed in both TCGA and CGGA dataset.…”

Section: A Comparison Between Our and Other Modelsmentioning

confidence: 99%

Peer Review #1 of "Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma (v0.1)"

2020

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Background Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lowergrade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments. Methods The RNA-seq data for LGG was downloaded from the UCSC Xena and Chinese Glioma Genome Atlas (CGGA). By robust likelihood-based survival model, LASSO regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender, and pathologic grade. The prognostic signature was validated in CGGA dataset. Finally, Weighted Gene Co-expression Network Analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, timedependent ROC curves of our model and other models are constructed. Results In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio[HR]=0.198, 95%CI= 0.120-0.325) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore，the model was validated in CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3-and 5-year survival of LGG patients compared to other models and may be promising independent biomarker of LGG.

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“…12,14 Genetic mutations can affect gene expression by means of aberrant transcription, epigenetic regulation, and cell signaling and gene dosage effects. 15 Gene expression profiles have been shown to exhibit the underlying characteristics of cancer, and are typically used to provide prognostic information for LUAD patients [16][17][18] and design new drug targets 19 however, incorporating gene expression-based methods into clinical practice has been met with difficulty, including overfitting, interpatient histologic heterogeneity, intratumoral heterogeneity, and a lack of accounting for existing clinical variables. 20,21 Moreover, the limitations of individual biomarkers mean that they cannot be used as reliable classifiers.…”

Section: Introductionmentioning

confidence: 99%

Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma

et al. 2019

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Background Lung adenocarcinoma (LUAD) is a set of heterogeneous diseases with distinct genetic and transcriptomic characteristics. Since the introduction of the 2011 International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society histologic classification, increasing evidence has provided insights into genomic mutations and rearrangements among individual histologic subtypes of LUAD. However, how genotypic and phenotypic features of LUAD are interconnected is not well understood. Methods We obtained the genomic, transcriptomic, and clinical data sets of 488 LUAD patients from The Cancer Genome Atlas database. Advanced statistical models were used to disentangle the interactions between genetic mutations and expression profiles, and to assess the alterations and changes in expression of each histologic subtype. The prognostic impacts of genetic mutations, expression profiles, and clinicopathological features were integrated to predict the outcomes of LUAD patients. Results From our data, one or more genetic mutations correlate with expression levels of 6054/18175 (33.3%) genes and explain 8–40% of observed variability in LUAD. The genetic mutations and expression profiles varied remarkably among the histologic subtypes of LUAD, which helped to explain the different prognostic impact based on subtype classification. Genomic, transcriptomic, and clinical data were all shown to have utility for predicting overall and recurrence‐free survival, with the largest contribution from the transcriptome. Conclusion Our prediction model integrating genetic mutations, expression profiles, and clinicopathological features exhibited superior accuracy over the current tumor node metastasis staging system to prognosticate outcomes of patients with LUAD (overall survival 67% vs. 55%, recurrence‐free survival 57% vs. 49%; P < 0.01).

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Integrative Analysis of DNA Methylation and Gene Expression Identify a Three-Gene Signature for Predicting Prognosis in Lower-Grade Gliomas

Cited by 49 publications

References 43 publications

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Peer Review #1 of "Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma (v0.1)"

Integrating genetic mutations and expression profiles for survival prediction of lung adenocarcinoma

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