An eight-mRNA signature outperforms the lncRNA-based signature in predicting prognosis of patients with glioblastoma

Gong, Zhenyu; Fan, Hong; Wang, Hongxiang; Zhang, Xu; Chen, Juxiang

doi:10.1186/s12881-020-0992-7

Cited by 13 publications

(13 citation statements)

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“…Through whole-genome DNA methylation analysis, researchers can distinguish the malignant degree of gliomas and predict the prognosis of patients ( 40 , 41 ). LncRNA, as a new participant, presented a new approach to explore the biological function of glioma cells and predict the prognosis of glioma patients ( 42 ). Moreover, methylated lncRNAs can directly bind to transcription factors or act as ceRNAs and bind to miRNAs, which plays an epigenetic modification role in tumors ( 24 , 43 ).…”

Section: Discussionmentioning

confidence: 99%

“…The genome-wide DNA methylation environment of glioma has shown extensive spatiotemporal heterogeneity. Through whole-genome DNA methylation analysis, researchers can distinguish the malignant degree of gliomas and predict the prognosis of patients (40,41). LncRNA, as a new participant, presented a new approach to explore the biological function of glioma cells and predict the prognosis of glioma patients (42).…”

Section: Discussionmentioning

confidence: 99%

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A Signature of Nine lncRNA Methylated Genes Predicts Survival in Patients With Glioma

et al. 2021

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Glioma is one of the most common malignant tumors of the central nervous system, and its prognosis is extremely poor. Aberrant methylation of lncRNA promoter region is significantly associated with the prognosis of glioma patients. In this study, we investigated the potential impact of methylation of lncRNA promoter region in glioma patients to establish a signature of nine lncRNA methylated genes for determining glioma patient prognosis. Methylation data and clinical follow-up data were obtained from The Cancer Genome Atlas (TCGA). The multistep screening strategy identified nine lncRNA methylated genes that were significantly associated with the overall survival (OS) of glioma patients. Subsequently, we constructed a risk signature that containing nine lncRNA methylated genes. The risk signature successfully divided the glioma patients into high-risk and low-risk groups. Compared with the low-risk group, the high-risk group had a worse prognosis, higher glioma grade, and older age. Furthermore, we identified two lncRNAs termed PCBP1-AS1 and LINC02875 that may be involved in the malignant progression of glioma cells by using the TCGA database. Loss-of-function assays confirmed that knockdown of PCBP1-AS1 and LINC02875 inhibited the proliferation, migration, and invasion of glioma cells. Therefore, the nine lncRNA methylated genes signature may provide a novel predictor and therapeutic target for glioma patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Signature of Nine lncRNA Methylated Genes Predicts Survival in Patients With Glioma

et al. 2021

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“…Growing evidence suggests that lncRNAs can be promising biomarkers for GBM diagnosis and treatment. However, it is unconvincing to use a single lncRNA as a target due to the significant heterogeneity of GBM (26). More recently, the molecular research and gene exploration of the tumor, especially the statistical analysis research based on big data, has made rapid progress (27,28).…”

Section: Discussionmentioning

confidence: 99%

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Niu¹,

Sun

Meng

et al. 2020

Front. Oncol.

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Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

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“…These results were comparable to other risk classification systems established by the lipid metabolism [4], pseudogenes [7], necrosis [9] or module [10] related genes that were differentially expressed between GBM and LGG. Furthermore, previous studies indicated mRNA signature outperformed the lncRNA-based signature [23], while combined with lncRNAs [24,25] added the power in predicting prognosis. Thus, we screened the combined lncRNA-mRNA signature and compared the predictive performance of three classi ers.…”

Section: Discussionmentioning

confidence: 95%

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

Wei

Wang

Zhao

2020

Preprint

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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.

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An eight-mRNA signature outperforms the lncRNA-based signature in predicting prognosis of patients with glioblastoma

Cited by 13 publications

References 50 publications

A Signature of Nine lncRNA Methylated Genes Predicts Survival in Patients With Glioma

A Signature of Nine lncRNA Methylated Genes Predicts Survival in Patients With Glioma

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

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

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