Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma

Wu, Yin; Tang, Guihua; Zhou, Quanwei; Cao, Yudong; Li, Haixia; Fu, Xianyong; Wu, Zhaoping; Jiang, Xingjun

doi:10.3389/fgene.2019.00419

Cited by 50 publications

(57 citation statements)

References 49 publications

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“…Recently, five gene‐related signatures have been reported to predict the prognosis of patients with GBM. Therefore, we compared the prognostic value of our two‐gene (GRIA2 and RYR3) signature (hereinafter referred to as GR‐Sig) with that of different gene sets for predicting prognosis of patients with GBM: the 4‐gene (HOXC10, OSMR, SCARA3, and SLC39A10) signature derived from Cao's study 24 (hereinafter referred to as HOSS‐Sig); the 4‐gene (LHX2, MEOX2, SNAI2, and ZNF22) signature derived from Cheng's study 25 (hereinafter referred to as LMSZ‐Sig); the 5‐gene (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) signature derived from Yin's study 26 (hereinafter referred to as PRGIT‐Sig); the 5‐gene (DES, RANBP17, CLEC5A, HOXC11, and POSTN) signature derived from Wang's study 27 (hereinafter referred to as DRCHP‐Sig); the 6‐gene (BPIFB2, HOXA13, LRRC10, NELL1, SDR16C5, and XIRP2) signature derived from Zhao's study 28 (hereinafter referred to as BHLNSX‐Sig).…”

Section: Resultsmentioning

confidence: 99%

A robust two‐gene signature for glioblastoma survival prediction

Pan

Zhang

Guo

et al. 2020

J of Cellular Biochemistry

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Glioblastoma multiforme (GBM) is a highly malignant brain tumor. We explored the prognostic gene signature in 443 GBM samples by systematic bioinformatics analysis, using GSE16011 with microarray expression and corresponding clinical data from Gene Expression Omnibus as the training set. Meanwhile, patients from The Chinese Glioma Genome Atlas database (CGGA) were used as the test set and The Cancer Genome Atlas database (TCGA) as the validation set. Through Cox regression analysis, Kaplan-Meier analysis, t-distributed Stochastic Neighbor Embedding algorithm, clustering, and receiver operating characteristic analysis, a two-gene signature (GRIA2 and RYR3) associated with survival was selected in the GSE16011 dataset. The GRIA2-RYR3 signature divided patients into two risk groups with significantly different survival in the GSE16011 dataset (median: 0.72, 95% confidence interval [CI]: 0.64-0.98, vs median: 0.98, 95% CI: 0.65-1.61 years, logrank test P < .001), the CGGA dataset (median: 0.84, 95% CI: 0.70-1.18, vs median: 1.21, 95% CI: 0.95-2.94 years, logrank test P = .0017), and the TCGA dataset (median:1.03, 95% CI: 0.86-1.24, vs median: 1.23, 95% CI: 1.04-1.85 years, logrank test P = .0064), validating the predictive value of the signature. And the survival predictive potency of the signature was independent from clinicopathological prognostic features in multivariable Cox analysis. We found that after transfection of U87 cells with small interfering RNA, GRIA2 and RYR3 influenced the biological behaviors of proliferation, migration, and invasion of glioblastoma cells. In conclusion, the two-gene signature was a robust prognostic model to predict GBM survival. K E Y W O R D Sglioblastoma multiforme, prognostic, protein-coding genes, signature

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Section: Resultsmentioning

confidence: 99%

A robust two‐gene signature for glioblastoma survival prediction

Pan

Zhang

Guo

et al. 2020

J of Cellular Biochemistry

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“…Exploitation of the function of dysregulated lncRNAs will provide potential clinical applications for TN's diagnosis and treatment. WGCNA can be used to identify key lncRNAs associated with multiple cancer pathogenesis and progression [23][24][25][26]. Survival analysis verified the identified lncRNAs possessing potential indicative roles in TN prognosis.…”

Section: Introductionmentioning

confidence: 90%

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Wang

Hou

et al. 2020

BioMed Research International

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Breast cancer is the most common cancer observed in adult females, worldwide. Due to the heterogeneity and varied molecular subtypes of breast cancer, the molecular mechanisms underlying carcinogenesis in different subtypes of breast cancer are distinct. Recently, long noncoding RNAs (lncRNAs) have been shown to be oncogenic or play important roles in cancer suppression and are used as biomarkers for diagnosis and therapy. In this study, we identified 134 lncRNAs and 6,414 coding genes were differentially expressed in triple-negative (TN), human epidermal growth factor receptor 2- (HER2-) positive, luminal A-positive, and luminal B-positive breast cancer. Of these, 37 lncRNAs were found to be dysregulated in all four subtypes of breast cancers. Subtypes of breast cancer special modules and lncRNA-mRNA interaction networks were constructed through weighted gene coexpression network analysis (WGCNA). Survival analysis of another public datasets was used to verify the identified lncRNAs exhibiting potential indicative roles in TN prognosis. Results from heat map analysis of the identified lncRNAs revealed that five blocks were significantly displayed. High expressions of lncRNAs, including LINC00911, CSMD2-AS1, LINC01192, SNHG19, DSCAM-AS1, PCAT4, ACVR28-AS1, and CNTFR-AS1, and low expressions of THAP9-AS1, MALAT1, TUG1, CAHM, FAM2011, NNT-AS1, COX10-AS1, and RPARP-AS1 were associated with low survival possibility in TN breast cancers. This study provides novel lncRNAs as potential biomarkers for the therapeutic and prognostic classification of different breast cancer subtypes.

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“…So many central nervous system tumors were named according to molecular parameters and histopathologic diagnosis, especially gliomas, ependymomas and medulloblastomas in the 2016 revision of the WHO classification (Zhang et al, 2019b). As we know, some molecular markers, such as MGMT (O6-methylguanine DNA methyltransferase) (Binabaj et al, 2018), isocitrate dehydrogenase (IDH) (Kwon et al, 2019), epidermal growth factor receptor (EGFR) (Chistiakov, Chekhonin & Chekhonin, 2017) and phosphatase and tensin homolog (PTEN) (Koshiyama et al, 2017) that have contributed to personalized therapeutic approaches and targeted anti-glioblastoma therapies have been routinely tested in glioblastoma patients clinically (Yin et al, 2019). However, there are few specific clinical indicators and therapeutic targets for LGGs compared to glioblastoma, so there is an urgent need to elucidate the mechanism of glioma development and progression, which can provide potential treatment targets for LGGs.…”

Section: Introductionmentioning

confidence: 99%

Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma

Xiao

Liu

Peng

et al. 2020

PeerJ

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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 lower-grade 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 UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator 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 the 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 the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic 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 the 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 a promising independent biomarker of LGG.

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Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma

Cited by 50 publications

References 49 publications

A robust two‐gene signature for glioblastoma survival prediction

A robust two‐gene signature for glioblastoma survival prediction

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma

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